Computer simulations in science education
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PhD research Nico RuttenSimulations in school practice
professors:Prof. dr. Wouter van Joolingen
Prof. dr. Jules Pieters
supervisor:Dr. Jan van der Veen
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Introduction
Theory
Literature review
Computer Simulations in Science Education
Teaching
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
INTRODUCTION
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Computer simulations are programs based on
a scientific model ofa system or process.
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reference: de Jong, T., 1998, Scientific discovery learning with computer simulations of conceptual domains
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Good sims can actually be pedagogically more effective than apparently similar classroom demonstrations and laboratory exercises with real equipment.
PhET sims have two main goals: increased student interest and improved learning.
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reference: Wieman, C.E., 2006, A powerful tool for teaching science
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reference: Finkelstein, N., 2006, High-tech tools for teaching physics - The physics education technology project
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reference: Finkelstein, N., 2006, High-tech tools for teaching physics - The physics education technology project
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Why use them?
COMPUTER SIMULATIONS IN SCIENCE EDUCATION
INTRODUCTION
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reference: de Jong, T., 1998, Scientific discovery learning with computer simulations of conceptual domains
Because simulations are simplified versions
of the natural world, they have
the potential to facilitate learning
by focusing students’ attention more
directly on the targeted phenomena.
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In comparison with alternatives such as textbooks, lectures, and tutorial courseware, a simulation-based approach offers the opportunity… to learn in a relatively realistic problem-solving context to practise task performance without stress to systematically explore both realistic and hypothetical
situations to change the time-scale of events to interact with simplified versions of the process or system
being simulated.
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reference: van Berkum, J.A., 1991, Instructional environments for simulations
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Visualizations may be especially useful for helping students see structure in phenomena and processes that are traditionally “invisible” to students.A process can be invisible if it is… too small (bacterial reproduction)
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
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Visualizations may be especially useful for helping students see structure in phenomena and processes that are traditionally “invisible” to students.A process can be invisible if it is… too small (bacterial reproduction) too big (tectonic shifting)
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
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Visualizations may be especially useful for helping students see structure in phenomena and processes that are traditionally “invisible” to students.A process can be invisible if it is… too small (bacterial reproduction) too big (tectonic shifting) too fast (chemical reactions)
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
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Visualizations may be especially useful for helping students see structure in phenomena and processes that are traditionally “invisible” to students.A process can be invisible if it is… too small (bacterial reproduction) too big (tectonic shifting) too fast (chemical reactions) or too slow (evolution).
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
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Visualizations may be especially useful for helping students see structure in phenomena and processes that are traditionally “invisible” to students.A process can be invisible if it is… too small (bacterial reproduction) too big (tectonic shifting) too fast (chemical reactions) or too slow (evolution).
Visualizations can make these processes accessible so learners can perceive the important structures.
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
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Features that are of particular help in the teaching of science: They free up teacher time so that they can interact with
students instead of dealing with the management of the experimental setup/apparatus and supervision.
Simulations offer an easy way of controlling experimental variables, opening up the possibility of exploration and hypothesizing.
Presenting a variety of representational formats including diagrams, graphics, animations, sound and video that can facilitate understanding.
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reference: Blake, C., 2007, Reconsidering simulations in science education at a distance - Features of effective use
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
THEORY
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Three trends nowadays dominate the field of learning and instruction. Learners are encouraged to: construct their own knowledge (instead of copying it from
an authority be it a book or a teacher): constructivism
in realistic situations (instead of merely decontextualised, formal situations such as the classroom): situationism
together with others (instead of on their own): collaborative learning.
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reference: de Jong, T., 2003, Learning complex domains and complex tasks, the promise of simulation based training
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Learners experiment and construct knowledge as ‘scientists’:
They provide the simulation with input
observe the output
draw their conclusions
and go to the next experiment.
DISCOVERY LEARNING
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reference: de Jong, T., 1998, Self-directed learning in simulation-based discovery environments
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discovery teaching:“All forms of obtaining knowledge for oneself
by the use ofone’s own mind.”
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reference: Swaak, J., 2001, Discovery simulations and the assessment of intuitive knowledge
expository teaching: The teacher presents
knowledge toa student recipient.
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Scientific discovery is usually interpretedas the processes of mindful coordination
between hypothesized theories andevidence collected by experiments.
reference: Reid, D.J., 2003, Supporting scientific discovery learning in a simulation environment
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reference: Hulshof, C.D., 2006, Using just-in-time information to support scientific discovery learning in a computer-based simulation
DISCOVERY LEARNING
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transformative processes:learners’ activities in these phases are performed for the sole purpose of yielding knowledge
regulatory processes:serve to manage and controlthe inquiry learning process
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reference: van Joolingen, W.R., 2005, Co-Lab - research and development of an online learning environment for collaborative scientific discovery learning
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Difficulties learners may have in dealing with discovery learning processes: difficulties in generating
and adapting hypotheses poorly designed experiments difficulties in data interpretation problems regarding the
regulation of discovery learning.
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reference: Reid, D.J., 2003, Supporting scientific discovery learning in a simulation environment
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reference: van der Meij, J., 2006, Supporting students' learning with multiple representations in a dynamic simulation-based learning environment
The goal of scientific discovery learning is not only to help subjects acquire domain knowledge…
…but also to enable them to apply their knowledge in new situations.
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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Literature review kinds of visualization peer collaboration kinds of support gaming level of engagement
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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kinds of visualization
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reference: Ploetzner, R., 2009, Students' difficulties in learning from dynamic visualisations and how they may be overcome
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reference: Ploetzner, R., 2009, Students' difficulties in learning from dynamic visualisations and how they may be overcome
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Ploetzner, R., 2009, Students' difficulties in learning from dynamic visualisations and how they may be overcome
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Many have argued that interactive 3D virtual environments have great educational potential…
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reference: Dalgarno, B., 2009, Effectiveness of a Virtual Laboratory as a Preparatory Resource for Distance Education Chemistry Students
due to their ability to engage learners in the exploration, construction and manipulationof virtual objects, structures and metaphorical representationsof ideas.
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reference: Dalgarno, B., 2009, Effectiveness of a Virtual Laboratory as a Preparatory Resource for Distance Education Chemistry Students
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Dalgarno, B., 2009, Effectiveness of a Virtual Laboratory as a Preparatory Resource for Distance Education Chemistry Students
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Nevertheless, based on various researchers, it is found that 3D models may lead to cognitive overload problems in hypermedia-learning environments in particular,
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reference: Korakakis, G., 2009, 3D visualization types in multimedia applications for science learning - A case study for 8th grade students in Greece
as such environments are assumed to generate a heavy cognitive load.
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Since Physics and Chemistry deal with three-dimensional (3-D) objects, the ability to visualize and mentally manipulate shapes is very helpful in their learning. In fact, much of what Physics and Chemistry students know takes the form of images.
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reference: Trindade, J., 2002, Science learning in virtual environments a descriptive study
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Virtual reality is a computer interface characterized bya high degree of immersion, plausibility, and interaction, making the user believethat he is actually insidethe artificial environment.
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reference: Trindade, J., 2002, Science learning in virtual environments a descriptive study
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Trindade, J., 2002, Science learning in virtual environments a descriptive study
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reference: Mitnik, R., 2009, Collaborative robotic instruction - A graph teaching experience
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reference: Mitnik, R., 2009, Collaborative robotic instruction - A graph teaching experience
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reference: Mitnik, R., 2009, Collaborative robotic instruction - A graph teaching experience
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Mitnik, R., 2009, Collaborative robotic instruction - A graph teaching experience
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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peer collaboration
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Kewley reported that children’s peer collaboration promotes… their higher level of reasoning critical and flexible thinking and clearer understanding of the problems.
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reference: Powell, J.V., 2004, Teaching Techniques and Computerized Simulation in Early Childhood Classrooms
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Collaboration increasesthe likelihood that learners engage in the type of talk that supports learning,
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reference: van Joolingen, W.R., 2005, Co-Lab - research and development of an online learning environment for collaborative scientific discovery learning
such as asking and answering of questions, reasoning and conflict resolution.
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Manlove, S., 2009, Collaborative versus individual use of regulative software scaffolds during scientific inquiry learning
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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kinds of support
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How simulations may be combined with instructional support to overcome difficulties that learners may encounter: direct access to domain knowledge support for hypothesis generation support for the design of experiments support for making predictions support for regulative learning processes.
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reference: Blake, C., 2007, Reconsidering simulations in science education at a distance - Features of effective use
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Heuristics are rules of thumb that can help to reach a certain goal in a complex problem-solving situation, for example: simplify problem identify hypothesis slightly modify hypothesis set expectations vary one thing at a time (VOTAT) simple values
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reference: Veermans, K., 2006, Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain
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Heuristics are rules of thumb that can help to reach a certain goal in a complex problem-solving situation, for example: equal increments confirm hypothesis extreme values make a graph present evidence keep track.
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reference: Veermans, K., 2006, Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain
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Veermans, K., 2006, Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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gaming
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The motivation of games could be combined withcurricular contents into what Prensky (2003) calls ‘Digital Game-Based Learning’ (DGBL). Games that encompass education objectives and subject matter are believed to hold the potential to render learning of academic subjects… more learner-centered easier more enjoyable more interesting and thus, more effective.
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reference: Papastergiou, M., 2009, Digital Game-Based Learning in high school Computer Science education - Impact on educational effectiveness and student motivation
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Papastergiou, M., 2009, Digital Game-Based Learning in high school Computer Science education - Impact on educational effectiveness and student motivation
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
LITERATURE REVIEW
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level of engagement
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According to Hansen,students retain…
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reference: Bravo, C., 2006, Modeling and simulation in inquiry learning - Checking solutions and giving intelligent advice
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Engagement is a multifaceted construct and implies behavioral, emotional, and cognitive participation in learning experiences.
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reference: Wu, H.K., 2007, Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments
Behavioral engagement…refers to involvement in classroom and extracurricular activities, such as positive classroom behaviorsand the amount of time spending on homework.
Emotional engagement…includes positive and negative emotions to school, teachers, or academic activities.
Cognitive engagement…involves students’ intellectual investment and effort to understand complex ideas and use thoughtful strategies.
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Wu, H.K., 2007, Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments
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COMPUTER SIMULATIONS IN SCIENCE EDUCATION
TEACHING
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One concern is that instructors will simply show students which
simulation parameters to set andask the students to record the answers
or they may use the simulationas a demonstration experimentat the front of the class.
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reference: Lindgren, R., 2009, Spatial Learning and Computer Simulations in Science
These practices undermine the potential of simulations for supporting authentic inquiry practices that include formulating questions, hypothesis development, data collection, and theory revision.
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A novel software-based interactive method is designed to achieve learner satisfaction, using virtual scenarios for the theoretical explanation.
The following steps are followed in the proposed method: brief explanation of the chapter structure presentation of the scenario explanation of the main concepts of the chapter challenge the students to predict the scenario evolution brainstorming and collection of ideas simulation visualization discussion and theoretical explanation.
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reference: Duran, M.J., 2007, A learning methodology using Matlab/Simulink for undergraduate electrical engineering courses attending to learner satisfaction outcomes
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Duran, M.J., 2007, A learning methodology using Matlab/Simulink for undergraduate electrical engineering courses attending to learner satisfaction outcomes
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LITERATURE REVIEW
review article published, titled:
The Learning Effectsof Computer Simulations
in Science Education
COMPUTER SIMULATIONS IN SCIENCE EDUCATION
10-04-23Computer Simulations in Science Education
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10-04-23Computer Simulations in Science Education
http://slidesha.re/simrevEN(case sensitive)