LEARNING WITH SIMULATION GAMES · 2012-03-05 · A – Interview with Robert Austin! ... Crookall...
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LEARNING WITH SIMULATION GAMES Evaluating Hotel Simulation Games’ Eectiveness on Higher Academic Performance within Service and Hospitality Michael Sørensen Advisor: Karsten Bobek, Department of Marketing MSocSc in Service Management 80 pages, total of 181.217 STUs
Transcript of LEARNING WITH SIMULATION GAMES · 2012-03-05 · A – Interview with Robert Austin! ... Crookall...
LEARNING WITH SIMULATION GAMES
Evaluating Hotel
Simulation Games’
E!ectiveness on
Higher Academic
Performance
within Service
and Hospitality
Michael Sørensen
Advisor: Karsten Bobek,
Department of Marketing
MSocSc in Service Management
80 pages, total of 181.217 STUs
Acknowledgements
I would like to express my gratitude to everybody who has supported me throughout this
learning process.
A special thanks to Karsten Bobek, Steffen Löfvall, Humanostic, Charlotte Sejersen Orland,
the pre-testing group fall 2010, Robert Austin and all the students in the Master’s Degree
class of Service Management 2010, who have contributed with their valuable inputs,
experiences and insights in simulation gaming and higher academic education.
Michael Sørensen
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Executive Summary The purpose of this thesis is to evaluate hotel simulation games’ effectiveness on
higher academic performance within service and hospitality. Effectiveness is assessed through
the examination of the relationship between playing hotel simulation games and derived
potential learning outcomes. Factors and processes that are expected to influence learning
within hotel simulation game playing, as well as different potential learning outcomes, were
incorporated into a designed logic model for hotel simulation games.
To validate the conceptualized relationships and designed model, several different
methods of gathering qualitative and quantitative data were employed. They included, among
others, a comprehensive questionnaire undertaken with students from the 2011 course of
Leadership & Strategy, and an exploratory interview with a Harvard Professor expert in
simulation games.
The responses and results reveal interesting insights into learning through hotel
simulation game playing. Overall, students’, teachers’ and game characteristics’ seem to
impact learning through game simulation. The processes underlying learning also seem to
positively impact the learning outcomes. Further, both cognitive and affective learning
outcomes were induced and facilitated through game playing.
This evidences that hotel simulation games are a highly effective learning tool for
higher academic educational purposes. Simulation games are, however, an effective learning
tool to boost mainly practical knowledge and, in this regard, should be considered as a
supplement to existing theoretical teaching methods. Having that into perspective, it can be
advocated that hotel simulation games should be integrated into current academic
curriculums, thus working towards shifting the current teaching paradigm to a more
interactive and practical approach to learning looking forward.
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Table of Contents
1. Introduction!....................................................................................................................................!6!
1.1. Introduction to the Topic!...........................................................................................................................!6!
1.2. Motivation for the Topic!...........................................................................................................................!8!
1.3. Choice of Simulation Game: HotelSim!................................................................................................!9!
2. Problem Formulation & Key Research Question!...............................................................!12!
2.1. Purpose!..........................................................................................................................................................!12!
2.2. Problem Formulation & Key Research Questions!.........................................................................!12!
2.3. Definitions!....................................................................................................................................................!13!
2.3.1. Hotel Simulation Games!.....................................................................................................................!14!
2.3.2. Effectiveness!............................................................................................................................................!15!
2.4. Limitations!...................................................................................................................................................!16!
3. Methodology!................................................................................................................................!19!
3.1. Structure and Approach!...........................................................................................................................!19!
3.2. Theoretical Validation!..............................................................................................................................!20!
3.3. Theoretical Approach!...............................................................................................................................!22!
3.3.1. Logic Model!.............................................................................................................................................!22!
3.3.2. Experiential Learning Theory!..........................................................................................................!25!
3.3.3. Constructivist Learning Theory!.......................................................................................................!26!
3.3.4. QAIT model!..............................................................................................................................................!28!
3.3.5. Bloom’s Taxonomy of Learning!.......................................................................................................!30!
3.4. Empirical Approach!..................................................................................................................................!33!
3.4.1. Data Collection!......................................................................................................................................!33!
3.4.2. Quantitative Research!.........................................................................................................................!34!
3.4.3. Qualitative Research!............................................................................................................................!41!
4. Theoretical Discussion!...............................................................................................................!44!
4.1. Designing the Logic Model for Hotel Simulation Games!...........................................................!44!
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4.1.1. Input Domain!..........................................................................................................................................!46!
4.1.2. Process Domain!.....................................................................................................................................!49!
4.1.3. Debriefing!.................................................................................................................................................!53!
4.1.4. Outcome Domain!...................................................................................................................................!54!
4.2. Relating the Logic Model for Hotel Simulation Games To Questionnaire!............................!57!
5. Results & Analysis!.....................................................................................................................!58!
5.1. Item Consistency Analysis!.....................................................................................................................!58!
5.1.1. Reliability!.................................................................................................................................................!58!
5.1.2. Validity!......................................................................................................................................................!61!
5.2. Research Results & Analysis!.................................................................................................................!62!
5.2.1 Input Domain!...........................................................................................................................................!62!
5.2.2 Process Domain!......................................................................................................................................!69!
5.2.3. Debriefing!.................................................................................................................................................!75!
5.2.4. Output Domain!.......................................................................................................................................!76!
5.3. Interview with Robert Austin: Insights from a Harvard Professor!...........................................!89!
6. Conclusion!....................................................................................................................................!91!
7. Implications!.................................................................................................................................!95!
8. List of References!.......................................................................................................................!97!
9. List of Figures and Tables!.....................................................................................................!103!
9.1. List of Tables: Full Results!.................................................................................................................!107!
10. Appendix!.................................................................................................................................!110!
A – Interview with Robert Austin!.............................................................................................................!110!
B – Interview with Karsten Bobek!............................................................................................................!118!
C – Quantitative Questionnaire!..................................................................................................................!125!
D – Meta Data Set (Excel version)!............................................................................................................!134!
E – Course Evaluation 2010!........................................................................................................................!144!
F – Course Evaluation 2011!........................................................................................................................!153!
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Learning is experience.
Everything else is just information.
- Albert Einstein.
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1. Introduction In this first chapter, the context of the research will be introduced to justify the selected topic,
and a brief description of the chosen case study will be provided.
1.1. Introduction to the Topic Most people consider education the vital ingredient in how an individual’s career
evolves in today’s competitive environment. The learning experiences gained throughout
primary and secondary school, perhaps supplemented with a bachelor’s and master’s degree
from a university or a business school, bring about valuable knowledge and experiences,
which the person can draw on in future positions within his/her professional career.
How education today is envisioned and structured depends on several factors.
Education varies across countries and continents, is dependent on which government that
governs in a specific country and its policies, which states, region or neighborhood the school
is located at, the school itself, the type and level of education, the teacher, classmates, and the
student. It is not too much to say that there isn’t a unified way of teaching. However, it is still
true that education generally remains reliant on a majority of teaching methods based on
knowledge transfer from teacher to student, through books, exercises, presentations (often
seen through power-point presentations), and lectures. This is known as the teacher-centered
approach to education.
In some educations, such as MBA and executive programs, a shift towards a more
student-centered approach has been taking place. The student’s role has shifted and now the
student is leading the way, taking up the majority of the discussions and arguments through a
case-based method - introduced by Harvard Business School in 1925 (Harvard Business
School, 2011) - while the teacher has become the moderator in the discussion. Despite the
success of the case-based method, the adoption of this style of education has been quite slow
since its introduction, especially within the education levels of primary, secondary and high
school.
In parallel to the case-based method, another student-centered approach is emerging,
namely simulation games. Simulation games are an educational tool where students learn
through the application of theory and decision-making to a simulated real-world business
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scenario. As the case-base method, simulation games are also an active learning method, but
with an incredible improvement potential. Since simulation games are supported by current
technologies that evolve on a fast-pace, they can be enhanced and perfected as technology
changes and emerges, adapting as well to students’ habits in regards to technology along the
way. While the case-based method has shown to be effective, it is still unclear how effective
simulation games are.
A reason for this may be that simulation games, at first glance, hold the misguided
notion that they are just fun games and not beneficial for higher academic purposes.
Understanding the benefits of simulation games within a learning context and evaluating its
effectiveness as teaching tool can unleash an unexplored learning opportunity greatly valuable
to students, especially when entering their professional life.
Despite the lack of an effectiveness measure, simulation games are increasingly being
deployed for educational purposes. More and more schools are starting to use simulation
games to enhance and supplement their teaching methods. Harvard Business School, for
example, use simulation games to replicate real-world contexts in order to reinforce students’
learning; it challenges students to analyze available information and make critical decisions
based on theoretical and practical knowledge to solve business challenges (Harvard Business
School, 2011). Saïd Business School, part of University of Oxford, has also adopted the use
of simulation games. In many of their MBA modules, the school has allocated 10 % of the
total teaching time to the use of simulation games as an integrated tool within these degrees
(Saïd Business School, Email communication, May 16, 2011).
The rise in the adoption of simulation games as learning tools is further supported by
the easiness and widespread use of today’s technologies. Available technologies facilitate
simulated environments and add value to disciplines more than ever, by creating a platform
where new forms of interaction between teachers and students can take place; thus
representing a platform that is focusing on experiential learning with the individual student in
focus. Technology is an opportunity to change the paradigm of how teaching can unfold in
2011 and beyond.
The Ministry of Science, Technology and Innovation (2007) has confirmed that there
is an un-explored and un-employed potential for e-learning via an innovative use of
information and communication technology. However, this new way of teaching has not yet
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been fully assessed and evaluated to capture if it truly holds the potential to be effectively
applied for higher academic education. Uncovering this potential is paramount for giving
legitimacy to this method, and that is exactly what this thesis attempts to explore.
1.2. Motivation for the Topic In today’s competitive environment higher education is not always enough to secure a
job after graduation. Experience is often expected to be part of a graduate’s curriculum even
before he or she graduates. But obtaining this experience is usually not an easy task, either
because it isn’t always possible to follow a full-time study while having a job on the side, or
because it is simply not possible to get a job without having any previous work experience to
start with. Crookall (2010a) illustrates this paradox with a parody that you wouldn’t fly with a
pilot that has not been thoroughly trained in a simulator before he or she is being allowed to
fly airplanes.
Having job experience is the most important qualification for securing a position as a
newly graduate, according to Michael Kornager from Jobcenter København (2010). But
acquiring job experience, for instance, in the format of internships, is generally not a
requirement as part of higher education. Theory in the form of articles, journals, books, etc. is
instead being used as the primary form of teaching and training for the professional life.
Employers would probably like to see more practical application of the theory learnt.
Aram and Noble (1999) argue that “business schools are not adequately preparing
students to understand and cope with the levels of ambiguity and uncertainty they will
inevitably face when they take up positions in organizations. They [the authors] believe that
this is because the models of teaching and learning that dominate academic practice are
those that are appropriate to the stable, predictable aspects of organizational life and do not
include the paradoxical and unpredictable characteristics of the professional business
environment” (Aram, E. and Noble, D., 1999, as cited in Lainema, 2000, p. 1 of 14).
A recent report from Boston Consulting Group (August, 2011) has found that
educators have the opportunity and potential to leverage on technologies such as simulation
games to significantly improve student’s academic performance and educational outcomes but
that they (business schools, universities, primary and secondary schools, etc.) haven’t yet
adopted such technologies in a meaningful way.
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In order to bridge this gap, Copenhagen Business School (CBS) has agreed to bring
simulation games into the daily teaching methods on bachelor’s and master’s degrees, in an
attempt to replicate the business environment and decision-making process that students will
face when entering their professional lives (Löfvall, S., Email communication, June 23,
2011). Before committing to this initiative, however, CBS preliminary assessed its suitability
through a successful pre-testing of HotelSim in fall 2010, which the author of this thesis was
part of. This experience triggered my interest and curiosity in regards to simulation games and
their effectiveness as learning tools.
Combining my desire to study in depth simulation games, with the opportunity to
research on a sample group from a higher educational setting, provided me with the perfect
opportunity for carrying out a master thesis focusing on the effectiveness of simulation
games. Where previous papers have primarily researched and dealt with study groups from
primary school, college or undergraduate classes (Egenfeldt-Nielsen, S., 2010), little attention
has been placed at the graduate level as a sample group. This is the perfect opportunity to
assess the effectiveness of simulation games with the ones going directly to the labor market,
where they can apply the learning from the game directly into the professional environment.
1.3. Choice of Simulation Game: HotelSim HotelSim is a business simulation game, powered by RedGlobal, which allows
participants to operate individually or in groups to manage an independent hotel in
competition with the other individuals or groups. This game requires participants to make
numerous strategic decisions on both short and long-term timeframes, with impact on the
overall performance of the hotel. From a management perspective, participants operate in a
scenario as close to real life as possible, managing a full-service hotel in a city center that
dynamically competes with other local hotels - i.e. the other hotels part of the game.
HotelSim has been tested by CBS in fall 2010, with positive feedback and evaluation.
Several participants took part in the game and later assessment, comprising different
stakeholders within the hospitality industry. Participants covered professors, Ph.D. and
graduate students from CBS.
The feedback was collected at the end of the game and some of them are reported below as
examples of overall assessment.
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“It was encompassing all the important aspects that I feel are important.” (Christian
Hvass, Assistant Professor, Ph.D.)
“Amazing experience.” (Lars Andersen, Graduate student)
“I thought the simulation program was complex but not overly cumbersome to use.”
(Christian Hvass, Assistant Professor, Ph.D.)
“I certainly believe that it can bring some great learning opportunities for others.”
(Michael Sørensen (the author), Graduate student)
“It is my strong believe and feeling that the game can help providing a strongly
improved strategic picture and level of insight into the effect and influence of different
market forces, and general aspect and strategies related to these, for students.” (Lars
Andersen, Graduate student)
“I got a good feel about the games' basic structure, capabilities, overall focus and
complexity, information gathering options, required level of analysis, decision making
processes, potential learning outcomes, and relevance for courses in Marketing”
(Torsten Ringberg, Professor and Ph.D.- & Line Coordinator)
“All the factors included in the game were really appealing.” (Lars Andersen,
Graduate student)
“If I were teaching hotel management this would be a very useful tool for students.”
(Christian Hvass, Assistant Professor, Ph.D.)
“In conclusion, it has been an exciting learning experience and something I think
should find application within more of the teaching going on at CBS”. (Thomas
Frandsen, Ph.D. Fellow)
Based on the overall comments derived from the HotelSim game simulation (Bobek,
K., Personal Communication, March 9, 2011), CBS and the Research Center for Tourism and
Culture Management, part of the Department of Marketing, decided that HotelSim holds
potential as an educational tool for the Leadership and Strategy course, part of the Service
Management Master’s degree program. Yet, CBS would like to further analyze the overall
potential of this game, and this thesis aims to contribute to this goal.
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While CBS has recently integrated HotelSim into its Service Management Master
Degree program and the elective “Learning by doing: How to Develop and Implement
successful Strategies in the Hospitality Industry”, several companies within the Hospitality
industry have already been deploying this game successfully as training tools. Relevant
players taking advantage of this interactive learning method includes: Marriott International
(covering brands such as Marriott Hotels & Resorts, Renaissance, Courtyard, Ritz-Carlton,
etc.), Best Western International, Intercontinental Hotel Group, Hyatt Hotels, and many other
larger and medium hotels.
Other higher educational institutions have also incorporated this teaching method into
their curriculum, summing up to approximately 30 universities and business schools,
including Cornell University (USA), Nanyang Technical University (Singapore) and École
Hôtelière de Lausanne (Switzerland). Along with hotels and educational institutions,
management companies, government agencies, destination-, and travel providers, etc. are also
adopting HotelSim (Red Global Group, 2011).
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2. Problem Formulation & Key Research Question This chapter focuses on the purpose of this thesis, leading to the key research question and
relevant sub-questions. Main terms of the research question are then defined, concluding with
limitations of scope.
2.1. Purpose This thesis intends to evidence that hotel simulation games are a valid and worthwhile
tool for higher educational purposes, within service and hospitality. Assessing validity and
worthwhileness involves investigating the effectiveness of hotel simulation games as learning
tools.
By using HotelSim as a case study, the author aims to uncover the main factors and
processes that influence learning within hotel game simulation, as well as the nature and
degree of potential learning outcomes. Only by facilitating learning can hotel simulation
games be considered effective, and thus legitimated academic tools to be integrated as part of
today’s learning experiences.
2.2. Problem Formulation & Key Research Questions Investigating the overall effectiveness of hotel simulation games as learning tools is
paramount to support their more widespread applicability and to recognize their value within
the educational environment.
Rather unexplored, hotel simulation games and their effectiveness as learning tools
will be analyzed and assessed, by studying how learning theories and other relevant theories
respond to simulation gaming and the potential learning outcomes derived from simulation
game participation.
Linking hotel simulation games to learning to effectiveness is the key in this thesis.
Therefore, the research question can be summarized as follows.
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What!are!the!main!learning!
outcomes!derived!from!
participating!in!hotel!game!
simulations?!
To!what!extent!are!hotel!
simulation!games!effective!in!
facilitating!learning?!
Answering the above research question will guide the overall structure of the thesis.
To aid in addressing this question, sub-questions have been included to act as working
guidelines. In this regard, hotel simulation games’ effectiveness will be analyzed with respect
to the following:
2.3. Definitions With the increasing proliferation of different teaching methods and the advance of
technology, the boundaries between playing and learning have become subtler than ever.
Misunderstandings may arise regarding the meaning of the term hotel simulation games, as
may also be the case with the term effectiveness and what it encompasses. Defining the
terminology employed throughout this thesis is thus imperative.
ARE! HOTEL! SIMULATION! GAMES! AN! EFFECTIVE! LEARNING! TOOL! FOR!
HIGHER!ACADEMIC!PURPOSES?!!
Which!factors!and!processes!play!a!
role!in!facilitating!learning!within!
the!context!of!hotel!simulation!
games?!
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2.3.1. Hotel Simulation Games
Simulation games or game simulation can at first glance give rise to the association of
regular computer games one plays for fun in ones spare time. Even though few computer
games are indeed simulation games, the overall definition of simulation games is somewhat
distinct and take into account the learning aspect embedded in these games.
Over the past decades, simulation games have been covered by a broad literature in
the fields of training and learning. Reviewing forty years of compiled research by the leading
journal of Simulation & Gaming (Bragge, J., Thavikulwat, P., & Toyli, J., 2010) evidenced
the lack of a specific and consistent definition of the term. The five most frequent phrases
referring to the field of game simulation were: Simulation, Game, Gaming, Simulation
Games, and Learning, and the five most used descriptors were: Decision Making,
Management Games, Programming Management, Management Science, and Business
Education. However, the similarities identified in the definitions were as widespread as the
different definitions themselves, from all the analyzed authors and journals within the field of
game simulation.
Taking into account the main similarities identified, a common denominator was the
fact that simulation games refer to business games that can simulate real-world scenarios or
business cases, reflecting the notion of experiential learning, or learning by doing, facilitated
by the use of interactive technologies.
Simulating real-world business scenarios involve applying theory to practice, and
imply a great deal of strategy development, decision-making and overall business
management. A specific decision taken in the simulation game will influence another factor,
as well as the same factor over time, and all decisions will have an overall combined effect on
the final simulated business performance.
But unlike decisions taken in the real world, decisions and outcomes taken in game
simulations have solely a training and learning purpose, although they are expected to
replicate the same expected dynamic effects on performance as in real business situations. In
this regard, simulation games can, to a higher extend than regular teaching, prepare students
to business situations similarly found in real jobs.
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Simulation games when it comes to the hotel business are merely the simulated real-
world business scenario of hotel management. The dynamics and mechanisms of the game
remain the same, but the focus is placed specifically in managing a hotel over time within a
competitive environment.
2.3.2. Effectiveness
Effectiveness, when it comes to simulation games, is a complex term to define.
Several factors play a role in defining the learning outcomes of performing simulation games,
and this complexity can jeopardize the implementation of game simulation as a learning tool
in the academic environment. A measure of effectiveness thus supports added value provided
by using simulation games.
Measuring learning outcomes provides an assessment of effectiveness. If learning has
occurred, then simulation games have been effective. Appraising learning is unfortunately not
very straightforward. There isn’t a single theory or model that is considered universal
throughout the learning literature to evaluate learning outcomes. However, there is one theory
that is generally accepted and used for this purpose: Bloom’s Taxonomy of Learning
Objectives (Undervisningsministeriet1, 2011a and 2011b).
Bloom’s taxonomy classifies two domains of learning – Cognitive and Affective –
which reflect, respectively, intellectual and attitudinal learning outcomes. Within each of
these domains, there are different stages of learning, implying that learning can be assimilated
in different degrees.
For simulation games to be effective, learning needs to occur in any of the two
domains of learning. In other words, intellectual or attitudinal performance needs to be
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1!Undervisningsministeriet!is!the!’Danish!Ministry!of!Education’.!
HOTEL!SIMULATION!GAMES!are!an!educational! tool!where!students! learn!
through!the!application!of!theory!to!pratice,!through!a!simulated!realXworld!
hotel!management!scenario!facilitated!by!the!use!of!interactive!technology.!!
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positively changed as a result of participating in game simulation. By fulfilling at least one of
these two learning outcomes, game simulations can prove to be effective learning tools.
However, simulation games have different levels of effectiveness, depending on how
many learning outcomes have been fulfilled by participating in simulation games, as well as
the degree of fulfillment within each learning outcome – i.e. which stage of learning has one
reached within a learning domain.
The more learning outcomes fulfilled and the more stages within one learning
outcome fulfilled, the more effective simulation games are in terms of learning. In this regard,
effectiveness can be assessed not only by its achievement or not, but also by its degree of
achievement. Put differently, simulation games can facilitate learning in a continuum from
minimum (one learning domain and one stage within this domain) to a maximum (two
learning domains and all stages within these domains) extent.
For the purpose of this thesis, the focus will rely on investigating the overall
effectiveness of hotel simulation games as learning tools. In this regard, the fulfillment of at
least one of the two learning outcomes is considered sufficient to support the effectiveness of
hotel simulation games.
A comprehensive explanation of Bloom’s theory combined with an analysis of its role
within the investigation conducted in this thesis and its related implementation within an
overall learning model is provided in Chapter 3.3.5. – Bloom’s Taxonomy of Learning and in
Chapter 4.1. – Designing the Logic Model for Hotel Simulation Games.
2.4. Limitations To the extent that this thesis aims at investigating whether or not hotel simulation
games are an effective learning tool for higher academic performance within service and
hospitality, only the effectiveness of hotel simulation games will be under scrutinization.
EFFECTIVENESS! OF! HOTEL! SIMULATION! GAMES! is! the! successful!
fulZillment! of! at! least! one! of! the! two! learning! outcomes! (cognitive! or!
affective)!derived!from!participating!in!hotel!game!simulation.!
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Even though the mechanisms and rationale are similar, and the results uncovered by the
undertaken research are expected to carry forward to other types of simulation games, the
effectiveness of other types of simulation games cannot be asserted based on this thesis.
The effectiveness of hotel simulation games is assessed through the use of HotelSim
as a case study. This case illustrates the common premises, mechanisms and dynamics in
place when managing a hotel, and therefore it is assumed that the results derived from the
carried out research can be extrapolated to different hotel simulation games. Further,
HotelSim is a worldwide, recognized hotel simulation game used by well-known institutions
around the world, thus it can be inferred that it reflects fairly well the hotel management
business. However, to be able to fully and statistically validate this assumption, a cross-hotel
game analysis would need to be undertaken. This is, though, out of scope of this thesis.
When considering theories and models that can aid in the discussion undertaken in this
thesis, only the most relevant ones are presented, discussed and analyzed in detail. These
models and theories represent the backbone of the theoretical discussion carried out, and work
as evaluation beacons for the research results. Despite the value that other theories and
models can add to the discussion, they portray incremental explanations to the more robust
theoretical argumentation and analyses, and due to the length limitation of this thesis, they are
only presented and discussed to the extent of their relevance to the analysis.
Also, other theories and models could hold potential to be part of the designed Logic
Model (Chapter 4.1 – Designing the Logic Model for Hotel Simulation Games). Despite this
potential, they would likely only add marginal clarification to the learning process that the
designed model aims at grasping, since this model already takes into account well-credited
and relevant theories and models that can aid in this respect, as assessed by the author.
Further, the used theories and models provide a quite holistic and explanatory view of the
learning process likely taking place, thus satisfactorily assessing and evaluating learning
environments for hotel simulation games. Adding more theories and models would only
complicate the designed model2 and not be feasible for the scope of the thesis.
One of the theories considered when designing the logic model for hotel simulation
games is Bloom’s Taxonomy of Learning. This model, as conceptually developed by Bloom, !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
2!A!model!is!a!simplification!of!the!reality,!yet!still!providing!meaningful!insights!into!the!reality.!
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takes into account two domains of learning: cognitive and affective3. Other authors
supplemented Bloom’s Taxonomy by including the psychomotor domain to learning, which
reflects behavioral outcomes. Although relevant, the psychomotor domain is not assessed
through this thesis, as behavioral changes – or skills acquired – require a longitudinal
assessment of behavior and performance, not possible due to the timeframe of the carried out
research.
Furthermore, the designed model is a logic model taking the outset in hotel simulation
games. As a logic model, the designed model reflects a chain of reasoning about causal
relationships in regards to learning stemmed from playing hotel simulation games. These
causal relationships are assessed and explained through simple statistical inference and by
assumptions and interpretations of the results of the undertaken research, instead of statistical
empirical testing. This is due to the fact that learning outcomes are difficult to capture and
measure statistically, and to the fact that logic models and their variables are more suited for
exploratory analysis than hypothesis testing (Hense, J., Kriz, W. C., & Wolfe, J., 2009).
Lastly, the occurrence of the last stage of the affective learning outcomes cannot be
validated through the undertaken research and analysis. This is due to the fact that the
Characterize stage deals with behavioral attitudes in the future, which are out of the
timeframe of this thesis. Despite this fact, two measurement items and results provide a good
indication of the attainment of this stage, which combined with the assumption that this stage
is likely to reached as all the previous affective stages have been fulfilled, it can be assumed
that the simulation game facilitated characterization (see Chapter 5.2.2. – Process Domain).
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3! The! inclusion! of! the! affective! domain! of! learning! in! Bloom’s! taxonomy! was! completed! by! David!Krathwohl,!Bloom’s!partner.!As!it!was!Bloom’s!intention!to!include!this!domain,!what!is!known!as!Bloom’s!Taxonomy!of!Learning!are!these!two!domains!of!learning!objectives/outcomes.!
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3. Methodology This chapter elaborates on the theoretical foundation relevant to the research question and
the empirical approach employed. After a short outline of the thesis, the key theoretical
concepts are defined – Logic Model, Experiential Learning Theory, Constructivism Learning
Theory, QAIT model and Bloom’s Taxonomy of Learning. The chapter concludes with a
thorough description of the carried out quantitative and qualitative researches.
3.1. Structure and Approach With the research questions in focus, this section outlines the overall structure of this
thesis, combined with a brief description of the content of each chapter.
Opening up each chapter, a short prelude highlights the main objectives of the chapter,
setting up the focus and facilitating the navigation throughout the thesis. Following the
prelude, the relevant topics of the chapter are discussed.
The first chapter sets the tone of the thesis, by introducing the challenges derived from
the current educational methods and the different available teaching alternatives, presenting
the concept of simulation games. The motivation for the thesis is touched upon; together with
the choice of the simulation game HotelSim as a case study.
Following this introduction, Chapter 2 addresses the purpose of the thesis and more
specifically, the core questions it intends to answer. Definitions of key terms employed are
stated and the scope of the thesis delimitated. In short, this chapter is the backbone of the
thesis.
Chapter 3 builds from this backbone to derive the overall framework for the analysis
undertaken. This framework is presented and a brief discussion for the employment of theory
and structured reflection is justified. The theoretical and empirical approaches are then
presented, providing a solid basis for the investigation carried out.
Building on the theoretical approach, the theoretical discussion is dealt with in
Chapter 4. Here the proposed research model, the Logic model for Hotel Simulation Games,
is designed with its subsequent variables. All major theories and models used throughout the
thesis are presented in relation to the Logic model. Finally, the Logic model is paired with the
quantitative questionnaire.
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20!
Chapter 5 opens up with an analysis of the statistical appropriateness of the
quantitative questionnaire used in the empirical research. After validating this approach, the
research results are presented and analyzed in respect to the designed Logic Model in chapter
4, uncovering if the expected learning outcomes have occurred as a result of hotel simulation
game playing. The chapter concludes with relevant insights from Harvard professor Robert
Austin in regards to simulation games’ effectiveness.
Chapter 6 concludes on the overall research question and sub-questions. Followed by
Chapter 7 that covers implications.
3.2. Theoretical Validation Before moving to the theoretical approach, it is important to consider the validity of
theory. Gary Thomas (1997) made a provocative argument in the article “What’s the Use of
Theory?” published in Harvard Educational Review against the use of theory in educational
inquiry.
According to the author, the use of theory is rarely accompanied by the discussion of
its meaning; and without an understanding of its meaning and definition, theory becomes
irrelevant to be used.
Further, he argues that the applicability of theories and theoretical models are
considered beforehand, meaning that the already existing literature is the cornerstone of
current analyses. As a result, a conservatism approach to learning is fostered, as academic
work then tends to be organized around structured reflections and established methodologies
only. Where the students are meeting the expectations of the teachers and requirements of the
higher educations to embed theory in their academic work, they are at the same time not
challenging the status quo and the development of further imagination.
This resistance for going beyond the established can be exemplified by the paradigm
of Thomas Kuhn (1970). This paradigm supports that findings are valid and knowledge is
gained only when they are confirming the theory. The problem with this is that if the findings
are not correlating with the predictions of the model or confirming the theory, this is
considered a misinterpretation and incompetence of the student, supporting that knowledge
has not been gained. This places some restrictions to the student’s willingness to discover and
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challenge current theories, and the learnings from using them. Obviously, this is not the
desired outcome.
Lastly, Gary Thomas (1997) criticizes theory and structured reflection in education in
terms of “its encouragement to particular kinds of thinking and to the discouragement of
diversity of thought” (p. 7 of 24). In other words, theory can create limitations as it tends to
structure and thus constrains thought. This line of thought is similar to Foucault’s ideas of
philosophy of science (Stanford, 2008) where he states that he deliberately approaches his
researches without having a range of theories available during the research process, in order
to look for differences instead of structures. Douglas, M. (1975) goes even further; by saying
that theory structures are a means of controlling what is permitted to count as knowledge (as
cited in Thomas, G., 1997).
Although all valid arguments, Gary Thomas (1997) simply assumes an inherent lack
of comprehension of meaning of theories and their implications. This is certainly a great
pothole, in the sense that it is a rough generalization. Scientific theory is undoubtedly an
essential – and officially required by CBS – source and platform for students to explain,
predict and draw learning experiences from, and making sense of it is just a challenge that
needs to be overcome. Theories and theoretical models work as starting points to support
researches, similar to this thesis, and can help to interpret scientific results and develop
theories further.
Using established theories does not necessarily imply that students can’t challenge
these theories and defy the status quo. If students are able to understand the meaning of
theories and models, and the educational system support critical and innovative thinking, then
students are equipped to judge current theoretical frameworks and use them as basis for
developing and/or improving these theories, or even creating contradictory and yet pertinent
new theories and models.
Learning through discovery, as described above, is of course an applicable approach
within some areas of education, and could possibly also be relevant in this thesis, in its
attempt to seek new knowledge and findings, and challenge the established educational
environment. Even though theory inhibit, to certain extent, particular kinds of thinking, it also
provides a start-up framework and guide the analysis, which can be extremely relevant in the
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development of new thinking, by inspiring thinking and uncovering points for improvement
or different analytical angles.
In sum, theory provides a line of thought upon which one can base its initial
discussion and analysis. However, questioning the theory and its applicability, as well as
challenging its assumptions, considering improvements or changes, or getting inspired for
new thinking, is what creates value and sets the ground for future discussions and discoveries,
setting theory evolution in motion.
Theory will thus be applied in this thesis to assess the simulation gaming environment
and provide insights into uncovering the effectiveness of simulation games. The most relevant
theories used throughout this thesis will be described and critically discussed below.
3.3. Theoretical Approach The purpose of this thesis is to assess the effectiveness of simulation games as
learning tools, by investigating the occurrence of learning as a result of participating in game
simulation. To achieve that, a comprehensive analysis of the factors and processes that play a
role in facilitating learning within the context of simulation games, and the resulting learning
outcomes derived from game participation is undertaken. Theories and models are applied to
provide a solid foundation for the discussion. Those that are the key cornerstones for the
analysis are presented below.
Other theories may be applied throughout this thesis whenever relevant, but as they
hold less influence on the overall analysis, they are only presented and incorporated in the text
when their contribution is significant and suitable to the discussion carried out.
3.3.1. Logic Model
The main pillar of the theoretical approach is the Logic Model (Weiss, 1972). This
model is fundamentally an Input-Process-Outcome model, where relevant factors impacting a
specific outcome are accounted for in the light of the process underlying the achievement of
the outcome. In other words, the Logic Model represents the logic flow of events or steps to
bring about changes in desired outcomes; or put more simply, it is a chain of reasoning about
causal relationships.
The Logic Model can take different shapes according to the different environments
and processes that it sets to investigate. Adjusting the model to reflect a specific environment
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or process is paramount if the model is to provide valuable and pertinent insights for the
investigation.
In the case of this thesis, Garris, R., Ahlers, R., & Driskell, J. E., (2002) logic model
will be used as a starting point in the analyses of simulation games’ effectiveness4. Since this
model was developed to assess and evaluate learning environments in regards to instructional
games, this model is expected to hold close similarities with a logic model for simulation
games.
The basic foundation of Garris et al. (2002) logic model is that learning is stimulated
by both intrinsic and extrinsic motivations. Intrinsic motivation is the player’s – or learner’s –
desire to repeat the activity over and over again just because the activity is interesting in itself;
whereas extrinsic motivation is the drive to repeat the activity because achieving an outcome
is important and rewarding.
Garris et al. (2002) logic model captures these two motivational drivers within, what is
called, the Game Cycle (Figure 1). According to the model, instructional games need to be
designed so they incorporate both desired instructional content and relevant features of
games. These two elements combined trigger the Game Cycle, which includes user judgments
or reactions in regards to the game (e.g. enjoyment, excitement, challenge, interest,
confidence), user behavior (persistence to play, time playing) and system feedback (feedback
on performance in game context). To the extent that the pairing of instructional content and
game features is successful, this cycle leads to repeated and motivated game-play, meaning
that loops of judgments-behavior-feedback are fostered – i.e. the more one enjoys to play, the
more engaged and persistent the person becomes, thus promoting more game interaction and
feedback.
Garris et al. (2002) logic model can be viewed as an Input-Process-Output model,
where the Process domain is interactive by nature. This gives the model a dynamic approach
to learning, in the sense that learning is cemented by continuous repetition of game activity.
The combination of these three domains plays a role in constructing learning from experience
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
4!From!this!point!onwards,!all!mentions!in!this!section!of!the!thesis!referring!to!”logic!model”!will!relate!to!Garris!et!al.!(2002)!logic!model,!unless!otherwise!stated.!!
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24!
and therefore is relevant in assessing the effectiveness of instructional games in terms of
learning.
Figure 1: Garris et al. - Logic Model
In essence, Garris et al. (2002) logic model gives an overview of the sources that are
part of the learning process and the interaction that occurs when framing learning as gaming,
with the derived learning outcomes. However, this model provides just a basic and simplified
framework to viewing and understanding the learning process that one undergoes as a result
from participating in instructional games.
Developing the model further, by evaluating which relevant characteristics should be
considered when designing simulation games, as well as the specific dynamic processes that
players experience and related expected learning outcomes resulting from simulation games
participation, is undertaken in Chapter 4.1 (Designing the Logic Model of Hotel Simulation
Games). In this chapter, a specific logic model is designed for simulation games, where the
above three domains and all relevant variables within each domain are considered and
discussed.
According to the article ‘the program logic model as an integrative framework for
multi-method evaluation’ (Cooksy, L., J., Gill, P., Kelly, P., A., 2001) the main disadvantage
is that it takes resources to develop a specific Logic model for the assigned purpose. Another
drawback is that the evaluator may use the Logic model inflexibly by ignoring unintended
effects that the outcomes might show in the end of the process.
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3.3.2. Experiential Learning Theory
Constructing learning from experience is a process that is anchored on the Experiential
Learning Theory proposed by Kolb (1984). He argues, “Learning is the process whereby
knowledge is created through the transformation of experience. Knowledge results from the
combination of grasping and transforming experience” (p. 41).
According to Kolb (1984), learning involves the interplay of two interdependent ways
of gaining knowledge: grasping experience and transforming experience. Within each of
these, there are two dialectical related modes of learning. Grasping experience can take place
through concrete experience (i.e. change of behavior resulting from experience) and/or
abstract conceptualization (i.e. change of how one thinks as a result of cognition); and
transforming experience can occur through reflective observation (i.e. process of discovery
and questioning) and/or active experimentation (i.e. process of acquiring and transforming
new experiences).
The cornerstone of the Experiential Learning Theory model is that learning lies in the
interaction of these four interdependent processes, each of which required for a holistic
integrative learning to occur. This interaction takes shape in that current and concrete
experiences are the basis for observations and reflections, and these guide the
conceptualization of abstract concepts and constructs, from which new implications for action
can be drawn. These implications can be finally tested and work as guide for new experiences
(Kayes, D. C., 2002)
Integrating action, abstraction, reflection and experience, Kolb creates a learning cycle
that reflects the continuous process of responding to personal and environmental demands. By
enriching concrete experiences with reflection, assigning it meaning by thinking, and
transforming it with action, new and enhanced experiences are created – and the continuous
repetition of this cycle deepens learning and facilitates its transfer to other contexts (Kolb,
1984) (Figure 2).
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Figure 2: The Experiential Learning Theory Model (Kolb, 1984)
The Experiential Learning Theory model provides a valuable contribution in
understanding the process of learning and knowledge creation in an experiential learning
environment corresponding to simulation gaming. The learner is “experiencing, reflecting,
thinking and acting to the learning situation and what is being learnt” (Kolb, A., Kolb; D.,
2005, p. 2 of 21), thus constructing learning through experience. However, assessing whether
simulation games can support and facilitate such learning processes, is a topic for
investigation in Chapter 5 – Results & Analysis.
3.3.3. Constructivist Learning Theory
Constructivism originates from Jean Piaget. A Swiss psychologist that scientifically
proved that constructivism such as playing has huge importance in education and learning.
Constructivism assumes that learning is an active process of constructing instead of acquiring
knowledge, and that instruction is a process of supporting construction rather than
communicating knowledge.
Constructivism supports the experiential cycle by proposing that learning is derived
from constructed understanding through prior experience and knowledge, going beyond the
information provided, and this prior knowledge is in itself constructed instead of retrieved
from memory. It acknowledges that individuals have different learning paths, and recognizes
that each learner, no matter if young or old, is unique in terms of his/her own contribution to
how learning takes place. In this regard, constructivism emphasizes group environment in
learning, by taking into account collective learning, where different individuals bring different
experiences and perspectives (e.g. motivation, background, culture, social aspects) to create
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the basis for reflection and learning. Team interaction reflects reality and empowers group
members to take responsibility in managing the learning experience.
In the context of group environment, not only prior knowledge and experience, and
cognitive processing are important to construct learning, but also the social, cultural and
historical settings (e.g. custom, rules, laws, roles) where learning occurs plays a role in the
construction of learning. Contextual and collective learning is thus emphasized through these
social systems represented by playing games.
Gaming is a constructive process, where imitation and accommodation of new
experiences, including failures, aid in constructing new knowledge. This process considers
constructing and reconstructing interpretations in response to situational demands and
opportunities encountered in the simulated environment (Lainema, 2008). In the case of
simulation games, this environment reproduces characteristics of the real business world,
facilitating the understanding of the interaction and dependencies among team members,
competitors and other stakeholders (Lainema, 2003). The intention is for the students to be
able to apply knowledge in the future based on understanding and perception of business
processes they have self-experienced; in other words, the “whole idea is to give the
participants knowledge that can be transferred to environments beyond the learning
situation” later on (Lainema, 2008, p. 16 of 19).
Lainema (2008) argues in her paper “Perspective making: Constructivism as a
meaning-making structure for simulation gaming” that the constructivist learning paradigm is
able to strengthen such learning processes through simulation gaming environments, as
learning is embedded in realistic settings; Realistic because simulation games represent
multiple aspects as well as complexities of reality, bringing about interactive learning
environments that are dynamic and not predetermined. This allows participants to construct
knowledge themselves by making sense of the experiences found in the game and applying
their knowledge – also the knowledge constructed through playing the game. (Tao, Y.,
Cheng, C., & Sun, S., 2009).
In regards to the role of the instructor, the constructivism theory supports that the
teacher or lecturer should take the role of being a facilitator, guiding, advising, supporting and
challenging, instead of lecturing. By facilitating a dynamic environment where students are
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exposed to the complexity of realistic environments, rather than the presenting of answers or
his/hers experiences, the instructor is aiding students in constructing knowledge.
The constructivism theory also sheds light into the process of learning, by analyzing
the learning processes and effects that might occur as a result of serious game playing, a goal
similar to the experiential learning theory. While constructivism and the experiential learning
theory hold close similarities in regards to how learning occurs (i.e. knowledge is created
instead of communicated), these theories diverge in terms of the factors influencing
knowledge creation and the related process one undergoes. Basically, constructivism is a
more holistic and broad theory that takes into account environmental factors such as social,
cultural and historical settings, as well as emphasizes collective learning and the transfer of
learning to environments beyond the learning situation.
While being broad and theoretical can assist in developing a comprehensive
understanding of the realistic interplay of different factors impacting learning, it also presents
drawbacks in terms of considering intangible processes that may be difficult to assess and
therefore may be difficult to function as measurement tools. This trade-off is the challenges
that both the experiential learning theory and the constructivist learning theory face.
3.3.4. QAIT model
The QAIT model (Slavin, 1996) provides valuable insights into elements relevant for
effective instruction. According to Slavin (1996), four basis elements must be in place and be
adequate for instruction to be effective. The power of these elements rests on their joint
adequacy and their interaction produces multiplicatory effects for learning, in the sense that
improvements in multiple elements are expected to have a greater effect on learning than the
combined improvements in individual elements. The four elements of the QAIT model are
Quality of instruction, Appropriate level of instruction, Incentive and Time on task.
Quality of instruction is “the degree to which information and skills are presented so
that students can easily learn them” (Slavin, 1996, p. 5). It is largely a product of the quality
of the curriculum and of the lesson presentation itself. It is important to note however, that
effective instruction is not just about good teaching (Slavin, 1996); otherwise if it were, then
it could be easily duplicated and transferred to any other learning situations. Instead, other
factors such as prior skills, motivation, time spent, etc., also play a role in effective
instruction.
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Slavin (1996) argues that the most important aspect of quality is the degree to which
the lectures make sense to students. If the information provided is orderly organized, relevant
points and material are repeated and emphasized when appropriated, topics are connected, and
there is a clear link between what is taught and what is assessed, then quality in instruction is
achieved.
Appropriate levels of instruction is “the degree to which the teacher makes sure that
students are ready to learn a new lesson – that is, they have the necessary skills and
knowledge to learn it – but have not already learned the lesson. In other words, the level of
instruction is appropriate when a lesson is neither too difficult nor too easy for students”
(Slavin, 2006, p. 277). The challenge is to find this balance of appropriateness for each
individual student, as all have different backgrounds, learning rates and prior knowledge and
skills, which means that they ideally require different levels of instruction. Despite this
requirement, usually education is provided through homogeneous or less individually tailored
instruction.
Incentive is “the degree to which the teacher makes sure that students are motivated
to work on instructional tasks and to learn the material being presented“ (Slavin, 2006, p.
277). This motivation can either be intrinsic – i.e. interest in the material being learned – or
extrinsic – i.e. rewards for learning the material, such as grade, praise, stars, etc. Regardless of
the type of motivation, incentives must be in place to ensure that students are motivated to
pay attention to materials being taught, study and perform the tasks assigned to them.
Lastly, the Time element captures “the degree to which students are given enough
time to learn the material being taught” (Slavin, 2006, p. 277). Time for learning is greatly
dependent on two factors: allocated time and engaged time. Allocated time is the time
scheduled by the teacher for a particular lesson, whereas engaged time is the time used by
students to actually engage in the tasks (i.e. time-on-task).
Taking the four elements together and accounting for their interaction provides great
insights into effective instruction. When students are presented with information and
materials that make sense to them, when they have the pre-requisites to learn this material,
when they have the motivation to engage in learning, and when the time allocated to learn is
appropriated, then learning or instruction effectively occurs. Amending the learning
experience by adjusting these different elements can optimize the instruction process.
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3.3.5. Bloom’s Taxonomy of Learning
Categorizing learning in a systematic and meaningful way can present some
challenges, since there isn’t a universal consensus throughout the learning literature onto how
to assess learning outcomes. However, there is a generally accepted theory within the
literature that aims to fulfill this purpose: Bloom’s Taxonomy of Learning Objectives.
Bloom’s model provides a theoretical and sound foundation as to how to characterize learning
objectives, and works as a benchmark to assess the occurrence of learning derived from
education (Tal, B. Z., Carton, T. C., 2008).
Bloom’s Taxonomy of Learning was originally developed by Benjamin Bloom in
1956, initially covering cognitive learning. His intention was to extend his model to cover
affective learning, but this work turned out to be completed by his partner David Krathwohl.
Later on, other authors proposed the supplementation of Bloom’s work by incorporating a
third domain to learning – psychomotor – which would reflect behavioral learning. Although
relevant, this domain is not assessed through this thesis, as behavioral changes – or skills
acquired – require a long-term assessment of behavior and performance, not possible due to
the timeframe of the research.
Bloom’s taxonomy is therefore considered as comprising of two domains of learning –
Cognitive and Affective – which reflect, respectively, intellectual and attitudinal learning
outcomes. Within each of these domains, different stages of learning are defined, implying
that learning can be assimilated in different degrees. A discussion of each of these domains
and their respective stages are undertaken in the following sections.
3.3.5.1. Cognitive Domain of Learning
Krathwool (2002, p. 1 of 7) formulates this domain of Bloom’s Taxonomy as: “a
framework for classifying statements of what we expect or intend students to learn as a result
of instruction”. The purpose is to have a framework that can act as a measurement tool,
grasping items that measure the same cognitive learning objectives across educations. Besides
being a measurement tool, it also “provides an organizational structure that gives a
commonly understood meaning to objectives classified in one of its categories” (Krathwohl,
2002, p. 7 of 7).
The cognitive domain, as well as the affective domain, is classified in a hierarchy
ranging from lower- to higher levels of learning objectives (or outcomes), implying that
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learning can be assimilated in different degrees. According to the model, cognitive learning
starts at a basic level of Remembering and move forward towards the complex level of
Creating. For more advanced learning outcomes to occur, the previous learning stages need to
be fulfilled. This condition is represented by a hierarchy of effects in Bloom’s Taxonomy of
learning model (Figure 3).
Figure 3: Cognitive Domain of Learning
The above figure illustrates the staircase structure that will be referenced in the
analysis Chapter 5 – Results & Analysis, where a continuum ranging from Remembering to
Creating represents the overall cognitive domain.
Remembering refers to a basic cognitive retrieval process, meaning that students are
able to recognize or recall previous learned knowledge (e.g. terminology, facts, theories, etc.),
being able to bridge what is presented to relevant taught materials. Understanding refers to
the student’s understanding of the material and what is being taught, where the instructional
activities (e.g. lecturing, books, simulation game, cases, etc.) construct meaning for the
individual student.
Applying measures the student’s ability to apply knowledge to a simulated case in new
and concrete ways to a given situation, as well as processing it before applying it. Basically it
demonstrates the student’s ability to put theory into practice. Analyzing encompasses the
ability to examine in detail, for example, constraints or interrelations in a problem, and
determine how those constraints can be solved, through meaningful assumptions or
alternatives. In other words, it involves critical assessments to a specific matter.
Remembering!
Understanding!
Applying!
Analyzing!
Evaluating!
Creating!
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Finally, the last two stages Evaluating and Creating refer to the student’s ability to
review and make judgments based on the previous stages in the cognitive domain, where they
use certain criteria (e.g. guidelines in simulation game, game results, exam requirements, etc.)
to produce usable recommendations. The ability to assess and judge leads to a solution or
decision, or the creation of something original (e.g. business plan, exam report/presentation,
etc.). By requiring the mastering of all previous stages in the cognitive domain of learning,
the Creating stage is the most complex type of cognitive learning (Tal Ben-Zvi, 2010; Tal
Ben-Zvi & Carton, C., T., 2008; Krathwohl, R., D., 2002; Anderson, P. H. & Lawton, L.,
2009).
This cognitive domain of the learning model provides a generic framework to
benchmark cognitive objectives and assess their related learning outcomes, which can be
applied by a range of different entities. For the purpose of this thesis, the cognitive domain
from Bloom’s Taxonomy will act as a measurement tool to assess which domains and their
related stages have been accomplished as a result of participation in simulation games.
3.3.5.2. Affective Domain of Learning
The affective domain of learning covers the students’ attitudes towards learning,
reflecting their emotions, feelings and beliefs in regards to specific learning – both process
and content. Students’ affective reaction to and their perception of learning influence if and
how learning occurs. When they like, enjoy and find stimulating what is being taught,
students are more likely to assimilate and apply this knowledge.
Figure 4: Affective Domain of Learning
Receiving!
Responding!
Valuing!
Organization!
Characterization!
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33!
The affective process domain starts with Receiving, which refers to the student’s
willingness to receive stimuli through e.g. lecture activities, discussions, books, etc.,
reflecting an openness to experience and willingness to hear. Responding encompasses
students’ participation and response to what he/she is receiving. In this case, students are
actively participating in the learning process.
Valuing demonstrates students’ acceptance and commitment to a particular stance or
action (in this case, simulating games) because of the perceived value. Basically, they attach
value to a stance or action and therefore decide to engage and participate. Organization
reflects the students’ accommodation of what is perceived learned with his/hers own value
system; Put it simply, students reconcile their personal views, stands and beliefs with what is
being taught. Lastly, Characterization, involves the acceptance and incorporation of these
values, that is, the values taught become the behavior of the student.
Together, these five stages represent behavioral attitudes towards a discipline –in the
case of this thesis, simulation games – and whether students have a change in attitudinal
characteristics as a result of being exposed to that specific pedagogy (Anderson, P. H. &
Lawton, L., 2009). This domain covers among other aspects the extent to which students are
willing to learn and whether perceived learning has occurred. A discussion of what is
perceived learning and its extent is carried out in Chapter 4.1.4 – Outcome Domain.
3.4. Empirical Approach
3.4.1. Data Collection
This thesis employs several different methods of gathering qualitative and quantitative
data with the aim to cross validate the collected data and overcome constraints in different
research methods. This technique is known as methodological triangulation. Using more than
one approach to the investigation of a research question enhances the confidence and validity
in the resulting findings (Bryman, 2004). Further, it minimizes and “compensate for the
drawbacks associated with relying on a single method” (Birkman, 2009, p. 332). Below a
breakdown of the different research techniques utilized is presented.
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Figure 5: Overview of Data Collection Methods
3.4.2. Quantitative Research
The quantitative research was primarily conducted to provide a solid foundation to
evaluate the learning process and investigate the learning outcomes derived from participation
in the HotelSim game simulation. The intention is to validate the conceptualized logic model
of learning for hotel game simulations and uncover the different learnings resulting from
game simulation play.
Quantitative research enables an objective assessment of the expected causal
relationships and the collection of larger amounts of structured data, which is easier to
quantify, summarize and analyze (Rog and Bickman, 2009). Through the use of the self-
report questionnaire, grade comparison, and a cognitive test, meaningful and consistent data
are analyzed to shed light into the effectiveness of hotel simulation games as learning tools
for higher academic performance.
3.4.2.1. Self-Report Questionnaire
The self-report questionnaire was employed to gather relevant insights from students
playing the HotelSim. It provided a platform to access their experience, perception, evaluation
Quantitative!
SelfXreport!Questionnaire!
Grade!comparison!(2010!vs.!2011)!
Cognitive!test!
Qualitative!
OfZicial!CBS!Evaluations!(2010!
vs.!2011)!
PreXtest!Evaluation!of!HotelSim,!Fall!
2010!
Interview!with!Karsten!Bobek,!the!facilitator!of!the!Simulation!Game.!
Exploratory!Interview!with!Robert!Austin!
Secondary!data!(journals,!papers,!articles,!books,!
etc.)!
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35!
and recommendation regarding the use of hotel simulation games. This is one of the main
quantitative sources upon which hotel simulation games’ effectiveness is evaluated.
Research Design, Sampling Method and Sample Characteristics
A comprehensive quantitative self-report questionnaire was carried out through
‘Defgo.net Survey Tool’ (Defgo.net, 2011). The sample profile consisted of students of the
2011 class of Strategy and Leadership, which is a course that is an integrative part of their
Master’s degree in Service Management at Copenhagen Business School, Denmark. 64
students were enrolled at the 2011 class and were expected to both participate in the HotelSim
simulation game and attend the final exam. All these students received the questionnaire.
Those students who participated at the HotelSim game were allocated in groups of up
to five students, where each group had a minimum of three different nationalities (Bobek, K.,
2011). A total of 15 groups were formed and within each group, a group leader named CEO
was nominated (by the facilitator of the game).
From the 64 questionnaires sent out only 34 returned completed, representing a
response rate of 53,1 %. The difference between the 64 enrolled students and the sample size
of 34 students refers to the students who either participated in the HotelSim but didn’t return
the questionnaire, or didn’t participate in the HotelSim altogether. The sample group thus
consisted of 34 students, being 24 female (71 %) and 10 male (29 %).
Pretest of Questionnaire
“A good question is one that is worded clearly and precisely so that the respondent
has a good understanding of its meaning and what is expected by way of a response” (Clarke,
A., 1999, p. 74). To ensure that the submitted questionnaire contained only ‘good questions’,
the questionnaire was pre-tested by a group of five Economics’ students from Copenhagen
University in Denmark. The aim was to discover if there was any question that was not well
formulated or dubious, or if any doubts could arise when the students were answering the
questionnaire. The choice of including Economics’ students as the testing group was to obtain
a third-party assessment of the questionnaire from students that are both part of the higher
level education and not familiar with business school terms nor with simulation games. In this
sense, these students could judge more objectively the structure, content and quality of the
questionnaire.
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During the pre-testing, it was brought to attention that the answer options should be
aligned, so the same answer options were used throughout the whole questionnaire and not
changed from question to question. Also, it was recommended that the questionnaire should
be structured with a question followed by a number of statements, instead of a topic followed
by a number of questions. A third outcome of the pretesting was the suggestion to re-organize
the questions, so they followed a more logic and clear approach from question to question,
delineating a process usually undertaken when playing games. The major recommendations
were incorporated in the final version of the questionnaire.
This version was then approved with a few adjustments by Steffen Löfvall, the head of
educational games at CBS, and Karsten Bobek, the advisor for this thesis.
Measurement Scales and Issues
In the final self-report questionnaire, the 7-point Likert scale was used, as follows:
‘strongly agree (1), agree (2), somewhat agree (3), neither agree nor disagree (4), somewhat
disagree (5), disagree (6), and strongly disagree (7)’. The rationale behind choosing the
Likert scale as the primary scale for the questionnaire is based on the high acceptability of
evaluation method as the industry standard within higher education institutions, including
CBS (CBS Evaluation, 2011).
Various studies show that the order of the scale (e.g. strongly agree being 1 or 7) has
no substantial influence on the responses of the participants (Weng, Li-Jen & Cheng, Chung-
Ping, 2000). But since the traditional response scale order ranges from positive to negative
with 1 being the highest value and 7 being the lowest value, this response-order was applied
in the questionnaire. However, the numbers from 1 to 7 were not visually apparent in the
questionnaire, just the continuum from ‘strongly agree’ to ‘strongly disagree’.
The data collected through the 7-point Likert scale was treated as interval data in this
thesis, despite the argument by Bickman, L. and Rog, J.D. (1998, p. 365) that measures of
subjective states shouldn’t be used for absolute meaning, instead they should be treated as an
ordinal scale. The rationale for going against this argument is due to the wide range of
statistical tools available for handling the Likert scale under a parametric test, when compared
to the analytical limitations that a non-parametric test holds. The benefits of the
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approximations caused by categorizing ordinal data as interval data is assumed to outweigh
the costs, despite the conceptual fact that Likert scales are in fact ordinal scales.
This choice is supported by various scientific articles, one of them being a recent
article from Norman (2010). In this article, he refutes the arguments that parametric statistics
cannot be used for ordinal scales like Likert scales, by addressing that the issue of robustness
is still to be proven true. In addition, Also, statistical programs such as SPSS, SAS, Excel, etc.
have no way of affirming or denying the true distance between the scales. Therefore, as long
as the numbers are reasonably distributed (which they ar in the 7-points Likert scale) and the
average variable (i.e. ‘neither agree or disagree’) in is the middle of the distribution, then the
distance among the scale points is the same, as there are “no independent observations to
verify or refute the issue” (Norman, 2010, p. 5 of 8).
Data Collection Procedure
The self-report questionnaire was sent out through email to the 64 students of the 2011
class of Strategy and Leadership on May 9th, 2011. On the same day, the author and Steffen
Löfvall presented the aim of the research to the 2011 Leadership & Strategy class. The
purpose of the presentation was to foster a genuine interest in the research, to demonstrate
CBS’s stake and support on the study, and to encourage the students to take the time to
answer the questionnaire. It was stressed that the survey was anonymous and optional to
complete, and that their replies would not affect their grading in any way. A similar
presentation of the research purpose was included in the email invitation sent out together
with the questionnaire, to encourage the students once again to participate in the survey.
Along with the presentation, there was a personal guidance on how to complete the survey as
well as the contact information of the author in case of any questions or doubts. Also, the
contact information of the provider of the survey tool (Defgo.net) was supplied, in case of any
technical difficulties. No gifts or similar were offered for participation, which was solely
voluntary.
Around 15 responses were received within the first days of the collection period. To
secure a higher number of responses, three reminders were sent out: on May 15th, May 31st
and June 6th. The data collection period ran from May 9th to June 22nd. A total of 36 responses
were received, two of those were not completed, bringing the total number of valid responses
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down to 34. The data was gathered in Defgo.net, before it was digitally exported to SPSS and
Excel for data treatment and analysis.
Disadvantages of the research design
The virtual application of the questionnaire offers some opportunities to the researcher
and/or respondents, such as ease of use, accessibility, time flexibility and fast deployment
over greater samples. Conversely, it also presents the disadvantage of being easy to ignore,
forget and turn down the invitation. Further, if the respondent decides to participate, then the
question of whether the data is reliable or not may arise, as there is no interviewer to directly
evaluate the provided answers, or to clarify any doubts the respondent might have.
Note that each and every single item in the questionnaire will not be analyzed in
detail; rather the overall findings will be illustrated through selected items to highlight the
theoretical discussions and validate the model’s expectation throughout the thesis. In this
regard, the results are more exploratory in nature, as evidenced in Chapter 5 – Results &
Analysis.
3.4.2.2. Grade Comparison (2010 vs. 2011)
To obtain an objective assessment of students’ learning and performance, the grades
from the Leadership & Strategy class were included in the research as a primary factor in this
regard. The grades from both the class of 2010 and the class of 2011 were gathered, in order
to provide a comparative assessment between a class that integrated HotelSim and a class that
didn’t. The 2011 class is therefore the sample group and the 2010 class is the control group.
Research Design and Sampling Method
Karsten Bobek, the advisor for this thesis and teacher of the Leadership & Strategy
course for both the 2010 and 2011 classes, provided the grade list from 2010 and 2011 of this
course (data)(Bobek, K., 2011).
All students enrolled in this course considered, but students who didn’t show up to the
final exam or decided to hand-in blank are not considered for the purpose of this research, as
they hold no value as indicators of learning and performance. A total of 54 students for the
2011 class and 46 students for the 2010 class were accounted for in the analysis.
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Use of Control Group
Employing control groups is recommended when examining students’ cognitive and
affective learnings (Anderson, P. H. & Lawton, L., 2009), in order to provide ground for
comparison and enable inferences that guide the validation of the relationship under
evaluation. Here, discrepancies between these two groups (i.e. 2010 and 2011 class) in terms
of grade, perception and assessment, as described in the grades list and official CBS
evaluation report, are used to enable solid inferences about the effects of simulation games in
higher academic performance (Hair, J., F., Black, B., Babin, B., Anderson, R. E., 2010).
Despite this recommendation, most past researches in simulation gaming have actually
(and unfortunately) excluded such control groups from their experimental design. To avoid
such common pitfall, a control group is employed in the empirical approach of this research,
allowing for a more objective and grounded analysis of the effects of hotel simulation games
for the experimental group, as both groups (i.e. classes) have the same prerequisite for
completing the Leadership and Strategy course, and are both enrolled in the Service
Management Master’s degree program at CBS. The only difference between the 2010 and
2011 classes is that the 2011 class has been using HotelSim during their course, while the
2010 class hasn’t.
Measurement Scales and Issues
Grades in CBS and in Denmark are assessed through a 7-point grading scale instituted
by the Danish Ministry of Education. The benefits of using this seven-point scale is that it
represents an objective and consistent measurement scale, as grades for individual
performance are assigned according to the fulfillment of the learning objectives of the course
curriculum as observed in the final exam, which remained the same for both the 2010 and
2011 classes. Therefore, the grading excludes to a higher extent the teacher’s subjective
appraisal of students’ performance, thus enabling a fair comparison of grades cross years.
3.4.2.3. Cognitive Test
The cognitive test was carry out as part of this research in order to indicate student’s
capabilities and performance, which are expected to have effects on the learning outcomes
derived from hotel game playing by students. This cognitive capability is considered an input
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variable in the designed model, which is informing the undertaken research (refer to Chapter
4.1. – Designing the Logic Model for Hotel Simulation Games for the model).
Research Design and Sampling Method
The cognitive test was undertaken through the PLI assessment tool powered by
Humanostic, who is a global company specialized in personality and cognitive tests. The PLI
tool is one of the most comprehensive tools to access cognitive abilities and therefore to
indicate capabilities and potential for performance. Knowing a student’s PLI level points out
to the student’s ability to absorb and acquire new knowledge (Humanostic, Email
communication, March 24, 2011).
The PLI test was offered to students of the 2011 class as an opportunity to test their
cognitive behavior. The test was applied on March 30th in class, with individual duration of 12
minutes. Each student completed 2 tests, so they could get familiar with the design of the test
at the first trial. The highest result obtained from the 2 tests was considered the student’s
cognitive level.
Taking the test was voluntary, which resulted in only 23 students (whom also
participated in HotelSim and in the final exam) completing the questionnaire. Even though
this sample size is smaller than the sample size for the overall research (N=34), the difference
in the average PLI score from having 34 instead of 23 students taking the test is expected to
not be significant.
Measurement Scales and Issues
The PLI test score ranges from 1 (lowest) to 50 (highest). A PLI result of 22 has been
found to be the general world average level according to Humanostic (Humanostic, Email
communication, March 24, 2011).
Disadvantages of the research design
The main issue of this test, or any cognitive qualitative test, is that a person’s
cognitive ability is boiled down to a single number. Even though that is very useful for
research purposes and objective analyses, it is also a major simplification of a complex
dimension. The lack of better objective alternatives for measuring cognitive abilities and the
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use of a recognized test from a well-known global company (thus implying accuracy and
quality) is, however, intended to mitigate this issue to a great extent.
3.4.3. Qualitative Research
The qualitative research was conducted through a range of methods: evaluation of the
official CBS evaluation course survey from 2010 and 2011; review of feedback of the pre-
testing of HotelSim fall 2010; expert interview with Karsten Bobek, experienced instructor in
simulation games; expert interview with Robert Austin, a relevant industry leader and
experienced professor in simulation games; and review of literature and secondary data on the
topic.
The combination of these different qualitative methods provided rich, subjective and
in-depth insights into simulation games and their effectiveness as learning tools advantages of
qualitative, through different opinions and personal viewpoints about the topic (Rog &
Bickman, 2009).
3.4.3.1. Official CBS Evaluations (2010 vs. 2011)
To achieve indirect insights from the students’ experiences regarding the Leadership
and Strategy course, as well as the simulation gaming experience, the official CBS
Evaluations from 2010 and 2011 were collected.
Research Design and Sampling Method
CBS’s official course evaluation was developed and is conducted by CBS Learning
Lab, now part of CBS Library. An email is send-out to all students enrolled in the respective
course at the end of the course (i.e. right after the final exam), and the students can voluntarily
complete the evaluation online within 2 weeks. The evaluations are in Appendix E (2010
class) and F (2011 class). For the 2010 class, 20 students completed the questionnaire;
whereas in 2011, this number was 22.
Due to these mechanics, it can be assumed that students’ answers give an overall
picture of their assessment of the course and therefore are likely less distorted, as the
evaluation is completed after they finished the entire course (including the exam), they are
anonymous, and they cover both the course and teacher as a whole and not only simulation
games (unless the student’s voluntarily decides to evaluate solely on the game). Thereby, it
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reduces the unconscious influence that direct questions on a topic may have on provided
answers.
Measurement Scales and Issues
The short pre-defined questionnaire is structured around 5-point Likert scale questions and
open-ended questions, where the open-ended questions are a greater part of the questionnaire.
3.4.3.2. Interview with Karsten Bobek, the instructor of the Simulation Game
The interview with the external lecturer at CBS and instructor of the course of
Strategy and Leadership, Karsten Bobek, was carried out on August 2nd 2011 at Copenhagen
Business School and lasted approximately 30 minutes. The interview consisted of semi-
structured and open-questions, where the interviewee was encouraged to add additional
comments when relevant.
The objective of the interview was to get his insights and opinions about the use of
simulation games for higher educational purposes based on his background experience,
motivation, expectations, challenges and recommendations. The interview has been fully
transcribed in Appendix B. His opinions and insights are incorporated throughout the thesis
where appropriate, and especially in Chapter 5.2.1. – Input Domain.
3.4.3.3. Exploratory Interview with Robert Austin, a Harvard Professor
Robert Austin is a Visiting Professor at Copenhagen Business School, and an
Associate Professor from Harvard Business School, who is considered to be an experienced
professor and expert in simulation games. The interview with him intended to gather valuable
insights into the integration of simulation games in higher education, and into the relevant
dynamics and mechanisms of games simulation that facilitate and enable learning.
The interview was conducted on April 12th 2011 at Copenhagen Business School and
lasted approximately 30 minutes. It consisted of both semi-structured and open-ended
questions, to allow for in-depth explanations and encourage expansion. It has been fully
transcript and can be found in Appendix A. His opinions and insights are incorporated
throughout the thesis where appropriate.
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3.4.3.4. Secondary data
Secondary data comprised of journals, papers, articles, books, websites, etc. were used
throughout this thesis to provide a source for data, information and knowledge upon which
the theoretical foundation of this thesis was built. In this regard, secondary data informed
current literature deficiencies that this thesis and the research carried out aimed to aid in
overcoming.
Philip Kotler (2010) argues that secondary data provides a good starting point before
data gathering and helps in defining relevant research problems and objectives. In this sense,
secondary data inspired to author to define the research question and was employed by the
author as a point of discussion when assessing and validating the research results, thereby
confirming existing hypothesis and arguments or contributing with new valuable information
to the research field.
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4. Theoretical Discussion In this chapter, a theoretical model for hotel simulation games is designed, based on an
Input-Process-Output approach. The specific variables of the model are discussed, as well as
the theories and models mediating the overall learning process. The chapter concludes with
an overview of the linkage between the designed model and research questionnaire.
4.1. Designing the Logic Model for Hotel Simulation Games Having the starting point on Garris et al. (2002) logic model (Figure 6), a specially
designed logic model is developed to capture the specific processes and environment
underlying hotel simulation games. As discussed previously, adjusting the similar but yet
generic model for learning derived from instructional games to simulation games is
paramount, if the model is to provide valuable and pertinent insights for assessment of
simulation games effectiveness. In the case of this thesis, this adjustment needs to further
reflect the particular learning environment and processes of hotel simulation games.
Figure 6: Garris et al. - Logic Model
In this regard, the designed logic model must account for factors impacting learning
outcomes in the light of the underlying process working towards the achievement of such
outcomes. This model should thus present relevant factors and processes that enables and
facilitates learning from playing hotel simulation games.
When constructing the logic model, it is important that the chosen measurement
variables capture these essential factors that contribute to the desired learning outcomes, and
that the relevant processes underlying hotel game playing are reflected in the model. Relevant
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theories and models are then applied to moderate the ongoing learning process taking place
upon playing, shedding light into if and how learning is derived from hotel game playing. The
interplay of these relevant theories and models provides the basis for understanding the
learning process and gives insights into relevant variables that facilitate this process.
Building upon the basic structure of Garris et al. (2002) logic model, the logic model
for hotel simulation games is structured as an Input-Process-Outcome model, demonstrating
the logic flow of events and steps that bring about learning outcomes. Each of these three
domains is however developed further, to reflect a more comprehensive, holistic and tailored
framework to assess hotel simulation games’ learning outcomes, and therefore, their
effectiveness as learning tools (Figure 7). This developed model is thus the central framework
for this thesis.
Figure 7: Logic model for Hotel Simulation Games (Author’s creation)
The elements of this framework – representing the hotel game simulation learning
flow from input to outcome – are analyzed in detail in the following sections, having as outset
Garris et al. (2002) logic model.
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4.1.1. Input Domain
Garris et al. (2002) model considers two relevant sources for learning, instructional
content and game characteristics, which combined trigger the learning process. According to
Garris et al. (2002), instructional content refers to the content covered by the game in regards
to the instructional objectives that the game aims to achieve; whereas game characteristics are
the specific features of the game that motivates students to play – i.e. fantasy, sensory stimuli,
rules and goals, challenge, mystery and control.
Although insightful and relevant, these two sources are just a couple that are expected
to have the power to impact learning, therefore presenting a simplistic view of the power of
simulation games to engage users and achieve desired instructional goals. The self-designed
logic model for hotel simulation games thus account for other relevant factors that may affect
learning outcomes, which are grouped into 3 main sub-domains: Student Characteristics,
Teacher Characteristics and Game Simulation Characteristics.
4.1.1.1. Student Characteristics
Student characteristics represent the group of factors that are related to the individuals
participating in the hotel game simulation. Different individuals have different sets of
cognitive, motivational, and demographic traits, which likely play a role in informing if and
how they learn.
Age is considered to negatively impact learning, according to developmental
psychology (Hense et al., 2009). The older an individual is, the harder it usually gets for the
person to learn the same knowledge or skills when compared to a younger person. Gender has
also the potential to correlate to learning; in the sense that there may exist gender-specific
predispositions concerning preferred learning environments (Hense et al., 2009). Cognitive
level is also relevant to be accounted for, as students with high cognitive abilities tend to
perform better than students with low cognitive abilities (Humanostic, Email communication,
March 24, 2011). Further, the contribution of cognitive level to assess learning has never been
proved before (Humanostic, Email communication, March 24, 2011).
Bachelor’s degree and professional experience may also impact knowledge creation.
According to Atkinson’s (1974) model of cumulative learning, previous knowledge and
previous experience are important predictors of learning. Further, specific Previous
knowledge in simulation games may also contribute to facilitate learning, in the sense that
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individuals who have already played simulation games have already moved beyond the initial
learning barrier represented by learning and understanding the game and its mechanisms in
themselves.
Finally, Motivation, which is presented here as the initial drive that the student has to
work on instructional tasks and learn the material presented, is also hypothesized to impact
learning. Here, motivation is considered the initial drive because it is assessed as an input
variable from the student’s point of view. Motivation can be fostered through game playing
and extrinsic incentives to encourage students to learn.
Motivation can either be intrinsic or extrinsic to the student. Intrinsic motivation is the
student’s desire to perform the activity because the activity is interesting in itself; whereas
extrinsic motivation is the drive to perform the activity because achieving an outcome is
important and rewarding (Garris et al., 2002). Whether intrinsic or extrinsic, the student’s
own motivation to play the game is expected to strongly impact learning. Ulrich Schiefele
(1996) argues that motivation and expectancies have proven to exert substantial influence on
learning processes and outcomes.
4.1.1.2. Teacher Characteristics
Teacher characteristics comprise the group of factors that are related to the instructors
utilizing hotel game simulations as learning tools. Specific characteristics of these instructors
likely influence the learning processes and outcomes of their students participating in the
simulation game.
The Training experience of the instructor is one of the factors that can impact the
student’s learning experience. Familiarity with a new instructional method can certainly be
crucial for structuring and managing effective learning (Hense et al., 2009). Further, the
student relies on the teacher to provide explanations and guide him/her through the process of
the game, as well as provide cues to the relationship between the theories of the course and
their practical application within the game (Douglas & Miller, 2007). Therefore, having
experience in training in regards to simulation games can facilitate learning.
Teacher’s Motivation is also another relevant factor when investigating simulation
games’ effectiveness. The instructor’s motivation refers to important affective and attitudinal
aspects of teacher behavior, and involves their drive to teach through simulation games
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(Hense et al., 2009).
Preparation concerns the instructor’s careful consideration of a learning strategy that
involves utilization of simulation games, taking into account student’s and game’s
characteristics (Douglas & Miller, 2007). In this effort, the teacher must allocate enough time
to assess the implications of this strategy and the complexity of this new method (Hense et al.,
2009).
Finally, Expectations refer to the instructor’s intended outcomes from utilizing
simulation games as learning tools. These expectations are envisioned to influence learning
through their effect on the instructor’s motivation and preparation of learning strategy.
4.1.1.3. Game Characteristics
Game characteristics refer to features of the game that have the power to affect
learning. Tailoring a simulation game to enable learning involves considering specific
features that tap into the learning objectives of the course while facilitating them through
practical use of theory in a realistic simulated setting.
Assessing the goal of the simulation game is the first step. Content quality reflects this
goal by representing how the learning objectives of the course are incorporated into the game
and integrated to match the functionalities of the game. Considering the simulation model
within the game, relevant content must be integrated to enable the student to learn through the
application of theory and knowledge, which is intended to be reinforced or further acquired
through game playing, to a simulated real-world business scenario (Hense et al., 2009).
Game quality reflects the specific simulation model within the game and particular
functionalities of the game that facilitates and promotes learning. The game must be able to
replicate valid and realistic scenarios while motivating students to participate through specific
features of games, such as dynamism, interaction, rules and a goal, learner’s control, multiple
paths, fantasy and challenge (Walker de Felix, J. & Johnson, T. R., 1993; Garris et al., 2002).
Lastly, IT challenges involve the technical difficulties underlying the game itself.
Technology can present some challenges as a facilitator for learning, in the regard that it can
become a barrier to learning instead of a tool or enabler. Further, technical difficulties or
flaws can cause student dissatisfaction, negative attitudes towards the game and course, and
student anxiety and frustration (Douglas & Miller, 2007).
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4.1.2. Process Domain
Garris et al. (2002) process domain describes a basic motivational mechanism to foster
continuous and motivated game playing, which enables and reinforces learning. According to
the model, the game cycle is triggered through the successful pairing of instructional content
and game characteristics, leading to loops of judgments-behavior-feedback that promotes
learning.
This mechanism incorporates the fundamental driving force for students to repeatedly
play the game, a relevant factor to promote game playing. However, by mainly tackling into a
few basic enablers of this mechanism, the model presents a too oversimplified view of the
learning process actually taking place, and disregards other important variables and
moderators that impact learning. Therefore other variables and processes are incorporated into
the new design model to offer a more comprehensive and holistic view of the intricate
learning process derived from simulation game playing. These variables range from level of
participation to quality of instruction to level of satisfaction.
Level of participation refers to the time that students have actually used in the
simulation game, demonstrating their commitment to and participation in the game. In
Slavin’s QAIT model (1996), time is one of the key aspects to influence learning, as the
degree to which students have given enough time to learn the material being taught – through
game simulation – positively correlates to the actual learning of the material. Time here is
measured through the number of hours put into playing as well as the number of game rounds
that the student participated – even though the later is reflected in the former.
Perceived quality of simulation games accounts for the student’s perception of
content quality, game quality and related IT challenges. Basically it reflects the player’s
assessment of the game characteristics, which should inform his/her (a) belief in the game as
improving general performance and knowledge (b) perception of realism as well as
motivation and excitement to play the game, and (c) any possible technological issues and
related frustrations. Douglas & Miller (2007) argues that “creating a fun, engaging, satisfying
environment that students enjoy participating can affect student ownership” (p. 9 of 11),
where student ownership is a measure of intrinsic motivation that empowers students to take a
role in the game. Feinstein, A. H. & Cannon, H. M. (2002) and Gosen & Washbush (2004)
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suggest that the learning environment’s perceived quality is expected to impact learning
outcomes.
Degree of involvement represents the intensity of involvement in the game, or in other
words, it demonstrates the student’s engagement in game playing (Hense et al., 2009). This
engagement is evidenced by the attendance of the student to the game simulation sessions, the
effort put into game playing and the related teamwork split.
Quality of instruction reflects the interplay of different teacher characteristics, which
informs how good the instruction is. Put differently, it represents the degree to which the
information and skills are presented in a way that is easy for the student to learn and that is in
line with the core learning objectives of the course – i.e. there is a correlation between what is
taught and what is assessed. Slavin’s QAIT model (1996) considers this variable another key
aspect to influence learning, and covers teaching methods and tactics used by the instructor,
as well as learning materials, curriculum, software, etc.
Group interaction assesses the interplay among group members when participating in
groups in the hotel simulation games. Such interplay derives from required teamwork by the
game and implies the convergence of different skills, competences and knowledge into one
group, where members need to cooperate to achieve the game goals. However, group
interaction facilitates social loafing and free-rider behavior, suggesting that group tensions
may occur (Hense et al., 2009). The dynamics of group interaction is expected to impact
individual learning.
Level of acceptance showcases the student’s perception of relevance and willingness
to play the simulation game over and over again. Recognizing the usefulness of the game as
improving knowledge and performance, and forming an overall positive judgment in regards
to the game, impact how actively the student engages in game play and therefore accepts it as
a learning tool. Hense et al. (2009) argues that student’s learning and acceptance is expected
to contribute in accomplishing the game’s overall goal.
Level of satisfaction reflects an overall assessment of the student’s perception in
regards to hotel game simulation, evidencing if they like and are content in playing the
simulation game and incorporating it as part of the learning process. Being satisfied with the
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game is expected to increase engagement in game playing and therefore learning (Hense et
al., 2009).
To sum up on the process domain by considering all process variables as a whole, then
the process domain basically reflects how the input variables inform the learning process and
the different key factors that are relevant as part of this process, further impacting the
occurrence of learning outcomes. Understanding how the input variables inform the learning
process requires, however, an understanding of how the learning process is moderated. The
interplay of relevant theories and models can shed light in this regard, by providing a basis for
understanding how the learning process is facilitated.
The Experiential Learning Theory as conceptualized by Kolb (1984) offers some
valuable insights into how the learning process is created. Learning involves the continuous
process of responding to personal and environmental demands, reflected by the input
variables of the model. Through the combination of student’s, teacher’s and game’s
characteristics, the game elicits a process where the student creates knowledge through the
transformation of experience.
In other words, the way the game simulates valid and realistic scenarios within the
learning objectives of the course, through an engaging and exciting learning environment that
the teacher is familiar with and committed to embrace, while motivating students to
participate (both through intrinsic and extrinsic motivational drivers), promotes repeated
game playing and experience, thus creating knowledge and inducing learning.
The different rounds of the game are a key aspect in facilitating this knowledge
creation process. The participants take concrete actions in one round, which result in game
experience through game simulation feedback, and this resulting feedback is observed and
reflected upon by participants, in an attempt to make sense of the causalities and implications
of their actions. Conceptualizing and constructing strategies and approaches based on these
reflections (to improve their game performance in the next round), the participants inform
their new game actions, which they put into practice in the next round. The repetition of this
process through several rounds leads to the refinement of conceptualized strategies and
approaches, and thus to the understanding of the underlying relationships among different
factors within the game, therefore informing the knowledge they create. Consequently,
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players engage in experiential learning by their intrinsic and extrinsic motivations to play the
game.
Intrinsic and extrinsic student motivations, reflected by student’s enjoyment of playing
the game or his/her will and/or requirement to attain a specific grade, lead to repeated and
motivated game-play. This in turn activates several of the process variables (e.g. level of
participation, degree of involvement, quality of instruction, etc.), facilitating learning.
Building on the Experiential Learning Theory, the Constructivism Learning Theory
supports the construction of knowledge through prior knowledge and experience, where
instruction supports this construction. Construction of knowledge, however, is also influenced
by the social, cultural and historical settings (e.g. custom, rules, laws, roles) where knowledge
creation takes place, thus emphasizing collective learning and contextual learning.
The implications of this broader perspective is that, not only learning is derived from
the continuous process of responding to personal and environmental demands represented by
the input variables of the model, but it also stems from the interplay of different process
variables taking place upon learning.
This means that student’s, teacher’s and game’s characteristics elicit a learning
process through construction of knowledge, which is also shaped by the way the group of
students interact and work as team (i.e. group interaction), and by the game constraints and
limitations evidenced through game feedback from different game rounds – which is based on
rules and roles built within the game. In this context, quality of instruction supports
construction, as both the game and teacher feed back to students based on their performance
in the respective rounds, and the teacher provides cues to the relationship between relevant
theories and their related practical application within the game. Therefore, The Constructivist
Learning Theory provides insights regarding collective and contextual learning, in addition to
the learning insights stemmed from the Experiential Learning Theory.
Slavin’s QAIT model (1996) also sheds light into the learning process the student
undergoes when playing simulation games. According to Slavin, for instruction to be
effective, quality of instruction, appropriate levels of instruction, incentive and time need to
be simultaneously adequate.
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Motivation – intrinsic and extrinsic – plays a key role in Slavin’s model, since it
informs the time or level of participation in the game, and it drives the student into engaging
in the game. The teacher can increase intrinsic motivation by “relating topics to students’
personal life, or by allowing students to discover information for themselves” (Slavin, 1996,
p.3); whereas extrinsic motivation can be enhanced through incentives – i.e. extrinsic tools
designed to increase students’ extrinsic motivation (e.g. grades, praise).
Teacher’s motivation, training experience, preparation and expectations also play a
role in enabling learning. These characteristics impact the quality of instruction and
appropriate levels of instruction, in the sense that the teacher (1) ensures that the simulation
game covers the learning objectives of the course and that the students have the pre-requisites
to learn the material; (2) is familiarized with this tool, being able to provide constructive
feedback and; (3) is motivated to teach through simulation games.
According to Slavin (1996), when the students are presented with information and
materials that make sense to them (derived from students’, teacher’s and game’s
characteristics), when they have the pre-requisites to learn this material (derived from
teacher’s characteristics), when they have the motivation to engage in learning (derived from
student’s characteristics, degree of involvement, level of acceptance, level of satisfaction),
and when the time allocated to learn is appropriated (derived from level of participation), then
learning or instruction effectively occurs (outcome).
4.1.3. Debriefing
Debriefing is a review and analysis of the events that occurred during simulation
game playing, where the events are described, the reason why they occurred are analyzed, and
the eventual mistakes and corrective actions discussed. Basically, it’s an overall assessment of
the way the game unfolded throughout the different rounds and the underlying rationale for
such unfolding.
The intention of the debriefing is to bridge theory and practice, by linking the events
that took place in the game to underlying theoretical explanations. Consequently, the
debriefing also bridges the gaming experience with the real world, converting game events
into learning experiences. Robert Austin (Interview, April 12, 2011) characterizes debriefing
as a very important part of simulation games, and that the learning processes only finish until
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you do the debriefing. In addition, he uses the debriefing sessions to determine the
effectiveness of simulation games along with students’ performance at the exam.
According to Garris et al. (2002), “learning by doing must be coupled with the
opportunity to reflect and abstract relevant information for effective learning to occur and for
learners to link knowledge gained to the real world” (p. 455), which is supported through
both the Experiential and Constructivist Learning Theories. Debriefing techniques provide the
guidance and support to aid in this process.
Taking the process domain and debriefing as a whole, the students time and
engagement put into playing the game, the way they perceive the quality of the simulation
game and the instruction, the dynamics of their group interaction, their level of acceptance
and satisfaction, and the opportunity to review and analyze game events, all inform the extent
to which the students playing the game learn.
4.1.4. Outcome Domain
Garris et al. (2002) model defines the process domain simply as learning outcomes,
without touching upon the different kinds of outcomes, neither their different learning stages.
Bloom’s Taxonomy of Learning model is a valuable tool in this regard, by considering
detailed possible learning outcomes, distinguished between two outcomes’ domains:
Cognitive and Affective.
Faria’s study (2001) supports the definition of learning outcomes, resulting from
participation in business simulations, in regards to these two domains. She reviewed a range
of papers covering the analysis of business simulation games over a 25 years period. Her
findings are parallel to Bloom’s model, thereby confirming that learning objectives and
outcomes can be classified in terms of cognitive and affective learnings. Robert Austin
(Interview, April 12, 2011) is confirming the above, that possible outcome measurements are
not only about cognitive learnings but also how the level of engagement and interest the
students are showing for the game.
Incorporating Bloom’s Taxonomy into Garris’ outcome domain can provide
significant insights into the type and the extent of different learning outcomes derived from
hotel simulation games.
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4.1.4.1. Cognitive Outcomes
Cognitive learning should be understood as the actual acquisition of knowledge over a
period of time. Knowledge acquisition can be viewed in various forms, and as mentioned in
the Chapter 3.3. - Theoretical Approach, it can be classified in six different levels according
to Bloom’s (1956) cognitive domain.
Measuring cognitive outcomes objectively is however a challenging task. Grasping
actual cognitive learning is usually difficult, as there aren’t many practical ways of assessing
these types of outcomes objectively. As a result, perceived cognitive learning is usually used
as a proxy for evaluating cognitive outcomes.
Anderson, P. H., & Lawton, L. (2009) investigated why little progress has been made
in objectively assessing cognitive learning in regards to business simulations (simulation
games). The authors concluded that perceptions are usually employed because they are easy
to measure, and because, when objectively assessing learning outcomes, only the lower levels
of learning on Bloom’s Taxonomy are measured. Focusing only on these lower levels of
learning presents twofold shortcomings. First, it isn’t very insightful to investigate only basic
knowledge or comprehension, since a pre-requisite to studying in higher education
institutions is that the students must already have reached that point, or be able to reach that
easily regardless of method. Second, the barriers for measuring higher-level outcomes are
particularly high in relation to simulation games.
This criticism against the measurement and therefore usefulness of simulation games
to enhance cognitive learning is examined in Elizabeth Clarke’s literature review “Learning
outcomes from business simulation exercises”. Her findings show that all learning outcomes
are in fact in the domain of affective learning, not cognitive learning. These findings can
potentially reduce the legitimacy and educational merits of simulation games in terms of their
validity as effective learning tools (Clarke, E., 2009).
In order to address these shortcomings and criticisms, and attempt to provide
significant insights into objective cognitive outcomes stemmed from simulation game
participation, and therefore their effectiveness as learning tools, this thesis combines different
assessment platforms and methods when evaluating hotel simulation games’ effectiveness.
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Objective evaluations are carried out through the comparative assessment of
performance at exams (through final grades) for students who participated in the HotelSim
simulation game with students who didn’t participate in these games (i.e. class from previous
year), as touched upon in Chapter 5.2 and Chapter 6. Combined with this assessment, a more
subjective measure of cognitive learning is also appraised, through the use of questionnaires
and course evaluation reports provided by the students. In this later case, perceived cognitive
learning is assessed.
Having a mix of perceived cognitive learning evaluation and a more objective
cognitive learning assessment (i.e. grade given at the end of the course) intends to balance
learning outcomes derived from hotel simulation games with less subjectivity. Using the
grade as objective measure provides a tangible basis to investigate individual performance
against the learning objectives of the course and its material. Further, the grade illustrates
performance that requires involvement and knowledge through the higher levels on Bloom’s
taxonomy, since analysis, synthesis and reflection are required for good performance in the
project writing and oral exam.
The requirements for the written project are that students should act as consultants,
evaluating how another group’s hotel has performed in comparison with the other hotels, and
giving suggestions as to what could had been done better and what could be done differently
in the future. Theory has to be incorporated in the project, and a section of evaluation and
recommendations need to be presented (Bobek, K., 2011). These requirements, in
combination with the oral examination, are evaluated against the learning objectives of the
course, appraising how many have been fulfilled. The grade is a result of the combined
evaluation.
Subjectivity is therefore significantly reduced by using the grade mark as (objective)
measure of simulation games’ effectiveness. Using student’s performance at the exam is also
one of the ways Robert Austin (Interview, April 12, 2011) employs to appraise the
effectiveness of different learning tools, such as simulation games.
Another possible form of objective cognitive outcome is the performance results from
the HotelSim itself. However, since the students played the simulation game in groups, it is
not be possible to isolate the performance measures on an individual level. Therefore this
measure is not included as part of the evaluative measures employed.
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4.1.4.2. Affective Outcomes
Affective learning is the students’ attitudes towards learning, reflecting their emotions,
feelings and beliefs in regards to specific learning; and it differs from cognitive learning in the
sense that it represents perceived acquired learning, which is not necessarily actual cognitive
learning.
Affective outcomes primarily focus on the individual student’s attitude towards the
simulation game and how he/she perceives the learning gains (Faria, A. J., 2001). Garris et al.
(2002) has similar views on the definition of affective learning outcome, and support that
affective learning outcomes happen when there are attitude changes in affective reactions,
such as feelings of confidence, self-efficacy, attitudes, preferences, beliefs, etc., regarding an
activity.
The challenge in measuring affective outcomes is to argue that their answers hold
educational value, as they are based on perceptions measured in self-reports from students.
Therefore, a reliable measurement of student’s attitudes is a difficult but necessary first step
before any theories can be considered (Statsoft.com, 2011). A reliable measurement is when
the items in the self-report questionnaire correlate to each other with a low variance, resulting
in a more precise measurement of its variables, thereby reducing the constraints in the
challenge. This is discussed further in Chapter 5.1. – Item Consistency Analysis.
4.2. Relating the Logic Model for Hotel Simulation Games To
Questionnaire As discussed in Chapter 3.4.2 – Quantitative Research, the questionnaire was
developed to cover the relevant variables in the three domains of the logic model for hotel
simulation games. To facilitate the understanding and linkage between the variables of the
model and the questions, Table 1 in Chapter 9. List of Figures and Tables has been developed.
Questions 2 to 14 address the input and output domain variables, and questions 15 to 19 cover
the output domain. The entire questionnaire is found in Appendix C.
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5. Results & Analysis This chapter opens up with an analysis of the statistical appropriateness of the empirical
research carried out. Having validated this approach statistically, the research results are
presented and analyzed, under the prism of the designed Logic Model for Hotel Simulation
Games. The chapter concludes with relevant insights from Harvard professor Robert Austin,
providing practical and in-depth insights into hotel simulation games’ effectiveness.
The different qualitative and quantitative researches undertaken aimed at investigating
the effectiveness of hotel simulation games as learning tools, by uncovering the main factors
and processes that influence learning through hotel game simulation, as well as the nature and
extent of potential learning outcomes. In this regard, it attempted to link playing hotel
simulation games to learning, to effectiveness of game simulations as academic learning
tools.
The overall framework reflecting this learning process was conceptualized in Chapter
4.1 – Designing the Logic Model for Hotel Simulation Games – and further investigated
through its application to the case of HotelSim. The results of such empirical investigation are
presented and analyzed in this chapter. Before such analysis is carried out, however, a
discussion of statistical appropriateness of such investigation is briefly undertaken.
5.1. Item Consistency Analysis “No matter how clearly a question is phrased there is always the possibility that the
interviewee may either inadvertently or deliberately give an untruthful or misleading
response. Therefore, it is advisable, when constructing an interview, to include questions that
will reveal any inconsistency in responses” (Clarke, A., 1999, p. 74).
5.1.1. Reliability
With that in mind, a consistency analysis of the questionnaire (Appendix C) was
performed, in order to assess the reliability of its individual items/variables presented in
Chapter 4.1. – Designing the Logic Model for Hotel Simulation Games. The assessment of
scale reliability is based on the Cronbach’s coefficient alpha (α).
Reliability refers to an instrument’s ability (in this case a self-report questionnaire) to
provide consistent results in repeated uses, and to meet the challenge of securing a precise
measurement (Marguerite, G. L., Dean, T. S., Katherine, H. V., 2010). Some see reliability as
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the same as validity, but in fact they are not exactly the same. Reliability is considered the
precision of measurements (i.e. measuring things consistently), whereas validity is considered
the accuracy of measurement (i.e. measuring things correctly) (Marguerite et al., 2010).
Reliability is necessary but not sufficient for validity – though still an important part of the
analysis.
Nunnally, J. C. (1978) has indicated a Cronbach’s coefficient alpha (α) of minimum
0.7 to be an acceptable reliability coefficient for most researches, and a minimum of 0.6 to be
desired in exploratory researches. This coefficient can vary from 0 to 1, so the closer the
coefficient is to 1, the more reliable the generated scale is, regardless of the research type.
The Cronbach’s alpha reliability of the overall research (51 item variables) in the self-
report questionnaire was α = .894, which is over the minimum and quite close to 1, thus
evidencing high reliability of the questionnaire. The only item variable that was not included
in this calculation was the PLI test result, as it was optional for students to answer. Therefore
including this variable in the calculation would misrepresent the coefficient and would
downsize the number of the sample to 22 if processed by the Listwise delection. Listwise
delection only includes cases (i.e. respondents) with values in all responses, which is known
as the complete case approach (Hair et. al., 2010). The rationale behind using the Listwise
delection is to increase the statistical power of the analysis, as cases with missing data are
excluded; however, it decreases the sample size if not all responses have been answered. In
the case of the PLI question, it was not obligatory for students to answer it, and therefore it
should not be considered a missing value. Also, open-ended questions and descriptive
questions were not included in the Cronbach’s alpha analysis as it is not possible to include
string variables (IBM SPSS, 2011) bringing the total valid cases for the Cronbach’s alpha
down to 30. Those 30 valid cases were then subjected to the above-mentioned consistency
analysis.
One of the reasons for the general high Cronbach’s alpha (α) can be explained by the
choice of using the Likert method as the measurement scale. According to Maranell (1974)
”The Likert method of scoring by summing the responses in each category has commonly
been found to yield higher reliability coefficients” (Maranell, G. M., 1974, p. 268). Another
reason can be the fact that the construction of the questionnaire took as starting point previous
surveys about the same topic, with already reliably proven questions.
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Looking closer at the individuals’ Cronbach’s alpha for each of the seven dimensions
(i.e. a dimension is the overall topic of a set of questions) in Figure 9 below, it can be noticed
that are not Cronbach’s alphas are above the recommended lower limit of 0.6. A Cronbach’s
alpha of only α = .340 is found in the dimension “What are your social learnings from
participating in the simulation game”. This low Cronbach’s alpha needs to be examined
further to ensure strong internal consistency before proceeding to an assessment of its validity
(Hair et. al., 2010). As mentioned, a coefficient of minimum 0.6 is generally considered the
lower limit for reliability in exploratory research, and the item-to-total correlations is
suggested by Hair et al. (2010) to exceed 0.5 to the summated scale result.
Looking at the Item-Total Statistics in Table 6 – List of Figures and Tables, three
items/variables have a negative item-to-total correlation. These items are: “I experienced that
some in my group didn’t participate in the group work as expected” with -.338, “I rather
choose my own group members than the lecturer decides who I am in group with” with -.120
and “HotelSim should be played individually, instead of in groups” with -.403. As those are
not intercorrelated with the other items, they were excluded from that respective dimension
and are not used in the analysis section. A new internal consistency analysis was again
performed on these dimensions, resulting in a satisfying Cronbach’s alpha of α = .772.
Valid cases N=30 (Listwise Delection)
Cronbach’s
Alpha
7 Dimensions Alpha (α)
All 51 items – (48 items .919) .894 What are your overall perceptions regarding the simulation game (5 items) .642 What are your learnings from participating in the simulation games (7 items) .736
What are your social learnings from participating in the simulation game (12 items)
.340
What are your social learnings from participating in the simulation game (9 items as 3 items were excluded due to negative item-to-total correlation)
.772
Is the simulation game a recommendable tool for future use (4 items) .826 How have you benefited from participating in the simulation game (9 items) .808 How satisfied are you with the simulation game experience (9 items) 0.740
Figure 8: Overview of Cronbach’s alpha for each of the seven dimensions.
Overall, the measurement scale of the seven dimensions doesn’t show any sum scale
lower than 0.6 and are thereby passing the threshold for exploratory researches. Except the
first dimension, the others are also passing the threshold of 0.7 for regular researches,
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supporting that the design of the scales is more than sufficiently reliable. The underlying
construct of the questionnaire and its high consistency are, in other words, showing that the
items in the questionnaire are measuring the same over time and returning stable responses,
meaning that if the research is repeated under a similar methodology is should return
consistent responses (Hatcher, 1994, as cited in Journal of Extension, 1999; Joppe, 2000).
5.1.2. Validity
Where reliability is whether or not the results are replicable, ‘construct validity’ is
considered the accuracy of measurement; that is, if it’s actually measuring what it is intended
to measure. Similar to reliability, validity can be asserted by the application of statistical tests
or investigation of the research processes (Jobbe, 2000). But unlike reliability, validity must
be inferred in this case, as it statistically cannot be directly measured with true values due to
time limitations.
It is out of scope of the thesis to assess construct validity, as it requires many months
of practical use of the survey instrument in various settings to measure its results, thereby its
construct validity (Statistics.com, 2011). Instead, as discussed in Chapter 3.2 - Theoretical
Validation, incorporating various methods for data gathering intended to minimize any issues
regarding validity. A similar approach was proposed by Anderson, Cannon, Malik and
Thavikulwat (1998), as cited in Gosen, J., & Washbush, J. (2004, p. 10 of 25), when assessing
validity. Their four instrument standards were:
X “Show evidence of reliability between the results obtained at one time to those
obtained later when applied to the same subject
X Be able to discriminate between individuals possessing different skills or performance
levels
X Show convergence with other instruments measuring the same constructs
X Yield normative scores for different populations.”
The first standard was supported in the previous chapter and the remaining standards
are covered later in this section. Hence, the evidence for validity can be derived from the
analysis of students, if the results come to the same conclusion via different methods
(triangulation), and if the results can be applied on the general population.
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5.2. Research Results & Analysis Having discussed the statistical appropriateness of the empirical investigation, the
results of such investigation are presented and analyzed in the following sections.
5.2.1 Input Domain
5.2.1.1. Student Characteristics
In order to get an overview of gathered responses for the analysis of student
characteristics, descriptive and frequency statistics are used. All the full results are found in
Chapter 9.1. - List of Tables: Full Results, tables 7-12, and the meta-data set is found in
Appendix D. It presents a sample size of 34 students, N = 34. A sample size of 34 students
out of 64 students, which is the total amount of students who both attended the exam and
completed the questionnaire, gives a response rate of 53.1 %. This can at first glance look like
a low response rate, but comparing with official evaluation reports from CBS shows that 34
valid responses is in fact a high number. The Course evaluation (Teacher’s report of the
Leadership and Strategy course) had a response rate of 31.7 % in 2010 (Appendix E) and 34.4
% in 2011 (Appendix F). And the response rate of the overall study program of Service
Management in 2009/10 (CBS, 2011b, page 29 of 33) was 53.1 %.
Beginning with age, the frequency statistics reveal an age average (Mean) of 27, with
maximum student age of 41, minimum student age of 22, and the most frequent age (Mode)
of 25. Regarding gender, 24 students are female and 10 students are male. In terms of
cognitive level, the highest PLI score is 38, the lowest is 10 and the mean is 26. The number
is based on a sample group of N= 23 only, instead of N= 34, as 11 of the respondents were
missing in this data set. A PLI result of 22 has been found to be the general world average
level (Humanostic, Email communication, March 24, 2011). This indicates that the 23
students who included their PLI test result from the total sample group in average have higher
cognitive ability than the worldwide average. This does not necessarily represent that all
students in the total sample group (N=34) have higher cognitive ability, it presents just an
indication. Also, the standard deviation is 7, showing a somewhat diverse cognitive level in
the sample group despite the above average level.
The sample group is constituted by wide range of bachelor’s degrees from finance,
marketing, economics, communications, human resource management, information
technology, and 12 other different programs (all with either 1 or 2 students only). The
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majority of the students come from a bachelor degree background from either service
management (8 students) or languages (4 students). All the various degrees are somewhat a
representative sample of all the possible degree programs that are offered at CBS, though the
service management students are a high representation (23.5 %).
The widespread background in bachelor degrees is also reflected in the professional
experience; ranging from general management, economics, engineering to events, marketing,
tourism and hospitality. The strong representations are within the hospitality industry with
20.6 %, tourism with 14.7 %, administration and sales each with 11.8 %. The remaining
sectors have either 1 or 2 students with a different professional background.
Looking at previous knowledge in simulation games reveals that 82.4 % have never
tried simulation games before, 14.7 % have tried once before and only 2.9 % (1 student) have
tried it 2 times or more. This one student is also the student who has tried it in a professional
environment.
Chapter 9. List of Figures & Tables, Table 5, shows the results in detail. In short, the
motivation for using simulation games is very high. 58.8 % strongly agree that they use it
because they were part of the course curriculum, 55.9 % either strongly agree or agree that
they expected it would raise their interest for the course subject, and 35.3 % strongly agree
that they expected it would involve them more in the course compared to a regular teaching
lecture. Less than 6 % (2 students) disagree with the above questions. Overall, the results
show strong motivation and positive attitude beforehand for using simulation games.
To further indicate the potential validation of the relationships between the above
input variables and the outcome variables, selected cross-tabulations were performed.
Assessing this effect through cross-tabulation is the appropriate way to demonstrate the
influence of input on output variables when these variables are discrete (Agresti, A., 2002).
The paired tabulations carried out focused on selected input variables, as not all the
thirteen input variables vary significantly among the respondents. In the case of bachelor and
professional experience, considering the average age of the students, their potential effect on
the outcome is not expected to be significant on average, and therefore these two variables are
not assessed through cross-tabulation.
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The below cross-tabulations provide insights into the relationship between selected
input variables and the cognitive outcome variable, as measured by grade.
Based on the presented results in the bar chart below (Figure 9), age does not seem to
impact students’ cognitive learning derived from playing the HotelSim simulation. No pattern
is evident in terms of grades attained in respect to different age levels. For all age groups, the
grades are in general concentrated among 7 and 10. A great percentage (considered equal or
higher than 50 %) of the students at the ages of 23, 24, 27 and 31 received the grade of 7, as
well as a great percentage of students at ages of 24, 25, 26, 30, 36 and 39 received the grade
of 10.
Figure 9: Cross-tabulation: Age vs. Grades
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Figure 10: Cross-tabulation: Gender vs. Grades
Based on the results showed in Figure 10, females assume to receive higher grades
than males. Where 60.9 % of the females received either 12 or 10, the males only acquired
30.0 % of their grades to be either 12 or 10. In the bottom of the scale, male is higher
represented by 70 %, and 39.1 % for females with either 4 or 7.
Figure 11: Cross-Tabulation: PLI test score vs. Grades
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Figure 11 illustrates the results for cognitive level versus grade. The first evident
indication is that the two students, who got the lowest and highest PLI scores, were also those
two students who received the highest grade ‘12’. The PLI mean score is 26, and those
students who had a cognitive score equal to this mean were graded either 7 or 10. These
students represent 48,6% of the total number of students who got either 7 or 10. In general,
there doesn’t seem to exist a significant correlation between cognitive level and grade, as
students with higher (above the mean) PLI scores have more frequently both higher (10) and
lower (4) grades, even though the higher grades occur more frequently than the lower grades
for students with higher (above the mean) PLI scores. Also, the maximum grade was received
by students with the higher and lower PLI scores, though a reason for that may be in the fact
that they were part of the same group and therefore some ‘help’ or ‘free-rider’ behavior might
have happened.
5.2.1.2. Teacher Characteristics
- Interview with Karsten Bobek: The role of the Instructor
The instructor had almost 20 years of training experience with simulation games from
his professional career at Nestlé, since he first got acquainted with simulation games in 1994.
In Nestlé, the orientation of the games was naturally within retailing, covering aspects such as
pricing, negotiating, marketing, planning, etc., whereas others games were focusing on
teamwork and organization optimization. The overall idea of working with simulation games
in a professional environment was, according to Karsten Bobek (2011), to illustrate the
processes that individuals, teams and departments within an organization, as well as
stakeholders across companies, competitors and so forth, undergone as part of having a stake
in the business. Also, the intention was to understand how these processes work and how they
could be optimized for future improvement.
The instructor’s motivation to include this technology in an academic education was
to create more meaningful learning experiences that hopefully in the end would result in
better-educated students; meaning that the students should have better capabilities to enter the
job market after the completion of the service management Master’s degree program.
Thereby, the game would bridge what is learnt in the business school with what is relevant for
work in a professional environment.
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In regards to the preparation phase, Karsten Bobek (2011) stresses that the allocation
of sufficient time, combined with the necessity that the instructor holds industry knowledge of
the simulated work environment, are both essential success criteria to consider beforehand.
More preparation time for the teacher, as well as for the students, is necessary and required
when using simulation games, as it is more demanding to teach with the outset of simulation
games compared to regular teaching books; it is a learning process that differs from what both
the teacher and the students are familiar with. Further, as a competition element is part of the
game, the game naturally requires the students to put more time and effort into the game than
their competitors if they want to win, thus increasing the effort of learning - similar to the
situation where a company wants to outperform its competitors in the professional
environment.
The intended outcomes and expectations from utilizing simulation games as learning
tools are to improve students’ critical and analytical skills, as well as their understanding of
the hotel industry and key related processes. This is possible through the set-up of the game in
dynamic rounds reflecting complex and realistic scenarios, where the learning process is
evolving through stages and boosted by a competitive environment. Such situation represent a
holistic and complex view close to the reality expected later in their careers, thus bringing
more interesting and aspiring aspects into teaching than what books and articles can deliver,
or at least to support the existing teaching methods.
In the case of the research carried out in this thesis, it is not possible to quantitatively
assess the direct impact of teacher’s characteristics in the learning outcomes of students
playing HotelSim (even though it is expected to be positive due to on the above discussion),
since the study investigated the implementation of the HotelSim game as learning tool by
only one teacher. A longitudinal analysis is thus required to examine this impact, and should
be the focus of future studies in the hotel simulation games’ field.
5.2.1.3. Game Characteristic
Content quality is how the learning objectives of the Leadership and Strategy course
are incorporated into the game. The official learning objectives of the course state that
“students must demonstrate comprehensive knowledge, and understanding of topics, theories,
methods, and models dealt with during the course” (Course description, 2011). To measure
the association to the HotelSim game, the following items were included in the questionnaire:
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“Enabled me to apply relevant theories and models from the curriculum to a practical
scenario” with the result of 3.735, “Increased my knowledge of general leadership” with the
result of 2.97, “Increased my knowledge of strategic issues in service management” with the
result of 2.60, “Increased my understanding of the factors that contribute to operating a
successful hotel” with the result of 2.03 and “It gave me the chance to speak out and be part
of the decision-making process more than in a normal lecture environment” with the result of
2.93.
All in all, the results show that respondents agree that HotelSim has enabled them to
learn through the application of theory and knowledge. This supports that learning outcomes
can be achieved using hotel games that are aligned and match objectives of the course, thus
justifying the inclusion of simulation games in higher academic educations.
Game quality reflects the specific simulation model and the particular functionalities
of the game that facilitates learning. Various items measured game quality. “I view the
simulation as a valid representation of reality to acquire applicable knowledge” with the
result of 3.03, represents a somewhat agree response. “The strategic decisions taken by my
group had the forecasted effects in the simulation game” with a positive result of 2.60,
between agree and somewhat agree, and a low standard deviation of 0.97, indicates consensus
about the game quality. Karsten Bobek (2011) further confirms that the educational qualities
for the game are fulfilled. Additionally, the current use of HotelSim by well-recognized
companies and universities supports and add credibility to the game’s quality. Other
perceived quality assessments are dealt with in the section 5.4.2. – Associations with the
Constructivist Learning Theory, under “perceived quality of simulation games”.
Further, IT challenges don’t appear to have any negative impact on the students’
experiences of the game. Only one student out of 34 mentioned that the game at one point
was somewhat slow in reaction when inserting the numbers. In other words, there were not
any relevant IT challenges. Robert Austin (Interview, April 12, 2011) argues that it is !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
5! The! presented! results! in! this! section! and! following! sections! in! this! chapter! refer! to! the! arithmetic!average!of!the!total!responses!based!on!a!7Xpoint!Likert!scale!(unless!otherwise!stated).!This!means!that!individual! responses! range! from! (1)! strongly! agree! to! (7)! strongly! disagree,! and! that! the! average! (i.e.!result)! is! a! number! somewhat! in! this! interval.! The! closer! the! results! are! to! 1,! the!more! positive! is! the!result!in!regards!to!the!statement!under!evaluation.!Results!lower!than!4!are!considered!positive,!whereas!results!higher!than!4!are!considered!negative.!Refer!to!Chapter!9.1.!X!List!of!Tables:!Full!Results,!tables!7X12!for!more!detailed!results.!
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important that the students don’t have to worry about the technical side of the game, that is
should be straightforward and without any difficulties for everyone. If the simulation game is
part of the learning environment in class, then the IT infrastructure should be in place and
working.
5.2.2 Process Domain
5.2.2.1. Associations with Experiential Learning Theory
The cornerstone of the Experiential Learning Theory, that relies in creation of
knowledge and thus learning through transformation of experience, is elicited by the
HotelSim game. This game promotes a learning environment that facilitates the four modes of
the learning cycle (Kolb, 1984), catalyzed by the specific characteristics of students and
teacher. To be able to go through these four modes, students need to experience and be
engaged in game actions, so their involvement enables reflection, conceptualization and
knowledge creation.
The research results uncover that the predictions of the theory are supported by
HotelSim, where the learning cycle is activated through students’ degree of involvement,
prompting students to go through the four modes of the learning cycle. The first mode,
concrete experience, is when the student is actively involved in the experience and willing to
experience new things. The item ‘The game required a high degree of involvement from me’
received a result of 2.33 out of the seven-point Likert scale, which lies almost between
‘agree’ and ‘somewhat agree’. It indicates that the students taking part in the game were
involved in it.
The second mode, reflective observation, can be illustrated when students are
reflecting on the experience after each round and finding meaning in their experiences. This
was measured through the item ‘The debriefing time was sufficient to review and analyze the
results of each simulation round’, with a result of 3.10. The third mode, abstract
conceptualization, is when the students are using their analytical skills to conceptualize the
experience and integrate new logical conclusions. It was measured through the item ‘The
simulation game was a good tool to test my analytical skills’ and the result was 2.36.
The last mode of the four, active experimentation, presented a result of 2.36 in the
item ‘The simulation game was a good tool to test my decision-making ability’, which
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consider if the students are able to use the gained experiences in their future decisions in the
game and elsewhere, that is, if they are able to apply new learning into new experiences
through decision-making and problem solving abilities. In short, the research results confirm
that the students have been through the idealized learning cycle – the experiential learning
cycle – meaning that the experiential learning theory has mediated and promoted the learning
process.
To illustrate the learning experience above, an example of the dynamics of the hotel
simulation game is presented. When the instructor is employing examples and readings to
foster the engagement in the simulation game, a concrete experience is initialized. The
experience becomes concrete when the students have understood, experienced and worked
with it over some time through the game simulation. Following a simulation round, it is time
for reflective observation. This happens when the instructor is taking the time to discuss and
reflect on the results and experiences of the round. Strategies are laid out before commencing
the game, decisions are taken, but these are changed along the way as outcomes after each
round are announced and the competitive simulated environment evolves, thus reflecting
abstract conceptualization and active experimentation. Different teams have different
strategies and each team member has a different role in the game and analytical skills to deal
with the presented outcomes and adapt the strategy and approach for the next round, which
each up affecting other team’s results. This competitive environment brings in dynamism and
interaction across all teams and individuals playing the simulation game.
Another example of how experiential learning theory can be beneficial to learning is
to consider the shift in student’s concentration on outcome to process instead; meaning that
students usually tend to concentrate on the outcome (grade) rather than the learning process
(lectures, discussion, studying, preparing, game, etc.) when conceiving learning, and this is
partially twisted through the learning moderation promoted by the experiential learning
theory. According to this theory, the process should be considered the causal link between
input and outcomes, and if learning is desired, then the process should be the focus point of
the learning situation and of what is being learned (Kolb, A.; Kolb, D., 2005).
Robert Austin (Interview, April 12, 2011) has no doubt in confirming the importance
of focusing on the process in a learning situation, especially the debriefing part of the process,
known as the reflective observation in the theory. It may seem obvious, but in fact the
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students’ approach and the official learning strategy of CBS is to focus on the grade outcome
(Kvalitet i Undervisningen, 2006, p. 20-21 of 75). This is a strategy that derives from the
book “Teaching for Quality Learning at University” by John Biggs, where one of three
important factors is to study with the purpose of fulfilling the course objectives, regardless of
the way such objectives are fulfilled. Such strategy certainly has its pitfalls and by introducing
hotel game simulations and their respective debriefing, this approach can be progressively
switched.
Contrary to this approach with focus on the outcome, then Kolb’s (1984) interest is
more on the process towards the outcome, than the outcome itself. Meaning that learning is
seen as an emergent process where outcomes only represent historical learnings, not the
knowledge of the future (Kolb, 1984). The experiential learning theory is built on six
propositions, where one of them is “learning is best conceived as a process, not in terms of
outcomes. To improve learning in higher education, the primary focus should be on engaging
students in a process that best enhances their learning” (Kolb, A.; Kolb, D., 2005, p. 2 of 21).
And that is done through continuously stimulating the student’s inquiry and skills for deriving
and modifying new knowledge through experiences.
This proposition is confirmed through the presented results. They support that hotel
simulation games, through the process of direct experience, plays a role in facilitating
knowledge creation and therefore has an effect on learning.
5.2.2.2. Associations with Constructivist Learning Theory
Many of the constructivism principles of effective learning environment and the
learning characteristics of constructivist learning processes are found in HotelSim, thus
supporting the use of hotel simulation games as learning tools. The results reveal several links
between constructivism and simulation games. Examples of those results are presented here.
Learning is the process of constructing new knowledge and in simulation gaming the
process is very similar to the one predicted in constructivist learning theory. The research
findings support this argument, validating that the process plays a role in facilitating learning
within the context of hotel simulation games. For example, the item measuring ‘The
simulation game provided me with knowledge that I can apply in managing hotels in real life’
reports a result of 2.40. This shows that the students are able to apply knowledge in the future
based on gains in learning they have self-experienced playing the game. This process is the
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core of constructivism because the participants involved construct new knowledge exactly
from those experiences, where instruction supports this construction.
This learning process through knowledge construction is also shaped by the social
activities that students engage in when playing the hotel simulation game, such as group
interaction. Such interaction was assessed through the items ‘The game required a high
degree of social interaction’, with the high result of 2.60, and “I worked efficiently with the
other group members when coordinating the activities and solving problems related to the
simulation game”, with the result of 2.83. According to the constructivist learning theory,
learning is an active, social process, where participants make meanings through the
interactions with each other (Tao, Y. et al., 2009). As the presented result is positive, it
supports that knowledge has been constructed in a social context.
Similarly to the social activities, students’ engagement in the course itself, compared
with regular teaching without simulation games, resulted 2.63 in the assessment of the item “I
used simulation games because I expected they would involve me more in the course
compared to a regular teaching”. Other mechanisms that positively can have influenced
student’s engagement is that the majority of the students ‘somewhat agree’ that they had fun
while playing the simulation game with the group. The result was 2.93 with a standard
deviation of 1.62. Having fun is a parameter that is rarely seen in education, despite the fact
that fun is beneficial and seems to promote learning, by activating the brain to be in a
positively emotional state – a state that more easily allows learning to happen (Washington
Post, 2010). However, the challenge lies in finding the right balance between fun and
engagement with instructional content and learning (Ash, K., 2011).
Further, the constructivist process supports construction of knowledge through
contextual learning. Knowledge is context dependent in the sense that learning has to occur in
a context where it is relevant (Duffy & Cunningham, 1996). Context is reflected in the
realism of the game and its constraints and limitations, evidenced through game feedback –
which is based on rules and roles built within the game. Such context affects students’
perceived quality of simulation games.
The realistic effects of the game were measured with the items “The simulation game
realistic recreates the hotel management environment” with a result of 2.77 and “The
simulation game covers the essential elements in managing a hotel within a competitive
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environment” with a result of 2.53. These realistic settings are confirmed to strengthen the
learning process, as the results fall between ‘agree’ and ‘somewhat agree’. Overall, these
findings support the predictions of the constructivist learning theory.
5.2.2.3. Associations with QAIT model
The QAIT model outlines that four elements – Quality of instruction, Appropriate
levels of instruction, Incentive and Time – must be simultaneously adequate for instruction to
be effective. First, the Quality of instruction and the Appropriate levels of instruction were
assessed in the items “The instruction provided by the lecturer, on how to play the simulation
game, was satisfying” with the result of 2.70 and a small standard deviation of 1.18 and the
item “Playing the simulation game demanded more than I usually expect from a course” with
a result of 3.17 and a standard deviation of 1.80. To give the possibility for a more in depth
answer, an open question was asked about the quality of instruction. The following
comments were some of the answers (complete answers in Appendix D):
“The instructor could have come up with better instructions in the beginning”
“More specific feedback on what was good and bad would had been nice”
“Since the course was focused on Strategy and Leadership, the element covered during
instruction and debriefing were relevant”
“More feedback on an individual/group level would have been preferred”
In regards to the Incentive element, the results show a positive indication towards the
degree to which the teacher made sure that the students were motivated. The instructor
created the incentive to participate in the course by including the simulation game as part of
the curriculum. The incentive element was assessed in various items, one being “Playing the
simulation game raised my interest for the Leadership and Strategy course” with a result of
2.50, just between ‘agree’ and ‘somewhat agree’, with a standard deviation of 1.33. Another
item was “The simulation game provides an exciting alternative to a regular course” with a
result of 1.93 and a standard deviation of 1.28. Both results, and especially the last one, show
that the game was a strong method of providing an incentive for academic learning.
Time is the last element relating to the effectiveness of the instruction. It was covered
in the item “The number of simulation rounds (12 rounds) was reasonable to cover all the
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aspects of simulating hotel management” with the result, 3.50, between ‘somewhat agree’
and ‘neither nor’, and in the item “The debriefing time was sufficient to review and analyze
the results of each simulation round” with the result, 3.10, close to ‘somewhat agree’. Both
results indicate that the time aspect can be improved, meaning that more time would allow
participants to have a better experience, and potentially a more effective instruction and
learning.
Level of participation refers also to the time the students have used in the simulation
game, but more directly relating to their participation. It was measured in the item “How
many of the twelve simulation rounds were you part of?”. The results show that 55.9 % were
part of all rounds and 29.4 % were part of between 8-11 rounds, giving a cumulative
percentage of being part of either all or most rounds in the game of 85.3 % of all respondents.
Another item relating to level of participation is “Average number of hours spent per
simulation round”. 1.5 hours per simulation round was the answer with highest frequency,
being one third of all answers.
Level of acceptance and level of satisfaction are the last two process variables of the
process domain. They emphasize, respectively, the students’ perception of relevance and
willingness to play the simulation game over and over again, and an overall likeness
assessment in regards to the hotel game simulation. Both variables have the potential to
impact how actively and motivated the student engages in game play, and therefore how they
accept it and like it as a learning tool. Ultimately, this leads to learning.
Even though not explicitly accounted within the QAIT model, these variables
represent the intrinsic counterpart to the extrinsic Incentives, and as motivational, they inform
the time/level of participation in the game. They can be viewed as enablers for the preceding
outcomes, in the sense that they inform how ‘open’ to learning the student is (Hense et al.,
2009).
The level of acceptance is illustrated in the item “I will recommend this simulation
module for others” and the level of satisfaction is evaluated in the item “Overall I was
satisfied with the learning experience”. As seen in the results through the histograms below,
the students’ (i.e. sample group’s) acceptance and satisfaction is more positive towards
simulation games than if you compare with a normal distribution curve. This means that both
level of acceptance and level of satisfaction tend to enable learning.
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Overall, the QAIT model delivers theoretical explanations for the instruction
supported by the results, which confirm effective learning. Students were presented with a
game that made sense and was interesting to them, they were able to play and see value in it,
they felt motivated and engaged to participate, and the time they allocated to play it seemed
appropriated, thus facilitating learning (or effective instruction) to take place.
5.2.3. Debriefing
Debriefing links the process to the learning outcomes, through a review and analysis
of the way the game unfolded throughout the different rounds and the underlying rationale for
such unfolding. In this regard, it bridges theory and practice, by linking the events that took
place in the game to underlying theoretical explanations, consequently converting game
events into learning experiences.
Looking at the results for the debriefing items demonstrate a positive learning
experience: “It gave me the chance to speak out and be part of the decision-making process
more than in a normal lecture environment” got a result of 2.93 and “The debriefing time was
sufficient to review and analyze the results of each simulation round” got the result, 3.10.
Beyond the numbers, the debriefing was voluntarily commented in the official CBS
Evaluation (2011) with the words, “The evaluation of each round in the hotel game during
the lessons was very constructive”, though some stated that “It would be nice with more
specific feedback, as we didn’t really know what was good or bad”, “The simulation game
toke too much of the time in the lectures”, “Takes too much time in class and outside class”,
“I’d would had preferred a more theoretical approach, than practical discussion”, and “In
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class, I’d expect to talk more about the game, running a hotel, etc. more than we actually
did”.
Although with room for improvement, the importance of debriefing cannot be
neglected. Perhaps in this case, it can be concluded that not enough time was allocated to the
debriefing part. The average quantitative responses showed a somewhat positive result, but
the qualitative comments indicate some constraints in how the debriefing stage was
experienced, thus demonstrating that time was not the only relevant factor. Perhaps another
reason is due to the fact that for most students this was the first time playing simulation games
and therefore their expectations regarding this learning tool was not properly set.
It is important to explain to students why debriefing cannot be excluded from the
lecture classes, as it is an important phase of the learning process. Stainton, A. J., Johnson, J.
E., & Borodzicz, E. P., (2010, p. 6 of 20) is describing it this way: “it is apparent that
effective learning requires sufficient time to reflect on engaging experiences so that learning
can be constructed”. Students should understand that deeper learning is occurring exactly in
the debriefing stage, as it provides a period of time where rich and careful observations can be
reflected, and perhaps learning gains can be shared with their fellow students, which all
together provide a study environment that is conducive to learning (Stainton et al., 2010;
Austin, R., 2011).
5.2.4. Output Domain
5.2.4.1. Associations with CBS’ Learning Strategy
Comparing and identifying similarities between Bloom’s taxonomy of learning and
the official CBS learning strategy is relevant before moving forward, in order to justify the
use of Bloom’s Taxonomy as reference point for the analysis of the output domain for hotel
simulation games employed by CBS. By understanding this relationship and evaluating the
HotelSim outcomes based on Bloom’s taxonomy, the learning potential of hotel simulation
games employed by CBS can be uncovered. CBS Learning Lab and its governing board have
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worked out the learning strategy of CBS. It consists of three fundamental components,
discussed as follows (CBS, 20056):
(1) CBS’s learning philosophy: is the learning process where the student is learning through
the construction and maintenance of meaningful contexts. Acquiring new knowledge, skills
and competencies applicable for solving future challenges is one of the five statements
constituting the learning philosophy.
In more detail, knowledge in what a student possesses that enables the student to apply
suitable information to a specific situation. In the process of developing knowledge, CBS’
role is to assist in providing an environment where the student can apply theories, methods or
practices learnt to a specific situation. This is similar to the lower levels of Bloom’s
taxonomy: remembering, understanding and applying.
In terms of skills, the idea is to facilitate study activities where the students can use
their knowledge to carry out a task or activity to solve a problem. Solving a problem requires
both knowledge and experience and training. Skills to be used in the analytical processes are
vital, such as being able to apply hypothetical models and methods to problem solving. This
is similar to the middle levels of Bloom’s taxonomy: applying and analyzing.
Competencies are when a student is able to combine knowledge and skills and apply
and evaluate them competently in a study situation or in other contexts, to create solutions,
take decisions and provide recommendations. Here study situation refers to situations that
fulfill academic standards for higher education, whereas other contexts refer to situations that
reflect what can be expected in the professional industry. This is similar to the higher levels of
Bloom’s taxonomy: evaluating and creating.
(2) CBS’s formation ideal: is the second component of CBS’ learning strategy. The aim with
this component is to explicitly indicate the academic and social profile and aspects, which
CBS desires that students develop during their study period. Examples of those competencies
are (CBS, 2005, p. 6 of 14):
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
6!A!new!learning!strategy!for!CBS!‘Business!in!Society’!is!currently!under!progress,!but!as!long!as!the!work!hasn’t!been!finalized,!the!learning!strategy!in!force!is!still!the!one!from!2005,!which!has!been!employed!for!the!purpose!of!the!comparison.!!
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“Educate graduates characterized by being employable, academically reflective and
by being capable of acting competently in different situations and contexts.”
“Academic goals are achieved by combining a diversity of study methods that
integrate research rooting with the inclusion of practice and a high level of problem
orientation, innovation and action orientation.”
“CBS graduates must be able to navigate in the global knowledge society which
entails that they are able to identify problems, structure, analyze, synthesize and
reflect critically as well having the ability to work constructively with information and
communication technologies.”
(3) Principles of study and study activity design: is the third and last component of CBS’
learning strategy. Below are selected principles (CBS, 2005, p. 8 of 14) that are overlapping
with what simulation games deliver, thus bridging the learning strategy of CBS (higher
academic school) and the emerging learning tool (simulation games). The potential of this
application (i.e. employment of simulation games by CBS) is discussed in the Chapter 6 –
Conclusion.
“The degree programs are of practical relevance and are problem oriented.”
“A mixture of varied, activating study elements is applied, with consideration for
differences in course levels, learning methods and methodology.”
“Information and communication technologies (ICT) are applied where they boost the
students’ learning processes (…).”
In sum, all three fundamental components are in alignment with Bloom’s taxonomy of
learning and with what simulation games can deliver, thus supporting and justifying the use of
Bloom’s Taxonomy as theoretical reference point for assessing learning outcomes derived
from hotel game simulations.
5.2.4.2. Outcome Data and Results: Official CBS course evaluation 2010 vs. 2011
To examine the effectiveness of HotelSim, a comparison of the teacher’s evaluation
report, for the Leadership and Strategy course, of the 2010 and 2011 classes is considered,
once HotelSim was incorporated into the curriculum for the 2011 class. By comparing an
evaluation that takes into account the use of HotelSim as a learning tool with one that doesn’t
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(i.e. control group), provides interesting insights into the different learning experiences of
students and effectiveness of learning tools employed. Both reports are found in Appendix E
and F, respectively.
A comparison of the reports demonstrates that the percentage of total replies in 2010
was 31.7 % while being 34.4 % in 2011. The course objectives, the lecturer and the course
description were the same in both years, so the main difference in evaluation can be assumed
to stem from the students’ perceptions and experiences in regards to HotelSim. When filling
out the official CBS course evaluation questionnaire, the students were unaware at the time
that their answers would be used for further analysis of the course and its applied learning
tools. This eliminates to great extent all biases in their answers towards their opinions about
the hotel simulation game.
The evaluation report assessed the perception of the course through the statement:
“The overall impression of the course is positive”. The result was 3.4 out of 5 in 2010 and 3.9
out of 5 in 20117, with 1 being ‘completely disagree’ and 5 being ‘completely agree’. The
teacher’s performance measured by “The teacher made the course relevant” was 3.8 out of 5
in 2010 and 4.5 out of 5 in 2011. The qualitative answers show a similar positive indication
towards the course in 2011, through a more in-depth appraisal. The students were asked to list
the 1-2 best aspects of the course. Here is a summary of their responses:
2010 2011
The 18 answers covered in general
aspects like: good guest lecturers, a good
teacher (in terms of connecting theory
and practice, easy to understand,
engaged, motivated, etc.), and an
interesting subject.
Out of 19 answers, 17 students answered
that the best thing in the course was the
HotelSim simulation game. The
remaining two answers were relating to
positive attributes of the teacher and the
guest lecturers.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
7!The!presented!results!in!this!section!refer!to!the!arithmetic!average!of!the!total!responses!based!on!a!5Xpoint! Likert! scale! (unless! otherwise! stated).! This! means! that! individual! responses! range! from! (1)!completely!disagree! to!(5)!completely!agree,!and! that! the!average!(i.e.! result)! is!a!number!somewhat! in!this! interval.!The! closer! the! results! are! to!5,! the!more!positive! is! the! result! in! regards! to! the! statement!under! evaluation.! Results! lower! than! 3! are! considered! negative,! whereas! results! higher! than! 3! are!considered!positive.!!!
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The students were also asked to provide 1-2 aspects they would like to improve.
Below are examples of their comments:
2010 2011
- More challenging readings
- More focus on service
- More cases from the tourism industry
- More exercises would help on my
understanding of the subject
- The concepts discussed were just put on
the board and it was hardly practical
- Two answers related to the use of too
much time on HotelSim in detriment of
other parts of the course
- Other answers were relating to the
amount of reading, choice of groups, etc.
5.2.4.3. Outcome Data and Results: grade performance 2010 vs. 2011
Below is a comparison of the total average grade of the students attending the
Leadership and Strategy course both in 2010 and 2011.
2010 2011
- 46 students attended the exam
- Total grade average was: 6.74
- 54 students attended the exam
- Total Grade average was: 7.81.
- 34 students attended the exam
questionnaire and completed the
questionnaire.
- Total grade average was: 8.21.
(Bobek, K., 2011)
As noted in the table, students that attended the 2011 class, where HotelSim was
integrated as part of the learning tools of the course, obtained a significantly higher average
grade than the students from the 2010 class – and this difference is particularly high when
comparing to the students who responded the questionnaire, and therefore are part of the
sample group for the effects of the research carried out in this thesis. This is a strong
indication of the positive effects of HotelSim as learning tool.
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The students who didn’t respond the questionnaire in 2011 cover both those students
who participated in the HotelSim but didn’t respond the questionnaire for any reason, and
those who didn’t participate in the HotelSim altogether. The difference in these two average
grades (i.e. 7.81 and 8.21) is a further indication of the effectiveness of HotelSim as learning
tool.
5.2.4.4. Cognitive Domain
The findings from the previous section, where the grade average increased from 6.74
in the 2010 class to 8.21 in the 2011, show clearly that hotel simulation games, illustrated
with the case of HotelSim, hold educational merit. The following figure illustrates how the
cognitive process relates to the course and the HotelSim simulation game.
Knowledge
Domain
Cognitive process
Remember Understand Apply Analyze Evaluate Create
Instructional
Activities
Previous
coursework
Lecture
Simulation
Game
Lecture
Simulation
Game
Simulation
Game
Simulation
Game
Simulation
game &
Exam
Report
Simulation
Game
Exam
Report
& Oral
Exam
As shown, cognitive learning occurs in all levels of the cognitive domain during the
time of the course and the game itself. The first stage in the cognitive domain reflects that
students must be able to Remember knowledge from previous coursework and lectures, which
is a pre-requisite to be able to follow and understand the following lectures and related
information and knowledge taught. In this way, students are able to understand and later
apply this accumulated knowledge for game playing. Also, information and knowledge
related to the simulation game itself, such as rules and roles, the interaction process built
within the game, and etc., must be remembered when playing the game (HotelSim) and
writing the exam report.
Once they remember, students must be able to Understand the lectures they attend to
and the related curriculum, as well as the guidelines and instructions for the game.
Understanding involves being able to comprehend and give meaning to what is taught, and is
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quite important to enable them to write the exam report (e.g. students must be able to
understand the principles of HotelSim).
Further, HotelSim is organized around a lot of information about the marketplace the
hotel is located in, the competitors, the customers, etc., which students must digest. Students
also have the option to purchase access to additional market reports with forecasts and other
data sets, for the purpose of providing them with as much information as in the real world to
base their decisions upon. Below is a screenshot of all the material they have access to and
which they can work as an outset for their decisions.
Figure 12: Overview of available material in HotelSim requiring Cognitive ‘Understanding’
The stage of understanding in the cognitive process can be assessed through the
question: “It increased my understanding of the factors that contribute to operating a
successful hotel”. The result was a positive high 2.038 with a low standard deviation of 0.96,
indicating that the sample group to a larger extent agrees that HotelSim highly contributes in
their understanding of hotel management.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
8!The!presented!results!in!this!section!refer!to!the!arithmetic!average!of!the!total!responses!based!on!a!7Xpoint!Likert!scale!(unless!otherwise!stated).!This!means!that!individual!responses!range!from!(1)!strongly!agree!to!(7)!strongly!disagree,!and!that!the!average!(i.e.!result)!is!a!number!somewhat!in!this!interval.!The!closer! the!results!are! to!1,! the!more!positive! is! the!result! in! regards! to! the!statement!under!evaluation.!Results!lower!than!4!are!considered!positive,!whereas!results!higher!than!4!are!considered!negative.!Refer!to!Chapter!9.1.!X!List!of!Tables:!Full!Results,!tables!7X12!for!more!detailed!results.!!
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The third stage in the cognitive process is when the students must Apply the
knowledge and information they remember and understood to be able to make appropriate
decisions in the game. Such knowledge and information refers to the previous coursework,
the lecture, the material and instructions of the game as well as knowledge acquired from
playing the game. Thereby, they move to a higher level on the taxonomy, deepening their
degree of learning. Applying knowledge basically demonstrates the student’s ability to put
theory into practice.
The below screenshot from HotelSim (Figure 13) illustrates this application stage, by
showing the price decisions students must take with impact in the next round in the game. In
order to take better decisions than their competitors, students must remember and understand,
for example, pricing theory and strategy implementation as well as supplementary
information available in the game.
According to Austin (2011), it is particularly at this point (i.e. beginning of the game)
that students are applying theories and undertaking theoretical considerations in regards to the
game, in order to inform and guide their game strategy. Based on his long experience working
with simulation games, Austin (2011) has noticed that in the later rounds of the games,
students place less emphasis on theoretical considerations and base their decisions more on
the experiences derived from game playing in a more tactical and reactive approach, adjusting
their strategy based on their own conception of how the game is best played. This decision-
making process is very similar to how a person acts in the real world.
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Figure 13: Example of the Cognitive stage of ‘Applying’
The third stage in the cognitive domain, apply, was assessed in the questionnaire with
the question: “The simulation game enabled me to apply relevant theories and models from
the curriculum to a practical scenario”. The mean result was 3.73, a result between
‘somewhat agree’ and ‘neither nor’, and slightly in favor of fulfilling the objective that
simulation games facilitate an environment where student’s can put theory to practice.
Moving to the fourth stage of the cognitive process, students must be able to Analyze
in detail the constraints or interrelations in a situation or problem during the game and
determine how to solve them, through critical assessment of the possibilities available and
meaningful consideration of assumptions and alternatives. The previous three cognitive
process’s stages should assist in informing the student in this task.
Taking the outset on the below screenshot as example, the hotel below has $35.500
available in funds for facilities investment. They have already invested $250.000 in a business
center and $6.000 per month in a valet parking, leaving the participants with the choice of
investing the remaining money in either a lobby kiosk or a travel & tour desk, or to save the
money for future investments in a later simulation round, where additional funds might have
been collected as a result of how the game has been played (i.e. previous decision rounds
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outcomes). The participants here must analyze this situation and take the best course of action
based on such analyzes.
Figure 14: Example of HotelSim data requiring Cognitive ‘Analyzing’ Processes
The analyze stage was assessed with the question: “The simulation game was a good
tool to test my analytical skills”. The result was 2.36, confirming that simulation games
provide an ideal learning environment for students to analyze and examine in detail a specific
matter, and further enhance their analytical thinking.
The above example is merely the point of the iceberg. Students playing simulation
games must consider all the functions and their respective interrelationships for the specific
hotel and analyze the best course of action considering the dynamics of such
interrelationships for effective performance (Ben-Zvi, T., 2008), besides the interaction of the
specific hotel within the marketplace where the five hotels operate and compete. Part of their
choice involves agreeing on the positioning for their hotel and its possible point of
differentiation in regards to competitors, and taking decisions accordingly; where other parts
involve responding to the results elicit through game playing and subsequently analyzing and
evaluating the balance scorecard after each round to inform the next round’s approach and
tactics.
Once students analyze the dynamics and results of single game rounds, they must then
Evaluate the results to assess the new course of action for subsequent rounds, as touched
upon previously. Evaluating concerns student’s ability to assess and evaluate their decisions
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and results and come up with proper solutions or recommendations for next simulation
rounds. The results compromising an individual hotel performance in the game is found in the
below balance scorecard.
Figure 15: Example of HotelSim data requiring Cognitive ‘Evaluation’
The different groups have to analyze and evaluate their results based on their learnings
from the precedent stages and use this knowledge for evaluating future decisions and for the
creation of the exam report. This stage was measured with the question: “The strategic
decisions taken by my group had the forecasted effects in the simulation game”, with an
average response of 2.60 between ‘agree’ and ‘somewhat agree’ and a low standard deviation
of 0.97, thus demonstrating that the students’ ability to evaluate had the expected effects,
which indicates that the evaluate stage has been accomplished as a result of participation in
simulation games, and that learning has thus occurred.
Lastly, the students have to Create the exam report and attend an oral defense of the
project. Writing the exam report requires students to review the information and knowledge
derived from the previous cognitive stages and make judgments based on the experiences
they’ve attained.
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Since they need to assess and judge the performance of a hotel which they were not
part of the management team, the experiences and knowledge they obtained through game
playing should assist them creating this report (i.e. an original piece of knowledge). This was
assessed through the item “The simulation game will help me in preparing myself for the final
exam (i.e. project writing and oral exam)” with the positive result of 3.47. If all the previous
stages are successfully fulfilled, then the students should have the cognitive abilities to master
this creating stage, culminating with a high performance at the exam.
To conclude, the stages in the cognitive domain of Bloom’s taxonomy have been
accomplished as a result of participation in HotelSim, thus implying that hotel simulation
games are effective learning tools in higher academic education at least with respect to
cognitive knowledge. However, it must be noted that to enable knowledge creation through
simulation games, other learning tool must be present to support such process. Simulation
games should not stand alone as the single educational tool employed in higher educations,
but instead should be accompanied by and integrated with regular lectures, books, articles,
previous knowledge, etc. to be able to fully capitalize on its potential.
5.2.4.5. Affective Domain
The figure below illustrates how the affective process relates to the course and the
HotelSim simulation game.
Affective
Domain
Attitudinal process
Receive Respond Value Organize Characterize
Instructional
Activities
Lecture &
Simulation
Game
Lecture &
Simulation
Game
Simulation
Game
Simulation
Game
Simulation Game
The affective domain reflects students’ attitudes towards learning, in terms of
emotions, feelings and beliefs, towards both the learning process and content. Students’
affective reaction to and their perception of learning influence if and how learning occurs.
The analysis of the data is generally positive on all matters.
According to the flow of the process, students must be willing to Receive stimuli to be able to
learn. This reflects an openness state to experience the simulation game and a willingness to
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hear and act on what is being presented. To a great extent, this is indicated through students’
motivation to engage in HotelSim, which has been previously analyzed in Chapter 5.21. –
Input Domain, under motivation. Therefore this analysis is not necessary to be carried out
again here.
After they receive the stimuli, students must Respond to what they have received. This
corresponds to their active participation in the specific pedagogy (i.e. HotelSim). This has
also been previously analyzed prior in this thesis, in Chapter 5.1.2. – Process Domain, under
level of participation, and therefore is not necessary to be touched upon again here.
Students must also Value the simulation game to accept it and be committed to participate,
which is a consequence of their perceived value of the game. This attachment of value that
leads to engagement and participation is measured through the items “Playing the simulation
game raised my interest for the Leadership and Strategy course” and “Playing the simulation
game raised my interest for the Leadership and Strategy disciplines in general”, which
received, respectively, results of 2.50 and 2.93.
Valuing the game induce students to actually Organize the perceived learning within
their own value system. This means that by recognizing the implicit value in playing the
game, students tend to accommodate and reconcile the learning derived from game playing
with their personal views, stands and beliefs. Many items combined measured this stage, with
the most interesting being: “It gave me the change to speak and be part of the decision-
making process more than in a normal lecture” with a result of 2.93; “Teamwork skills were
an important element in performing well in the game” with a result of 2.33; and “I’d rather
learn through the case-base methods than simulation games” resulted in the average score of
4.40. Note that this question was reversed, so it demonstrates that students favor simulation
games when compared to case-base learning.
In the last stage of the affective domain, students must be able to Characterize,
meaning that these values incorporated into their own value system are then acted upon. Put
differently, these taught and accepted values become the behavior of the students, in the sense
that the hotel simulation game facilitated a consistent change in attitudinal characteristics of
the students’ behavior. As this stage deals with behavioral attitudes in the future, it can only
be assumed that this change is likely to happen, once all the previous affective stages have
been fulfilled and the game facilitated characterization. Thus, it cannot be empirically
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confirmed due to the time limitations of this study, being a subject for future investigation.
However, indications of its attainment include the items: “I would like to play HotelSim again
in an education environment” and “I would like to play HotelSim again in a professional
environment”, which got results, respectively, of 2.33 and 2.40. Lastly, the item: “I would
recommend this simulation module for others”, with a result of 2.07, further supports
students’ perceived value and behavioral change resulting from playing HotelSim.
Facilitating all stages of the affective domain of learning, hotel simulation games, as
illustrated by HotelSim, facilitate attitudinal learning. When combined with the findings from
the cognitive domain results and the grade performance evaluation (i.e. two previous sections
of this thesis), hotel simulation games can be viewed as effective learning tools for higher
academic performance within service and hospitality.
5.3. Interview with Robert Austin: Insights from a Harvard Professor The insights from Harvard Professor Robert Austin (Interview, April 12, 2011) are
used throughout the thesis whenever appropriate. The other relevant recommendations and
qualitative insights are gathered here to shed further light into the employment of simulation
games as learning tools, and their respective effectiveness.
According to Austin (2011), simulation games are definitely an effective tool with a
number of merits, as they: (1) foster engaged and involved students; (2) challenge students
with unexpected – but still realistic – situations, by facilitating a learning environment that
focuses on double-loop learning, which means that experiences encountered and learnings
gained in a simulation round should be included in the proceeding rounds for successful
performance and learning; (3) complicate things more than the text-book version, in order to
facilitate deeper learning, where deeper learning refers to students being able to identify rich
and careful observations, and reflect about their experiences; (4) motivates students to a
greater extent towards the course, as the game is often more fun than other teaching methods,
which leads to increased participation level through tacit learning; and (5) push students, in an
interactive way, to reach better conclusions in the discussions and to make sense of the
actions in the context where the game takes place.
Despite these promises, Robert Austin (2011) emphasizes that simulation games
shouldn’t be used as a stand alone teaching method, but instead should be combined with
traditional teaching methods and materials, such as case-studies, books, lectures, etc. He also
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stressed that simulation games require sufficient time for playing in order to evoke efficient
learning outcomes.
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6. Conclusion In this chapter, the findings from the research are analyzed under the optic of the research
questions and sub-questions. The validation of the designed logic model for hotel simulation
games is assessed and possible justifications delineated. The chapter concludes with the
appraisal of the validation of the research question of this thesis.
The purpose of this thesis is to evaluate the effectiveness of hotel simulation games on
higher academic performance within service and hospitality. Evidencing that hotel simulation
games are effective on higher academic performance involves assessing if hotel simulation
games facilitate and induce learning. Linking hotel simulation games to learning to
effectiveness has thus been central to this thesis.
Inferences about the effectiveness of hotel simulation games, illustrated through the
HotelSim case study, thus addressed the main research question of this thesis, as well as the
three sub-questions.
The research findings discussed in the previous chapter support that hotel simulation
games are an effective learning tool for higher academic purposes, by validating that both
cognitive and affective learning outcomes stemmed from hotel simulation game play.
ARE! SIMULATION! GAMES! AN! EFFECTIVE! LEARNING! TOOL! FOR! HIGHER!
ACADEMIC!PURPOSES?!!
Which!factors!and!processes!play!a!
role!in!facilitating!learning!within!
the!context!of!simulation!games?!
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The analysis partially supported the designed logic model for hotel simulation games,
which outlined the expected factors and processes underlying learning through hotel
simulation games.
In respect to the input variables related to student’s characteristics, age, cognitive
level, bachelor degree and professional experience, don’t seem to impact learning from hotel
simulation games. The results are inconclusive regarding previous knowledge in simulation
games, since only one student has tried it before. Motivation and gender, on the other hand,
seem to positively affect learning.
In regards to teacher’s characteristics, their quantitative impact on learning outcomes
is not possible to be assessed, since the research evaluated the implementation of the
HotelSim by only one teacher. However, they are expected to be positive, especially the
variable experience, since experience is vital to inform how students experience the game and
learn from it through guidance and debriefing. Teachers should also consider allowing more
time in the preparation and execution phases of the game, as the more technically- and
student-centered aspects of the game require more effort than regular teaching does.
In relation to the game characteristics’ input variables, content quality and game
quality affected positively the student’s learning experience, by enabling them to learn
through the application of theory and knowledge to practice. The impact of IT challenges is
however inconclusive, as only one student faced difficulties. On the other hand, this can be an
indication that IT challenges are not really an issue for hotel simulation games such as
HotelSim.
In respect to the process variables, all of them seem to positively impact learning from
playing hotel simulation games. The underlying processes taking place upon game playing
also facilitate and moderate this learning process. As stated by Albert Einstein in the opening
quote of this thesis, “Learning is experience. Everything else is just information”.
Experiencing is the key to learning, because knowledge is transformed and created trough
experience. And experience is exactly what simulation games enable.
The debriefing process is especially relevant in this regard, as the research findings
demonstrate. Even some of the negative evaluations of this phase evidence that students
would like the debriefing section to be better and longer, supporting the relevance of this
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What!are!the!main!learning!
outcomes!derived!from!
participating!in!game!
simulations?!
To!what!extent!are!simulation!
games!effective!in!facilitating!
learning?!
process for learning. It is during this phase that a review and analysis of the way the game
unfolded and the underlying rationale is provided, thus presenting the last key piece to the
game puzzle.
Despite the non-statistical or conclusive support of some of the conceptualized factors,
the justification of such may rely more on fundamental reasons than the non-existence of the
conceptualized relationships.
Such reasons can include: (1) the great number of variables included in the model; (2)
research shortcomings, such as: the small sample size, the lack of longitudinal research, the
difficulty in isolating the direct relationship between some of the input and output variables
(without noise of other variables); and (3) the lack of more in-depth statistical empirical
testing, which focused on simple statistical inference, assumptions and interpretations. Future
researches can tap into these shortcomings to attempt to validate the designed model for hotel
game simulation.
Finally, the last variables of this model refer to the outcome domain, assessed by the
two remaining sub-research questions.
Learning, as a result of playing hotel simulation games, can be evidenced through
cognitive and affective learning outcomes. When learning has occurred, intellectual or
attitudinal performance is positively impacted as a result of participating in game simulation.
By fulfilling at least one of these two learning outcomes, hotel game simulations are
considered effective learning tools.
As discussed throughout the thesis, each of these two domains has different stages of
learning, implying that learning can be assimilated in different degrees. Therefore, hotel
simulation games have different levels of effectiveness, depending if one or both learning
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outcomes are fulfilled, and on the degree of fulfillment within each learning outcome. The
more learning outcomes fulfilled and the more stages within one learning outcome fulfilled,
the more effective hotel simulation games are in respect to learning.
In terms of cognitive learning, both the students’ grade average increased significantly
from 2010 to 2011, where the simulation game was incorporated into the Strategy &
Leadership course curriculum, as well as all the cognitive stages of Bloom’s taxonomy of
learning were attained as a result of game play. This evidences that learning occurred to the
maximum extent possible regarding cognitive outcomes.
In regards to affective learning, hotel simulation games also facilitated all stages of
attitudinal learning, except the last one, characterization, which could not be evaluated due to
the required time frame for supporting behavioral changes. However, the game facilitated
characterization and indications of its attainment support the likelihood of this last stage
being also achieved. Despite of that, the degree of fulfillment within affective learning was
close to maximum, if not maximum, demonstrating strong positive attitudinal learning.
Overall, the research findings support that hotel simulation games are an extremely
effective learning tool for higher academic performance within service and hospitality.
Therefore its incorporation into academic curriculums is recommended, as long as considered
in conjunction with other supporting learning methods. Where hotel simulation games are
practical learning tool for the course, lectures and supporting literature provide the theoretical
foundation for learning, and underlying beacon for simulation game playing. Together, these
methods have the potential to boost students’ learning experiences to their maximum.
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7. Implications In this chapter the implication of the research findings are presented and discussed, leading
to recommendation for the future.
The uncovered results in this thesis provide great insight into the applicability and
usefulness of hotel simulation games for higher educational purposes. As touched upon in the
previous chapter, hotel simulation games are highly effective as learning tools when
combined with other appropriate teaching methods. Carefully considering the course
curriculum when integrating hotel simulation games is thus paramount to enable learning.
Further, simulation games require greater time than regular teaching for its effects to
be positive on learning. As mentioned by Robert Austin (2011), if the allocated time for the
game is not appropriate, then learning is unlikely to occur, at least to the same extent.
Additional time need to be allocated in class for the game and the curriculum must be re-
structured to allow this new teaching tool to take up enough time in the class. Therefore, the
set-up of the curriculum needs to account for the trade-off between theoretical foundation and
practical experience, represented, respectively, by more traditional teaching methods and
hotel simulation games.
This is particularly relevant at higher academic institutions in Denmark, where it is the
students’ own responsibility to assign the amount of time they want to spend preparing for
lectures and exams, and on attending classes (CBS, 2010). Unfortunately, this leaves the
students with the possibility to not participate in the hotel simulation game. In the respect,
assessing the above trade-off gets even more importance when defining the course
curriculum.
Additionally, as a result of this ‘no compulsory’ approach to education, intrinsic
motivation plays an even more crucial part on the student’s acceptance and engagement in
participation. And as long as performance at the exam is their primary extrinsic motivation for
learning, the institution of incentives for participation in the game simulation should be
incorporated to the course. An option can be to integrate the completion of and performance
on the simulation game as a graded evaluation, which is part of their final grade. Otherwise,
as long as the exam grading is solely determined on students’ performance at the exam, and
not on the process towards the exam, it will be hard to fully capitalize on the potential hotel
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simulation games hold for learning. Accounting for these motivations, incentives and their
influence on involvement and performance are still to be considered in the preparation and
planning process for future application of hotel games simulations.
As discussed throughout the thesis, simulation gaming is another way of acquiring
new knowledge and learning experiences, which can however be perceived as a challenge for
established educational institutions. Fortunately, governmental institutions like the Danish
Ministry of Science, Technology and Innovation (2007), which have perceived the
educational value in this learning tool, are endorsing the incorporation of this practice, thus
reducing uncertainty and doubts regarding its benefits for education.
For instance, the national strategy of the Danish Ministry of Science, Technology and
Innovation is to focus on: “Inspiring and motivating educators other than front runners to use
ICT-supported learning…” and “to ensure strategic focus on ICT-supported learning and
increasing the use of this at the higher education institutions”. Globally, institutions like the
Boston Consulting Group (2011) also supports that “Implementing a digital-friendly
curriculum is key” (p. 9 of 30). Now, it is simply up to the higher academic institutions
around the globe to embrace simulation games to contribute to the chance of the learning
paradigm looking forward.
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9. List of Figures and Tables VARIABLES QUESTIONS
Input Domain – Student’s Characteristics
Introduction Question 1
Gender Question 2
Age Question 3
Student’s cognitive level: PLI test Question 4
GPA Question 5
Name of hotel Question 6
Bachelor’s degree Question 7
Professional experience Question 8
Previous knowledge in simulation games Question 9
Motivation Question 10
Process Domain
Level of participation Question 11
Level of participation Question 12
Perceived quality of simulation game Question 13
Degree of involvement Question 14
Quality of instruction Question 14
Group interaction Question 16
Level of acceptance Question 17
Level of satisfaction Question 19
Outcome Domain
Learnings Question 15
Learnings Question 18
Table 1: Overview of the linkage between the Logic Model and Questionnaire
Gender
Valid Frequency Percent Valid Percent Cumulative Percent
Male 10 29.4 29.4 29.4
Female 24 70.6 70.6 100.0
Total 34 100.0 100.0
Table 2 - Frequency: Gender
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Age
Valid Frequency Percent Valid Percent Cumulative Percent
22 years old 1 2.9 2.9 2.9
23 1 2.9 2.9 5.9
24 6 17.6 17.6 23.5
25 8 23.5 23.5 47.1
26 1 2.9 2.9 50.0
27 5 14.7 14.7 64.7
28 1 2.9 2.9 67.6
29 1 2.9 2.9 70.6
30 2 5.9 5.9 76.5
31 2 5.9 5.9 82.4
34 3 8.8 8.8 91.2
36 1 2.9 2.9 94.1
39 1 2.9 2.9 97.1
41 1 2.9 2.9 100.0
Total 34 100.0 100.0
Table 3 - Frequency: Age
Cognitive Test – PLI Test
Valid Frequency Percent Valid Percent Cumulative Percent
10 1 2.9 4.3 4.3
13 1 2.9 4.3 8.7
16 1 2.9 4.3 13.0
19 1 2.9 4.3 17.4
22 2 5.9 8.7 26.1
25 1 2.9 4.3 30.4
26 4 11.8 17.4 47.8
28 4 11.8 17.4 65.2
29 1 2.9 4.3 69.6
31 1 2.9 4.3 73.9
32 2 5.9 8.7 82.6
33 1 2.9 4.3 87.0
34 1 2.9 4.3 91.3
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35 1 2.9 4.3 95.7
38 1 2.9 4.3 100.0
Total Valid 23 67.6 100.0
Missing 11 32.4
Total 34 100.0
Table 4 - Frequency: PLI test score
Table 5 - Motivation
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Table 6 - Item-Total Statistics: What are your social learnings from participation in the simulation game?
9.1. List of Tables: Full Results
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD
I used simulation games because they were part of the course curriculum 2.07 1.62 I used simulation games because I expected they would raise the course subject
2.67 1.69
I used simulation games because I expected they would involve me more in the course compared to a regular teaching
2.63 1.83
Table 5: Why did you use simulation games?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD The simulation game realistic recreates the hotel management environment
2.77 1.22
The simulation game covers the essential elements in managing a hotel within a competitive environment
2.53 0.97
Did the simulation game environment provide you with the possibility to apply relevant theoretical knowledge to the game
3.27 1.28
I view the simulation as a valid representation of reality to acquire applicable knowledge
3.03 1.54
I perceive the user-interface as easy to use (user-friendly) 2.80 1.49 The instruction provided by the lecturer, on how to play the simulation game, was satisfying
2.70 1.18
The debriefing time was sufficient to review and analyze the results of each simulation round
3.10 1.37
The number of simulation rounds (12 rounds) was reasonable to cover all the aspects of simulating hotel management
3.50 1.38
I attended class more frequently than usual because of the simulation game
4.40 1.54
The game required a high degree of involvement from me 2.33 1.37 Table 6: What are your overall perceptions regarding the simulation game?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD The strategic decisions taken by my group had the forecasted effects in 2.60 0.97
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the simulation game The simulation game enabled me to apply relevant theories and models from the curriculum to a practical scenario
3.73 1.48
Playing the simulation game demanded more effort than I usually expect from a course
3.17 1.80
Playing the simulation game engaged me more in the course than a regular teaching method does
2.63 1.40
The simulation game was a good tool to test my decision-making ability 2.33 1.24 The simulation game was a good tool to test my analytical skills 2.36 1.35 The simulation game provided me with knowledge that I can apply in managing hotels in real life
2.40 1.28
Table 7: What are your learnings from participating in the simulation game?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD I worked efficiently with the other group members when coordinating the activities and solving problems related to the simulation game
2.83 1.72
My group and I tackled the game challenges satisfactorily 3.00 1.70 I experienced that some in my group didn’t participated in the group work as expected
3.20 2.07
I had fun while playing the simulation game with my group 2.93 1.62 I experienced cross-cultural differences among my group members 3.80 1.69 Cross-cultural differences positively affected teamwork 3.60 1.40 I felt that it was relevant to have a nominated CEO to organize and lead the group during the simulation game
3.27 2.20
Teamwork skills were an important element in performing well in the simulation game
2.33 1.35
The game required a high degree of social interaction 2.60 1.25 I rather choose my own group members than the lecturer decides who I am in group with
3.30 1.82
HotelSim should be played as individually, instead of in groups 4.30 1.90 Overall I was satisfied with the group experience 3.13 1.76
Table 8: What are your social learnings from participating in the simulation game?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD HotelSim holds potential to be used throughout a whole degree program such as Service Management with focus on Hospitality
2.30 1.32
HotelSim holds potential to be included in other CBS graduate courses 2.53 1.28
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HotelSim holds potential to be included in CBS undergraduate courses 1.93 0.78 HotelSim holds potential to be included in CBS executive courses and continue education programs
2.40 1.07
Table 9: Is the simulation game a recommendable tool for future use?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD The simulation game increased my knowledge of strategic issues in service management
2.60 1.22
The simulation game increased my knowledge of general leadership 2.97 1.30 In increased my knowledge in managing a hotel at a corporate level 2.27 0.83 It increased my interest from the hospitality industry 2.67 1.21 It increased my understanding of the factors that contribute to operating a successful hotel
2.03 0.96
It increased my understanding of strategic decisions and its impacts 2.50 1.11 It gave me the chance to speak out and be part of the decision-making process more than in a normal lecture environment
2.93 1.72
Game simulation is a challenging learning environment 2.40 1.13 Game simulation offers me a dynamic learning experience 2.13 1.11
Table 10: How have you benefited from participating in the simulation game?
Valid cases N=30 (Listwise Delection) Item Statistics
Means (M) and Standard Deviation (SD) of key items M SD Playing the simulation game raised my interest for the Leadership and Strategy course
2.50 1.33
Playing the simulation game raised my interest for the Leadership and Strategy disciplines in general
2.93 1.55
I would like to play HotelSim again in an educational environment 2.33 1.58 I would like to play HotelSim again in a professional environment 2.40 1.13 I would will recommend this simulation module for others, e.g. next years SEM students
2.07 1.26
The simulation game provides an exciting alternative to a regular lecture course
1.93 1.28
I’d rather learn through case-base methods than simulation games 4.40 1.48 The simulation game will help me in preparing myself for the final exam (i.e. project writing and oral exam)
3.47 1.63
Overall I was satisfied with the learning experience 2.47 1.33 Table 11: How satisfied are you with the simulation game experience?
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10. Appendix
A – Interview with Robert Austin Transcript of the Interview with Robert Austin
Not transcribed: a brief introduction to the purpose of the thesis.
1. What is your background in higher educational teaching?
I have been a professor since 1997 and I taught until 2007 at Harvard Business School where
we taught everything by the case method, so a very interactive, very experiential. And I have
taught here at CBS since then, so lets say I have a lot of experience in teaching; I am
interested in teaching; I like to teach and think the students like my classes.
2. What are your experiences with business simulation games (professional and/or in educational environments)?
I have number of experiences with those. One of the things that we used to do at the early
stage at the MBA program at Harvard was to run a game that was called Crimson Readings.
A paper based simulations, no automation really. I participated as kind of a coach, and it
unfolded over four days. And then there was in the operations class that I taught, we used to a
manufacturing setup where we would actually make products using wires and diagrams and
so forth, actually, they are very simple products. But that was not the point. The point was to
do forecasting, and figuring out a manufacturing process, and so on. We also, at Harvard,
used to use something call the Beer Game. Which is a famous, John Sterman from MIT
invented I think. It is a supply chain management system game. We used that a lot. Used it
every year I taught the operations course. I am also the author of a simulation game that is
published by Harvard Business School Publishing on Project Management. So I have used
simulations quite a bit.
3. Do you think that simulation games can be an effective learning tool? If so, how?
Yes, I do think it is an effective tool. It got a number of merits. One is that it is very engaging
so students become very involved in their learning with simulations. To take the Beer Game
as an example, that is another level of learning that happens. You can talk about the bullwhip
effect in theory but you really don’t get it until it happens to you in the simulation. So you
read about it, you know what to do not to have that happen but you get a real appreciation for
the bull-whip effect when you actually have to work out; have to participate in a supply-chain
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simulation and the it cracks back on you and causes you great difficulties. One thing I like to
do with it, is that it goes over a number of rounds and the students never know what the last
round will be, that is part of the design simulation. In the early stages people are confident
when they are executing plans and things seem to go as they expect. You get to the middle
stages and actually I uses when I teach that I go around and I listen when they work in teams
of two and then you hear the conversations of the two of them; at first they are like: no
problem, we got this under control, everything looks great, and in the middle I often
experience the students saying “that is odd”, and when you get deeper into it, you people go
“oh my gosh, we are screwed, we are screwed”. I usually go around and take notes on what
they are saying, some of it is kind of funny, so when you do a debrief, and I think debriefing
is a very important part of the simulation, I don’t think you finish learning until you do the
debriefing.
When do you usually do the debriefing?
Usually the class afterwards, or it depends on how you schedule the setup, it could be right
after with session of simulations and a session of debriefings, but I think when we did it with
the Beer Game it was in the next class. And hardly that was because we needed some time to
crunch the numbers and have the graphs ready where were interesting lesson, where the best
examples are found, etc.
The students playing the game are they adjusting to how the game is reacting or are they
reacting using with that they have learnt in theory?
I think they do both. They always start out with some theory and it may not be in a formal
theory, it can be their kind personal theory where they have an idea of what is going to cause
what, and as they work through (pause)…, when I teach project management I start by laying
out what I called the text-book version, so the text-book version is the way it is suppose to
work in the ideal world, the way the perfect project would work. And then I spend the rest of
the course to complicate things in one way or another. I think that was simulation games do
that is nice is that they complicate things in a way that leads to deeper learning. So you learn
that the theory is useful but it is not complete and you have to apply it carefully.
But it should not be too complex either, right?
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That is right. But I mean it is often in project management designs to have simple scenarios,
and more challenging scenarios. One of the scenarios in project management game is
designed to be impossible and that because that is too because it is also part of project
management experiences, that there are projects where you will not succeed by the criteria
laid out for you. And then the challenge for the manager is how to make it as with a few
problems as possible, so make it a small disaster as possible versus a big disaster in that
project, and thereby making it a win.
Those impossible scenarios come with a lot of frustration as well?
Yes, but that is not the only scenario, in the Beer Game for example, what usually happens is
that the first couple of scenarios they get comfortable because they know how it works. But
then we start throwing curves at them so we say in the middle of the project of 12
sessions/rounds of play in the customer suddenly change their requirements on you; changes
what you want them to build. Well, that makes life harder, right? But it is pretty realistic. By
doing that we break away from the perfect world where every requirement is anticipated in
advance and so forth. We talk more about how to deal with less perfect situations and that is
pretty important too. It is harder to theorize about and often you can’t theorize about it,
further, there are readings I have assigned to talk about chaotic project environments.
4. In your opinion, what are the immediate benefits/effects of such games? Can you characterize those?
I think you can characterize them in terms of deeper understanding that is what I would look
for. I would look for that in the debriefing whether they are making rich and careful
observations about what they have seen and experienced. If you show a graph that somebody
did and you ask why did that happen? What was going on here? Then people start looking at
it for a while and they start to get it. For instance, they can see that there is too much going in
terms personnel changes that is causing us a problem, say. What is causing this project not to
be as productive as the other, oh it looks like they changed staff three times when the other
one didn’t. What does that mean? How serious is that? How worth is this project? If you lost a
person in the middle of a project how big of a project is that. What is that equivalent off?
In regards to the effects of such games, I think you can test general understanding as a result
of using games. You don’t test by noticing that they got better playing the game. That is not
the idea.
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Could an idea be that they are more motivated towards the course, higher participation, etc.?
Yes, and maybe they bring the insights from the simulations into the next case or problem
setting.
The general attitude to the course itself and the way it is taught could perhaps be influenced?
Yes, that is right. And I also think that people are more likely to remember what they have
learnt in the environment; they are more likely to remember it; they are more likely to use it
again. It is not just for memorizing it for the test, it is more than that.
Any downsides/challenges?
It is clearly I think going to be used with other pedagogical approaches because simulation
games are experimental but it may not be the best way to teach a theoretical framework. You
might use it to create intuition about the theoretical framework and then in a lecture or similar
lay out the framework in detail so you motivate the interest that way in the framework, so
when they actually see it they go “ohh wow, I see why I need that now” as oppose to what
they have thought it you have just showed them the framework without doing the simulation
first.
Do you think you need an incentive to participate?
Yes, although if you have a game that is designed so not everyone can participate I would say
there is a downside there. My project management simulation game is a single player
simulation so as many people as you have computers can participate so usually everybody
participate in that. With the Beer Game the way we used to run it people participate in teams
of two and I think that is about as good because they talk, they debate, they decide what to do.
The number of participants could also match the same number of people in a team that it
would look like in a real situation, e.g. a board meeting would consists of a certain number of
board members, correct?
Yes, that is true. In certain situations you could do that.
5. How do you determine a simulation’s effectiveness as a learning tool, both educationally?
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I think I do that partly by listening during the debriefing and understanding how their
conclusions were but also just how you evaluate any other learning approach: how are they
doing on the exam, etc.
Would that be your success criteria as well?
Not only, I also care about how they reason during class discussion and during debrief but I
think they are both important.
And what about in a professional environment?
No I usually don’t test people so there it is just a matter of pushing them in an interactive
mode to reach better conclusion in the discussion. I teach a lot of executives but I almost
never test executives.
Would you say that the effectiveness is higher if the game replicates the real world?
I don’t think you can perfectly replicate the real environment but I do think that if you want a
simulation that is a reasonable representation of the problem you are trying to educate
students about, in the particular dimensions you are dealing with, so it does not have to be a
replication of reality but it has to contain the essential elements, so one of the big things you
e.g. managed in project management is scope/quality, cost and time. Those elements needs to
be in project management simulation but certain other elements may not have to be, in fact, I
think simulations needs to focus its attention only on the elements that you are trying to get
the teaching across, so if its about project management you don’t want a simulation that is so
realistic that it includes worries about marketing, that is just outside the scope.
So you don’t think you can apply a game over a whole set of topics and various courses and
integrate it over time?
Yes, you could. Going back to Crimson Readings, you are basically re-in acting real life and
it is always a little artificial but I think that is okay as long as the complexity that you want
students to confront are there in the simulation, and not the ones that are not relevant for the
course. So if it’s a marketing course you don’t want operations issues in the forefront; to put
certain things out of scope.
And what about the instructor’s role?
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Yes, I think he/she is important, especially in the debriefing part. I think during the simulation
often you are observing as an instructor. To really get the maximum learning out of a
simulation I think you have to have a debriefing experience, maybe there are games out there
that don’t require a debriefing experience but to me when I have used simulations the
debriefing is the place where you really see the learning happening.
6. How would you measure the benefits/effects on the outcomes of using such games? Some would argue (the theory of experiential learning, Kolb, 1984) that learning is conceived as a process and not in terms of outcomes only – do you agree?
Yes it is a process, and if you were to examine students that have been through something like
that they were do better, for example in terms of higher grades. But it would not be the only
outcome. Other outcomes could be the extent they remember the material five years later?
How did it stay with them? How well did it serve them in their subsequent years? How
interesting and engaged they were? These are all things that are worth asking about and
watching for.
7. How would you compare a student’s performance in a course using simulation games, compared with a regular lecture?
Yes you could. It would depends on so many things, the simulation itself, the debriefing, the
pedagogical skills of the person leading the debrief and so on.
I am planning to compare performance in such games, through the students’ attitude of the
games found in evaluation’s reports and through grade comparison. What do you think of
that?
I think that is an interesting experiment.
8. Which factors and conditions do you believe must be met to make a simulation game a successful and integrated part of today’s learning experience?
I think it has to be a simulation that engaging the students, they have to be interested in it so
that they are really trying and not just going through the motions, I also think the debrief is
really important, that there is a post-experiment of some kind, some sort of reflection build in
to the way the simulation is used.
Do you think IT skills are important part in order to gain anything from the games?
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No, I don’t think so. The most I have seen you don’t have to be a particular experienced with
IT.
9. In an active learning environment such as simulation games, what kind of learning (cognitive, social, motivational, etc.) do you think the students/professionals will gain/experience?
And how would you compare it with a regular lecture/real world experience?
10. Which potential do you think simulation games holds? Here at CBS? Any specific courses? Some subjects it would work better than others?
Yes, I think it does. One of the challenges here has to do with whether the IT infrastructure
works well enough, so it is not good to plan a simulation in class and some of the equipment
not functioning. At Harvard Business School Publishing they offer simulation classes within
strategy, marketing, operations and so on.
11. Any recommendations when introducing such games? How should it be integrated? Use the simulation in class or outside classroom/ as training course for professionals?
In my teaching note of the project management simulation I have staged the different
scenarios so you would do scenarios one and two, and then you might teach some stuff, and
then you would do scenarios three and four, on and on like that. Followed by a debrief after
each cycle. I would combine with other materials. Other more traditional materials like
papers, maybe cases, etc.
When you have limited number of hours available I think the simulation cycles should run
outside classroom, and then the debriefing will be included in class, do you agree?
I think that is possible too, I think it is even part of my teaching note that instructors need to
decide for themselves whether they play in class or not. I think it is possible to do it that way
and get a great deal out of it, as long as you include the debriefing.
12. To what extend do you think simulation games can be compared with the real world?
I think there is always somewhat remove from the full complexity of the real situation; the
important thing is whether it includes the elements relevant for the course.
And in terms of the social aspects found in teamwork? Do you think that environment is
similar to a real situation?
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Suddenly people have a lot of fun with simulation games, a lot of laughter, a lot of giving
each other a hard time. Reasonably close.
13. Are there any circumstances where using simulation games wouldn’t be advisable? Why?
I think there would a lot of places where you wouldn’t use simulations. I think you need a
certain amount of time in the course space to devote to it, to let it fully develop. I wouldn’t
use it in a really short course probably. Those courses that wouldn’t allow me for a rich
enough debrief. And that is probably why I tend not to use simulation games with executives
as often and that is because the things I do with the executives are much shorter in duration
and simulations does require a certain amount of effort; get to run, orchestra the logistics, so
you wouldn’t do it if it was part of small program that does not really have space for it, like a
week course.
14. What are the main trade-offs between simulation games and other learning techniques?
They are certainly more engaging. There may be more efficient approaches for certain
subjects, not as engaging as simulation games but more efficient through non-simulation
approach.
Compared with case method is to me kind of like simulation because you are asking people to
put themselves in the protagonist but you are not asking people to play a game or walk around
and do things so its kind of like a simulation where a lot of what you are imagining is
hypothetical. I mean I think it has some of the benefits as case teaching has; it’s engaging,
people tend to remember it, when they acquire ideas they acquire an understanding of the
context that motivates the actions and that makes the action makes sense in the that context so
you don’t end up with someone that can repeat the theory to you but actually I have no idea
how to apply it or why we need to have a theory like that.
15. Do you also think it could be a beneficial tool for professionals? What could they gain from using this tool? And how would you measure these gains in the real world? Any industries for which simulation games are more appropriate than others?
Not asked because of overlap with previous questions.
16. How do you see the use of simulation games for learning purposes in the future? Any challenges?
At CBS we need robust infrastructure so you can’t walk into a classroom and discover that
the equipment doesn’t work. But I think the possibilities are dramatic, as the technology gets
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better. More things are clearly possible in a simulation than it used to be. You see that with
video games as evolved, now they are pretty realistic. And a few years ago they probably
won’t. I think it holds a lot of features for online and screen based simulations or even our
projections will look silly when we get to that place.
17. Could you share any best practices?
A simulation is not a course, you can’t expect people to learn it by playing the game, you can
design an online course but I don’t think they are designed to be complete learning packages,
they have to be put together with other materials, forms, cases, lectures, papers, books, and
so forth. Beyond that I would say don’t underestimate the importance of debrief, the game is
great but leave plenty of time for the debrief because that is where the real learning gets
implemented I think.
B – Interview with Karsten Bobek Interview with Karsten Bobek: Instructor and facilitator of HotelSim (in Danish).
Dine erfaringer med simuleringsspil igennem din professionelle baggrund?
Nestlé har brugt web baseret spil og manuelle simuleringsspil siden 1994 på Nestlé
træningscenter i Vevey, Schweiz. Et af spillene, som jeg har benyttet, har været KamStrat,
udviklet i samarbejde med IMD, som omhandler fire Key Account Management teams på den
ene side og fire detailkæder på den anden side. Så får man udleveret ansvaret for et område,
f.eks. dyrefoder, hvorefter spillet går i gang imellem alle parter med fokus på pris fastsættelse,
forhandling, sortiment, markedsføring, merchandising m.m.. Det var den ene del. Den anden
del var da jeg implementerede ’Best Practices’ for Nestlé i Europa, for at anskueliggøre hvad
det drejede sig, hvilke processer der skulle lægges vægt på. F.eks. have vi et spil, Integrating
Commercial Planning, med fokus på budgettering planlægning, hvor forskellige funktioner
fra virksomheden, som f.eks. salg, marketing, finance, osv. skulle deltage. For at folk skulle
arbejde sammen, gav vi dem forskellige informationer, hvor de så var i teams og skulle
kæmpe mod hinanden. De der arbejdede bedst sammen, kom også med de bedste løsninger.
Derfor kunne vi anskueliggøre at ved at arbejde sammen i denne proces gjorde at resultatet
blev bedre. Meget simpelt. Det var også baggrunden for at jeg sagde efter at have undervist
første år, hvor jeg sagde at det kunne gøres bedre. At inkludere noget mere anskueliggørelse.
Hvad var din motivation for at bruge simuleringsspil?
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Det er sjovt, givende og jeg tror jeg kan uddanne nogle bedre studerende. Det der er formålet
er at komme op med nogle undervisningsformer der gør at de studerende bliver bedre når de
bliver uddannet.
Hvorfor HotelSim?
Der findes et hav af spil på markedet i dag, af meget svingende kvalitet. Jeg havde brug for et
spil som passede til studiet, dvs. indenfor ”Trade, Tourism and Hotel management, Travel
and Transportation or meetings and events” som jo er de 4 områder uddannelsen drejer sig
om. Samtidig skulle det være et spil på et højt akademisk niveau, som kunne udfordre de
studerende intellektuelt og samarbejdsmæssigt. Efter at have diskuteret med forskellige
indenfor forskellige industrier og på CBS, besluttede jeg mig for HOTELsim. Dette spil
havde såvel den akademiske som branchemæssige credibility, idet det var udviklet af erfarne
branchekendte folk fra RedGlobal i samarbejde med Cornell University og med input fra
Marriott og Best Western. Vi testede spillet i december 2010 for at sikre kvaliteten og det
bekræftede at spillet levede op til forventningerne.
Hvad var din forventning (og målet) til brugen af simuleringsspil?
Ja. Simpelthen, fordi f.eks. alle undersøgelser viser også at det med at man sætter sig ned og
læser en bog ikke er så virkelighedsfuldt som hvis man prøver at gøre tingene. Det her er så
tæt på virkeligheden som man kan komme. Det spil vi har her, HotelSim, i og med det er
udviklet bl.a. for Marriott og Best Western og i samarbejde med dem. Det er reality with a
touch. Det er det, det drejer sig om.
Havde du også en forventning om et højere karakter gennemsnit?
Det håbede jeg. Rent logisk set så burde være resultatet. At når der kommer mere ind i
hovedet og man bliver testet på det, at det også viser sig i resultatet. Der er ingen tvivl om at
spillet kan bruges til mange forskellige ting. Man kan sagtens forestille sig at man brugte det
over flere semestre over flere forskellige fag over flere moduler men med det samme spil.
Spillets parametre kan så justeres så det passer til det enkelte fags fokus område.
Men tror du ikke det er bedre at have forskellige spil til forskellige fag og emner?
Spørgsmålet er hvad du har brug for. Har du brug for f.eks. et spil alene omhandlende
finansiering eller har du brug for et spil der kan give der et helhedsbillede. Ligesom med
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spillet hos Nestlé, så er formålet også, at man er i stand til at arbejde på tværs af
organisationen og se fordelene i at man inddrog forskellige elementer ind i spillet. Det må
også være svært at være studerende at have det store gyldne overblik efter man har været
igennem forskellige moduler. Hvordan hænger tingene sammen? Der kunne man måske
forestille sig med sådan et spil, kunne få tingene til at hænge bedre sammen. Altså ved at
bruge det samme spil over flere forskellige fag.
Det der er det vigtigste i sidste ende er om man får bedre studerende ud ad den anden ende.
Det er også dét studerende klager over, at uddannelsen er god men undervisningen bør
forbedres.
Når du nævner bedre studerende, tænker du så på om du som underviser får bedre
evalueringer, eller om de får højere karakter, eller om læringen der er sket kan huskes om 1
år, m.m., eller hvad?
Det der er vigtig for mig, er at de studerende der kommer ud fra CBS er i stand til at komme
ud i erhvervslivet og opfylde et meningsfyldt job. Det skal være sådan at det man har lært kan
bruges til noget. Det er det der er det vigtigste. Det er klart at jeg kun repræsenterer et fag,
men det er stadig det overordnede mål for mig.
Baseret på disse erfaringer, hvad er dine anbefalinger til brugen af simuleringsspil?
Ud fra det vi har set indtil nu så virker det. Nu skal vi så til at se på hvordan vi kan bruge det
på et bredere plan. F.eks. hvis de studerende ikke føler at der er sammenhæng mellem
modulerne, så kunne det her være noget der kunne bringe tingene sammen. Og anskueliggøre
i højere grad hvad der egentlig er vigtigt i de forskellige fag.
For at det skal lykkes hvad er så dine succeskriterier?
At de kan bruge de erfaringer de har gjort sig når de kommer ud. Men også at de får højere
karakter end ellers. Den tilbagemelding der har været på trods af at folk er forskellige og de
betragtninger der måtte være. Jeg har taget så mange bias ud af det her ved at have samme
opgave setup, samme censor, samme mål beskrivelse, osv. Som sidste år. Så vi kan sige at det
her virker. At der er en positivt effekt. Det jeg gerne vil have der kommer ud af det, er at de
studerende føler at det er bedre end alternativet. Og så skulle det også meget gerne være
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således at det kan bruges til at vise de ansvarlige at her har vi noget som kan bruges bredere
til at få nogle bedre studerende ud af det.
Hvilke udfordringer ser du ved brugen af simuleringsspil?
Det kræver utrolig meget af underviseren fordi man skal kende den industri man er inden i.
Det er ikke bare at man lige sætter sig ind i hvordan sådan et spil fungerer. Som lærer kan
man opleve at man får udleveret en bog med vedlagte powerpoints og teacher’s notes som gør
det nemmere at forberede sig. Her skal man faktisk kende industrien ret indgående. Og hvis
du ikke kan det så er det hårdt. Det kræver meget mere tid til forberedelse end ved udlevering
af en bog. Det giver nogle begrænsninger. Fordi som de ligger lige nu, på markedet af
simuleringsspil, så ligger der ikke så mange manualer og lign. til at understøtte spillet. F.eks.
med HotelSim, har jeg – som underviser - kun set det samme som de studerende har set.
Hvad er instruktørens rolle i simuleringsspil? Forberedelsestid? Erfaring? Forudsætninger?
Det kræver nok at man laver et kursus for dem der skal undervise i det til at forstå hvordan det
hænger sammen. Udover at de fysiske rammer skal være på plads, så skal de studerende nok
forberede sig på at de skal bruge nogle flere timer. De skal forberede sig bedre hvis de vil
indgå i denne læringsproces. Det er jo en konkurrence situation. Dvs. at selvom man ikke får
en karakter for ens deltagelse, så bliver man jo alligevel bedømt på en eller anden måde.
Men i og med at det er svært at forestille sig flere timer til rådighed, så kunne man måske
forestille sig at pensummet bliver mindre, så man får flere timer til rådighed?
Helt klart. Man bliver nødt til at skære ned på pensummet. Der er to skoler på CBS. Den ene
siger at man skal igennem et vist antal sider på et fag. Så er der den anden skole som siger at
man kun skal bruge det man har brug for. F.eks. ved at case undervisning kun bruger cases
som pensum.
Men du mener stadig at spillet ikke skal stå alene, korrekt?
Ja, det kan det ikke. Spillet skal bruges til at illustrere de modeller som man har som pensum
eller at man har nogle cases som kan underbygge og virkeliggøre tingene. Men man behøver
nødvendigvis ikke at have et fuldt pensum bestående af to lærebøger. Jeg mener at man
sagtens kan klare sig med nogle artikler og cases ved siden af. Så længe at kvaliteten af disse
svarer til det ønskede curriculum. Men også ved at måle på hvor mange timer skal den
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studerende bruge på det. F.eks. ved at sige at 800 sider svarer til X antal timer. Hvor mange
timer skal man bruge på spillet og hvor mange timer er der tilbage til resten af pensum,
således af arbejdsbyrden er den samme.
Mener du simuleringsspil er et effektivt værktøj til læring?
Ja, det er der slet ingen tvivl om.
Hvordan vil du måle om det er effektivt eller ej?
I form af karakterer. Det er alt andet lige, i følge CBS, det eneste man kan måle på. Men man
kan også se på, at de studerende har fået nogle oplevelser. Sociale oplevelser, det at folk
arbejder sammen på en anden måde i et kompetitivt miljø end hvis de vil lave i en normal
gruppeopgave, fordi de konkurrerer.
Nogle læringsteorier nævner at det er processen fremfor resultatet der er vigtigt, i hvilken
grad er du enig?
Alt andet lige er det korrekt. Men i et miljø som det danske hvor alt skal måles så er det nok
alligevel karaktererne som er argumentet. Og selvfølgelig også de andre ting som du har
spurgt om i dit spørgeskema, og vi kan også sammenligne den tilbagemelding fra de
studerende fra sidste år med de studerende fra i år. Helt ærligt, så tror jeg ikke vi kan gå ind
og argumentere ret meget for de bløde værdier. Der er jo også det problem at licensen til at
spille spillet skal købes. Og når de studerende ikke kan tvinges til selv at købe spillet idet det
ikke betragtes som en lærebog - i følge Undervisningsministeriet - så skal CBS selv betale for
dette. Det vil så betyde at de skal have nogle rigtige gode faste argumenter for at gøre dette.
Mener du det kan bruges på universitetsniveau som en del af en kandidatuddannelse?
Det her kan bruges på mange forskellige niveauer. Der er mange forskellige spil på markedet
dvs. det tilpasses forskellige læringssituationer og niveauer. F.eks. arbejder jeg på at finde et
spil indenfor produktudvikling og eksport fra små og mellemstore virksomheder, som vil
være helt anderledes end HotelSim, men som vil opfylde nogle af de samme behov med at de
studerende kan forstå end proces og at de får nogle ‘aha’ oplevelser, at man forstå årsagen til
at man skal prøve nogle ting tilsvarende virkeligheden selv. Så jeg slet ikke i tvivl om at
tingene kan bruges meget meget bredt. Og i meget højere grad på CBS.
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Hvorfor blevet HotelSim spillet i grupper og ikke på individuelt niveau?
Det er der forskellige årsager til. . I Undervisningsbekendtgørelsen står der at studiet skal
sætte tingene i en global kontekst af hensyn til globaliseringen, osv. Det er så heldigt at 60 %
af holdet er ikke-danskere. Og det må jo også betyde at vi skal sørge for at de studerende er i
stand til at arbejde sammen på tværs af nationaliteter og kulturer i et miljø, hvor der var noget
pres på. Og dermed hvordan andre kulturer reagerer, og ikke mindst danskere, derudover også
at arbejde sammen på tværs af kulturer. Den anden årsag er at det vil være meget krævende
hvis folk skulle arbejde alene. Spillet er så omfattende, så de studerende vil ikke være i stand
til at komme nok i dybden med det og det vil betyde at de ikke vil få nok ud af det.
Det betyder også at performance i spillet afspiller gruppens performance, selvom man ikke
ved om det egentlig er halvdelen af gruppen der har spillet. Men det svarer jo også til det
virkelige liv. Gruppesammensætninger var lavet på den måde af der skulle være mindst tre
nationaliteter og der måtte højest være to danskere. Og ideelt set minimum 4 studerende per
hold.
Kan du se en forbindelse med performance i spillet og resultatet til eksamen?
Jeg kan se at de grupper som har klaret sig godt i spillet, at gruppemedlemmerne i disse
grupper ikke har haft de store udsving til eksamen. Men som jeg også sagde i starten, så er
performance i spillet ikke det vigtigste, svarende til at dem som får de bedste karakter på CBS
eller nødvendigvis er dem som får den bedste karriere i det virkelige liv eller får job først. Så
menneskelige relationer spiller også ind, netværk og mange andre ting. Så jeg er ikke sikker
på at dem som vandt spillet også er dem som også har fået de højeste karakterer. Men det må
analyseres yderligere.
Andet?
Hvis CBS kan beslutte at vælge 4-5 spil som dækker bredt, f.eks. et spil indenfor business
administration, supply-chain, entrepreneurship, retailing, m.m. Og gerne flere spil så man
tilsvarende dækker de højere antal af forskellige uddannelser CBS repræsenterer. Men som
nævnt tidligere, så er det vigtigt at man skal vide og forstå hvordan man bruger spillene og
hvordan de hænger sammen først.
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Det er ikke direkte relateret, men det kunne være rigtig godt hvis man kunne få lov til at give
en karakter for deltagelse i spillet, men det har jo ikke noget at gøre med spillet og det vil
også gøre det vanskelig for at se om det fungerer. Det har jo også vist sig at de studerende
faktisk har brugt den samme forberedelsestid eller mere end de ville have gjort på en normal
undervisning. Afslutningsvis, så er der ingen tvivl om at simuleringsspil kan hjælpe med at
anskueliggørelse hvad det egentlig drejer sig om. At sætte ord på teori i højere grad end hvis
man skal arbejde ud fra en bog.
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C – Quantitative Questionnaire
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D – Meta Data Set (Excel version)
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E – Course Evaluation 2010
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F – Course Evaluation 2011
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