Learning Mechanism Aggregation - Towards Enhanced Student E-LearningCarl Beckford

Department of ComputingThe University of the West Indies, Mona

Jamaicacarl.beckford@uwimona.edu.jm

Ezra K. MugisaDepartment of Computing

The University of the West Indies, MonaJamaica

ezra.mugisa@uwimona.edu.jm

Abstract: Higher Education Institutions (HEIs) embrace the use of technology to enhance course delivery. As technology improves, teachers and administrators are asked to use new teaching strategies and assessments as they prepare students. Where learners do not access knowledge in the mode most suited to their form of knowledge acquisition, there may be non-optimal learning. Many theories of teaching and learning have been purported. Some of these are with respect to various learning mechanisms such as cognition, needs, intelligences and learning styles. A debate persists whether there is a need to examine learning mechanisms (styles) or whether there is any impact to learning by applying the determined learning mechanisms. The debate concludes the need for a best-fit of content matching to the type of teaching methodology to be applied. This paper posits OLECENT, an approach to provide increased learning in a batch of learners by lessening the gap between teaching and learning styles. The Learning Mechanism Aggregation Framework is posited for the aggregation of learning assessment instruments and identifying the commonalities or primitives where such learning instruments are deemed to be within same equivalence class. The Framework determines the transference of knowledge based on the determined best fit learning mechanisms but allows the service requester or learner the flexibility of receiving knowledge transference in other learning indexes within the learning instrument.

Introduction

The standard and quality of course delivery and the level of learning or knowledge transfer which takes place are attributed to the parties (service requesters and service providers) but there are questions with respect to whether the greater burden rests on the learner or the teacher. Where learners do not access knowledge in the mode most suited to their form of knowledge acquisition, there may be a loss of learning or a delay in learning and/or ultimately non-optimal learning. Further, as the population within a batch of learners increases, there is an increasing tendency away from the optimal level of learning, amidst the limited and finite number of human teachers and resources, and the current design of Learning Management Systems or other e-learning tools.

Many theories of teaching and learning have been purported and/or established. Some of these are with respect to various learning mechanisms such as cognition, multimedia, needs, motivation, intelligences, learning styles, and others. There is a debate whether applying a specific learning mechanism enhances the learning of the learner. Some researchers say that applying learning styles in order to improve learning results have not provided any form of conclusive evidence that matching the form of instruction to learning style improved learning or even attention (Kirschner, 2017; Wallace, 2011). Others say that statistical analysis supports distinctions of learning styles among students of various disciplines and consideration of learning styles influences and enhances learning (Englander, Terregrossa, & Wang, 2017; Kunioshi, Noguchi, & Tojo, 2018).

Whereas some researchers say there is no strong evidence that teachers should tailor their instruction to their students' particular learning styles (Glenn, 2009), what is needed is an acknowledgement that learners may have a dominant style per type of knowledge transfer and an approach which dynamically determines and administers the knowledge transfer (Mumford, 1995). This paper posits OLECENT, an approach to provide increased learning in a batch of learners by lessening the gap between teaching and learning styles. The Learning

Mechanism Aggregation Framework is posited as an approach to aggregate and classify learning instruments, and dynamically administer knowledge to a learner or learning service requester based on best fit learning index(es).

Learning Theories

Education has always been provided with new ideas about learning and teaching. As technology improves, teachers and administrators are asked to use new teaching strategies and new assessments as they prepare students. Teaching in an online environment requires a special set of teaching skills since many of the strategies and tactics associated with best teaching practices are somewhat constrained by the primarily text-based environment. Many theories of teaching and learning have been purported and/or established. Some of these are Cognitive Load Theory of Multimedia Learning, Maslow's Hierarchy of Needs, Cognitivism, Experiential Learning, Constructivism, ARCS Model of Motivational Design, Constructionism, and Multiple Intelligences Theory.

The theory proposed by John Sweller, Cognitive Load Theory of Multimedia Learning, focuses the load on working memory during instruction. The theory describes the human cognitive architecture, and the need to apply sound instructional design principles based on our knowledge of the brain and memory (Sorden, 2012). Abraham Maslow’s theory, Hierarchy of Needs is a motivational theory in psychology that argues that while people aim to meet basic needs, they seek to meet successively higher needs in the form of a hierarchy. Maslow presented the idea that human actions are directed toward goal attainment. Any given behavior could satisfy several functions at the same time; for instance, going to a pub could satisfy one's needs for self-esteem and for social interaction (Maslow, 1987). The theory of Cognitivism says that the learner is viewed as an information processor (like a computer) and focuses on the inner mental activities (Hung, 2001). The ideal learning system should provide for some capture of each learner’s learning mechanism/style/intelligence.

Teaching and Learning Mechanisms

Research in the area of learning style/mechanism/intelligences has been active for in excess of five decades. Increasingly, research in the area of learning style is being conducted in domains outside psychology. These domains include medical and health care training, management, industry, vocational training and a vast range of settings and levels in the field of education (Cassidy, 2004). Learning styles are ways of learning presumed to allow individual(s) to learn best. It is believed that most people have a preferred way in processing information (Kolb & Kolb, 2005). There are a number of instruments developed for determining learning mechanisms, preferences or styles. Some of these are Witkin’s Field-Dependence/Field-Independence, Kolb’s Experiential Learning Model (ELM) and Learning Style Inventory (LSI), Hunt et al.’s Conceptual Level Model, Dunn & Dunn’s Learning Styles Inventory (LSI), and Fleming and Baume’s VARK Model (Cassidy, 2004).

The theory by Howard Gardner, Multiple Intelligences identifies seven distinct intelligences. The theory says that we are all able to know the world through language, logical-mathematical analysis, spatial representation, musical thinking, the use of the body to solve problems or to make things, an understanding of other individuals, and an understanding of ourselves. Where individuals differ is in the strength of these intelligences and in the ways in which such intelligences are invoked and combined to carry out different tasks, solve diverse problems, and progress in various domains. The VARK Model evolved from the VAK Model. The Visual, Auditory and Kinesthetic (VAK) learning style model is a common and widely-used model of learning style. According to this model, most people possess a dominant or preferred learning style; however some people have a mixed and evenly balanced blend of the three styles of visual learners, auditory learners and kinesthetic learners. In summary visual learners tend to learn through seeing and think in pictures, auditory learners tend to learn through listening and have highly developed auditory skills, and kinesthetic learners tend to learn through moving, doing and touching and express themselves through movement (Fleming, 2006).

The Kolb’s Experiential Learning Model (ELM) and Learning Style Inventory (LSI) has been called the Kolb Learning Style Model. Kolb and Kolb (2005) states a 9-fold classification of learning styles with combinations of feeling, watching, thinking and doing/acting scored as a 12-item self-report questionnaire. Kolb's learning theory sets out four distinct learning styles (or preferences), which are based on a four-stage learning cycle. The model offers both a way to understand individual people's different learning styles, and an explanation of a cycle of experiential learning that applies to us all.

Learning Mechanism Debate

In the paper, Learning Styles/Teaching Styles: Should They… Can They Be Matched? , Dunn & Dunn (1979) says that for decades, supervisors have been evaluating faculty in an effort to isolate those characteristics that produce effective instruction. Their efforts have been misdirected by weaknesses both in their assumption and their basic designs (Dunn & Dunn, 1979). Dunn & Dunn says that the attitude teachers hold towards various instructional programs, methods, and resources as well as kinds of youngsters they prefer working with constitute part of their “teaching style”. The paper states that teachers can assess themselves with an instrument that simultaneously identifies their teaching style and reveals the areas that need to be expanded to respond to additional characteristics. However, the debate persists whether there is a need to examine learning mechanisms such as styles or intelligences or whether there is any impact to learning by applying the determined learning mechanisms.

The Position against the Usage of Learning Styles

A learning style is supposedly a mode of learning that is most effective for an individual. It supposedly helps to improve learning results. Why does this myth persist? Twenty-five years of research on how applying learning styles improve learning results and related themes have not provided any form of conclusive evidence that matching the form of instruction to learning style improved learning or even attention (Wallace, 2011). An instructor who attends a learning-styles seminar might start to offer a broader mixture of lectures, discussions, and laboratory work—and that variety of instruction might turn out to be better for all students, irrespective of any matching (Glenn, 2009). Most so-called learning styles are based on types; they classify people into distinct groups. The assumption that people cluster into distinct groups, however, receives very little support from objective studies. Nearly all studies that report evidence for learning styles fail to satisfy just about all of the key criteria for scientific validity (Kirschner, 2017). Prisacari and Danielson (2017) concurs with Kirschner when they did a study to compare student performance taken in paper-based or computer-based testing mode. The results do not provide evidence to suggest that instructors need to be concerned about testing mode (paper versus computer) when designing and administering chemistry tests (Prisacari & Danielson, 2017).

Glenn (2009) says that there is no strong scientific evidence to support the "matching" idea, they contend there is absolutely no reason for professors to adopt it in the classroom (Glenn, 2009). Bretz (2017) reviewed articles and concluded that no experimental evidence exists to support the hypothesis that instruction designed in response to student learning styles can actually improve achievement (Bretz, 2017). There is quite a difference between the way that someone prefers to learn and that which actually leads to effective and efficient learning. A preference for how one studies is not a learning style (Kirschner, 2017).

The Position for the Usage of Learning Styles

Amidst the voice of those opposing the moot of the importance of learning mechanism debate, other researchers provide their evidence to support the moot. The study by Nitz, Ainsworth, Nerdel, and Prechtl (2014) showed that student perceptions of interpreting and constructing visual-graphical representations and active social construction of knowledge, predicted students' outcome at class level, whereas the individually perceived amount of terms and use of symbolic representations influenced the students' achievement at individual level (Nitz, Ainsworth, Nerdel, & Prechtl, 2014). For a given lesson, one instructional technique turns out to be optimal for all groups of students, even though students with certain learning styles may not love that technique (Pashler, McDaniel, Rohrer, & Bjork, 2008). The work of Kunioshi et al (2018) examining Japanese and American learners conclude that teaching styles seem to simultaneously result from the cultural context as well as reinforce it. Science and engineering instruction in the Japanese educational context tends to reflect and reinforce a personalised transmission of knowledge style, while instruction in the American context tends to match and reinforce learning styles characterised by impersonal, inductive thinking (Kunioshi et al, 2018).

The study by Englander et al (2017) examines whether there are distinctive learning styles among students majoring in one of several science disciplines, namely biology, chemistry, pharmacy and physician's assistant. The work concludes that statistical analysis supports such distinctions (Englander et al, 2017). Khan, Shamim, and Nambobi (2018) identifies myriad ICT (information and communication technology) tools and shows association between learning styles and respective ICT tools. The author concludes an importance of integrating myriad online tools to ensure efficient utilization of learner’s time, student engagement and visualization, networking and

collaboration, availability, and usability and conformability. He says further that there are learners’ diversities and online tools and suggests matching learner types to ICT tools (Khan, Shamim, & Nambobi, 2018).

The Conclusion concerning the Usage of Learning Styles

There is evidence for a hybrid approach or an approach that does not assume that each learner has a fixed learning mechanism for all types and magnitude of knowledge transfer. In response to Glen (2009) concerning the variety of instruction being better for all students, irrespective of any matching, Kolb says that the bottom line is probably correct: There is no strong evidence that teachers should tailor their instruction to their students' particular learning styles. "Matching is not a particularly good idea (Glenn, 2009). It is concurred that an injective (1-to-1) matching of teaching mechanisms to learning mechanisms is not ideal, as the function may be bijective. Mokhtar, Majid, and Foo (2008) says that because students can absorb information in a variety of ways, researchers categorize learning styles into three groups: information processing based, personality based, and multidimensional or instructional based. Mokhtar et al. (2008) then classifies various learning instruments to one of these groups, namely Information processing: Kolb’s Experiential Learning Model, Felder and Soloman’s Index of Learning Styles, Honey and Mumford’s social approach; Personality: Myers–Briggs Type Indicator, Keirsey Temperament Sorter; and Multidimensional or Instructional: Dunn and Dunn Model, Human Information Processing Model (Mokhtar, Majid, & Foo, 2008). Mumford’s (1995) findings propose that many activities fail to achieve their potential because they concentrate on only one stage of the learning cycle, such as requiring online students to read a textbook chapter, but failing to include a related activity that instructs them to apply the chapter’s information (Mumford, 1995).

Whereas there is support for and against the topic of matching teaching style to learning style, it is concurred with Kolb that what is needed is a best-fit of content matching to the type of teaching methodology to be applied.  What is necessary is an approach that diagnoses a student learning style and attempts to do a match (not so much to a teaching style but) to the type of content with the flexibility of dynamic update as new information concerning the learner is gathered.  We propose the OLeCenT approach which allows learners of a particular mechanism or style the flexibility of engaging with leaning objects designed for other learning mechanisms.  The approach suggests that the system learns the variance by a particular learning style and provides the course offering to future learners primarily to match the newly learnt learning path. The debate is concluded with the concept of levels of learning by Entwistle (2012) and the discussion of optimal learning by Son and Sethi (2006), that the optimal level of learning is the highest level of learning achievable in a given time and nature of the uptake function; we consider the nature of the uptake function to include the learner, learning environment and learning mechanism.

The OLeCent Approach

In the interest of a positive effect on the level of learning, we propose OLECENT, an approach that employs a tool (OLeCenT) for learner-centric course delivery in the online environment. OLeCenT may be integrated with a Learning Management System for enhanced course administration. We embrace the integration of learning mechanisms or styles to achieve a maximal matching with the teaching styles. Teaching-learning in higher education institutions is examined with an analysis being done on course delivery in view of learning styles. OLeCenT has four components, namely Diagnostic Analysis, Repository and Workflow Setup, Learning Administration, and Learner-Centric Assessment and Evaluation, see [Figure 1].

Diagnostic Analysis

The Diagnostic Analysis component allows for any learning style mechanism that has measurable notations, see [Figure 2]. Various LSIs may be stored for usage. This allows learning objects to be used with multiple LSI without tagging LOs to the instruments. Learning Objects are tagged for general VARK features and can therefore be used with any of the Learning Style Instruments. Likewise Learning Style units are classified based on VARK content and is used as reference to determine which learning objects best match the various learning styles. OLECENT uses learning style indexes throughout its implementation and therefore translates the terms, codes and notations of specific learning style instruments to the OLECENT learning style indexes. The tool provides each teacher and learner with a diagnostic assessment to ascertain the teaching-learning style(s) and preferences. Both teacher and learner are tested to determine skills matching. The learner-centric tool may be

Figure 1: The Online Learner-Centric Tool (OLeCenT) has four components, Diagnostic Analysis, Repository and Workflow Setup, Learning Administration, and Learner-Centric Assessment and Evaluation.

designed for any of the sets of learning styles with any Learning Style instrument. The learning style units are classified based on the Visual-Auditory-Kinesthetic content thus providing ease of relation to learning objects.

Repository and Workflow Setup

Within the Repository and Workflow Setup component, the tool is able to (1) receive and update learning objects of different types for a single unit of learning as well as (2) setup a designated workflow of how the learning objects are ordered for delivery of the course content. These two processes are reflected as Maintain Learning Object Learning Style Index(es) and Setup Learning Style Course Path(s), see [Figure 2]. Each type of learning object is specific to a basic teaching-learning style. LMSs have the mechanism for teachers to provide learning content in reusable learning objects. The course designer is able to state the specific unit of course content and the related objects specific to the teaching-learning styles. There is a one-to-many relationship between each unit of course content and the teaching-learning style learning object. The teaching-learning style learning object forms part of the learning process and is designed to support one or more learning styles. The objects are placed in a repository for retrieval at course compilation subject primarily to the learning style course path that has been setup.

The Course Learning Object Workflow is initially setup by the course designer or teacher but may be updated as the system learns the norms of various users per learning style. In LMSs, course designers indicate the workflow of teaching-learning units and the learning objects which relate to the teaching-learning units as well as the direction and timing for display. This exercise continues for the learner-centric tool. Additionally, the designer indicates these workflows per learning style; the actual order of teaching-learning units may vary based on the learning style. The course designer also documents rules as to which learning objects should be omitted based on other options chosen. The rules also indicate the conditions on which to allow the learner-centric tool, based on its learning, to assist in the workflow of the teaching-learning process.

Learning Administration

For each learner the tool generates a learner course path (the set of teaching-learning style learning objects designated by the workflow for delivery of the course content), learns which other teaching-learning styles are favoured by specific types of learners and uses this acquired knowledge to enhance the formulated learner course paths, see [Figure 2]. The learner course path is determined from the learning style for the learner determined from Diagnostic Analysis, as well as the course learning object workflow for each learning style setup by the course designer or teacher during Repository and Workflow Setup. Having determined the learner course path, the tool provides the teaching-learning style learning objects based on the workflow and where the learner is at in the scope of learning the course content. The learner may however choose to view or undertake another path. For fluidity of learning, the tool provides the objects in hypermedia with more than one learning object related to the determined or

Figure 2: The OLECENT Model has inter-relations among the components, Diagnostic Analysis, Repository and Workflow Setup, Learning Administration, and Learner-Centric Assessment and Evaluation

chosen learning style. It is where the learner chooses to undertake other path(s) that the tool learns which other styles are favoured by specific types of learners in the delivery of certain sections of the course.

The Learning Administration component maintains the path of learning objects, duration and completion percentage for each learner. This is stored within the Learner Learning Object Workflow and is used as the source of data to determine whether an alert should be generated for the course designer or teacher. The Course Learning Object Workflow setup by the course designer or teacher may be updated as the system learns the norms of various users per learning style. Where the Course Path Update Factor was entered during Repository and Workflow Setup, and where a new learner path was undertaken by the learner, the Factor is used to determine whether the new path is used that many times by learners with similar styles when compared to the teacher-entered course workflow.

Assessment and Evaluation

Similar to Learning Administration, within the Assessment and Evaluation Model, the learner assessment path is determined from the learning style for the learner determined from Diagnostic Analysis, as well as the course learning object workflow for each learning style setup by the course designer or teacher during Repository and Workflow Setup, see [Figure 2]. Having determined the learner assessment path, the tool provides the teaching-learning style learning objects based on the workflow and where the learner is at in the scope of learning the course content. The path of learning (assessment) objects, duration and completion percentage for each learner is stored.

Subject to the flexibility setup with use of pre-requisites, OLeCenT determines the level of compatibility or disparity between the teacher designed path and the student chosen path. OLeCenT may generate a learning path consistency and disparity analysis for each course in view one or more teaching-learning styles, activities (including A-Assessment, B-Lab, F-Field work, G-General, L-Lecture, O-Other, S-Seminar, T-Tutorial), disparity allowance time (length of time the learning object was accessed) for 0 or more minutes, the diagnosed or chosen learning style, and consistency check criterion of learning path or learning object. The learning path consistency and disparity analysis for COMP1005 for all teaching-learning styles, all activities, where learning objects were accessed for 0 or more minutes, for the diagnosed learning style, and using consistency check criterion being the learning object, reflects that whereas fifteen (15) learners were deemed to be of type Visual Language, only 33% of such users followed consistently the learner course path initially determined by the course administrator, see [Table 1]. The disparity contains sections of the learner course path designed for other types of learners. Where the disparity in the other 66% of users followed a new path at a level equivalent or greater than the Course Path Update Factor, a new learner course path would be determined and real-time adjustment of the learner path may be done.

Table 1: Learning Path Consistency and Disparity Check is determined for 1 or more teaching-learning styles and learning object activities, for 0 or more disparity allowance minutes, for the diagnosed or determined teaching-learning style, using the learning objects or learning path as the main criterion for the disparity/consistency check.

The Learning Mechanism Aggregation Framework

The Learning Mechanism Aggregation (LMA) Framework embodies the approach of analyzing learning mechanism instruments, identifying commonalities or primitives among these instruments, defining central themes or indexes and enhancing knowledge transfer as per determined best central theme or index. Whereas the Online Learner-Centric (OLECENT) approach is based on the Learning Mechanism Aggregation Framework, the principles of OLECENT have been applied only to learning assessment instruments which examine learning in view of styles or intelligences. The LMA Framework is discussed in view of the Research Objectives, the Research Methodology, the Components of the Framework, and Evaluation of the Framework.

The Research Objectives

To understand, design and implement the Learning Mechanism Aggregation Framework, three objectives were deemed to be necessary. The objectives were determined based on what was necessary to examine and understand learning patterns among online learners and what affects such patterns, through to the proposal of a concept of identifying commonalities among learning with the aim of increasing the level of learning among similar types of learners.

The first research objective is to examine courses in an online environment offered by at least one university and analyze these against online learning standards and learning outcomes; the aim is to ascertain whether learners, their learning methods, and what influences their learning reflect commonalities or similarities across various types of courses. The second objective is to perform an analysis of learner-centric versus teacher-centric course delivery as provided with the teaching-learning methodology, including course material knowledge transfer, course delivery and assessment scheduling and their effectiveness with respect to an assessment of a measure of learning. The third objective is to develop an ontology of teaching/learning styles or types with the aim of finding commonality that may assist in applying same in the online environment.

The Research Methodology

The basic and applied research approaches can be quantitative or qualitative or both (mixed methods). The approaches are translated into the design processes that includes such as questions, data collection and analysis, and write-up and validation (Creswell, 2013). We employ the Design Science Research (DSR) methodology which involves the creation of new knowledge through design of novel or innovative artifacts (things or processes) and analysis of the use and/or performance of such artifacts along with reflection and abstraction. The artifacts include algorithms, human/computer interfaces, and systems design methodologies or languages (Vaishnavi & Kuechler, 2015). More specifically, DSR is employed with output as framework with specific examination of the process steps from Awareness of Problem to Conclusion. With the output as framework, there are real or conceptual guides to serve as support or guide. At the Awareness of Problem step, the output is a proposal which details aspects of the problem and a tentative design is the output of the Suggestion step, giving consideration specifically h ow to model the artifact, what to take into account or neglect in terms of features, and whether the model is appropriate with respect to level of abstraction, difference to reality, validation and constraints.

Evaluation is a very significant component of a Design Science Research contribution (Hevner et al., 2004; Peffers, Rothenberger, Tuunanen, & Vaezi, 2012). Peffers et al. say that evaluation methods may be varied including simulation, functional testing, informed argument, or scenarios (Peffers et al., 2012). Evaluation is generally regarded from either the ex ante or the ex post perspective. For the evaluation step, the ex ante perspective is applied as the framework is evaluated before implementation (Pries-Heje, Baskerville, & Venable, 2008). Ex ante evaluation of the artifact is done based on the design specifications alone, where methodologies are categorized along the dimensions of basic approach which includes fundamental, composite and meta approaches, and application which includes positivist/reductionist and hermeneutic ways.

Concerning the first research objective which is to examine courses in an online environment with the aim of ascertaining whether learners, their learning methods, and what influences their learning reflect commonalities or similarities across various types of courses, the DSR process step to be applied is Awareness of Problem with the output being Proposal. With respect to the second objective which is to perform an analysis of learner-centric versus teacher-centric course delivery and their effectiveness with respect to an assessment of a measure of learning, the DSR process step is Suggestion with the output being Tentative Design. Likewise, concerning hich the development

of an ontology of teaching/learning styles or types with the aim of finding commonality that may assist in applying same in the online environment, the DSR process step to be applied is Suggestion with output of Tentative Design.Commonalities amidst Learning and the Teaching-Learning Methods

Four types of courses are examined, namely (1) courses where the course content and assessment may be provided primarily in a written form (Theory), (2) a significant basis for the generation of knowledge to be garnered from the course is by use of software tool(s) external to the LMS (Tool-based), (3) a significant portion was mathematical or used mathematical symbols or formulae (Mathematical), and (4) courses where the transfer of knowledge include a practical, laboratory or hands-on component (Practical). Consideration was given to the more significant traits of a type employed within a course, thereby including each course within a single course type.

There are three overlapping domains of educational objectives, namely cognitive (knowledge-based goals), psychomotor (skills-based goals), and affective (affective goals) (Bloom & Krathwohl, 1986). Due to the transference of knowledge expected, the domain of knowledge-based goals is the one most relevant to faculty and administrator training; theory and mathematical courses are considered to be relevant to this domain. For courses which are tool-based, whereas the educational objective was also skills-based in terms of the tool being used, the primary objective remains knowledge-based. Courses with a practical component have a major educational objective being skills-based. The affective goals were also deemed relevant but to a lesser extent than the others.

Analysis was done of the Bachelor of Science and the Bachelor of Education degree programmes within a certain university. Twenty-five courses were offered in three types of faculties with teachers from the main and off-campus sites of the university. The faculties of consideration were Science and Technology, Social Sciences, and Humanities and Education. All courses were offered in parallel to the face-to-face mode of delivery which previously existed and for the significant majority, the teacher was the same for both modes of delivery. The data was collected primarily through the use of questionnaires and interviews. Whereas a number of course analyses was done including by programme type, by faculty, by faculty type, and by course type, a few elements of the specific data for this study were extracted. The selection is based on the uniqueness or distinction identified between the overall summary for all courses, and the data when compared within its specific type of course. Analyses were done with respect to both the teacher and learner experiences.

For this discussion the course and its use of the online environment in view of the learner participation, and the teaching-learning methods are examined. Whereas these two sections do not represent all the areas that were analyzed, they comprehensively represent the sections of the course that reflected interesting differences and notably differences that could aid in identifying the commonalities amidst learning and the teaching-learning methods in the online environment. The summarized data provide an overall examination of all courses with a comparative breakdown of courses summarized by course type. The four course types (theory, tool-based, mathematical, and practical) are used where these types are determined based on the course content and the general course offering. The “overall” column of each table, see [Table 2] and [Table 3] highlights the percentage of courses when related to all courses that were examined. The “theory”, “tool-based”, “mathematical” and “practical” columns each reflects the percentage of courses when related to the courses of its course type.

Learner Involvement

In view of the learner involvement, learner-learner interactions as well as the teacher-learner interactions are examined for comparison, see [Table 2]. The teacher-learner interactions relate to the general student response to questions posted by the teacher or the encouragement for group or other online discussion or meeting. These data were examined under the major categories dealing with student participation, in addition to the teachers’ level of satisfaction with the general student participation. For this analysis the participation based on an at least minimal usage of one or more posting or submission is examined. The attempt is to examine the percentage of courses which showed quarter, half, three quarters or more of the course student registrants with the at least minimal online participation. The eighty-four percent so reflected for courses where over three quarters of the student population made at least one submission does not necessarily suggest satisfactory online participation but it normally suggest a high percentage of courses with most students at least making one submission. The individual details reflect that none of the practical courses had the minimal participation of over three quarters of that course student population.

It must also be noted that “Very Satisfied” was available as an option for teacher response and teachers could have judged their satisfaction based on different criteria. Notwithstanding, enhancements to the LMS may not be the only necessity for improved student participation. Cumulatively thirty-six percent of all courses were deemed to have unsatisfactory student participation; this is in comparison to cumulative percentages of twenty-eight for theory courses, zero for tool-based courses, fifty for mathematical courses and sixty-seven for practical courses. As

all courses examined used similar online environment and the same version of the Learning Management System (LMS), the reasons for all tool-based courses being adjudged to have average or better student participation must be

Learner Participation CategoryOverall

(All Courses)

Analysis by Course Type

Theory Tool-based

Mathe-matical Practical

Learner Participation(Student population who made at least one online posting or submission)Quarter or less 8% 0% 0% 0% 67%Half or less (more than quarter) 0% 0% 0% 0% 0%Three quarters or less (more than half) 8% 0% 0% 17% 33%Over three quarters 84% 100% 100% 83% 0%

Teachers’ satisfaction with learner participationVery unsatisfied 4% 7% 0% 0% 0%Unsatisfied 32% 21% 0% 50% 67%Average 40% 43% 50% 33% 33%Satisfied 24% 29% 50% 17% 0%

Table 2: Comparison of the student participation between all courses and each course type.

explored. One reason purported is the need for tool-based courses to make online submission (from an output of the external tool) for a grade to be assigned, possibly increasing the comfort level of the student with the LMS. It is intended for this comfort level to be achieved by methods more technically oriented.

Teaching-Learning Methods

Comparisons are done specific to teaching-learning methods and general communication, primarily including the teacher-learner, see [Table 3]. These data were examined under the major categories dealing with the course delivery methods employed, other necessary communication not specific to the general course delivery such as the mechanism for general announcements or the teacher-learner communication to specific student issues.

Analysis of the data within the table below highlighted that for the general teaching-learning methods employed by the teacher, the two most used were the encouragement for group discussion and the posting of PDF or static files. Whereas the percentages for individual course types were approximately similar to that for the overall percentage, only half of the tools-based courses employed the method where students were encouraged or expected to learn through group discussion and debate.

Teaching-Learning Methods and Communication Category

Overall (All

Courses)

Analysis by Course Type

Theory Tool-based

Mathe-matical Practical

General Course Delivery methods employedQuestions in Discussion Board 76% 79% 100% 67% 67%Encouragement for Group Discussion 80% 86% 50% 67% 100%PDF or Static Files 80% 86% 100% 67% 67%Online presentations 52% 36% 100% 83% 33%Links to Online Course Resources 52% 50% 50% 67% 33%List of Offline Course Resources 68% 86% 100% 33% 33%

Announcements or Communication to specific studentsDiscussion Board – Announcements 80% 71% 100% 83% 100%LMS Messaging or E-Mail Facility 76% 86% 100% 67% 33%LMS Chat Facility 44% 57% 50% 33% 0%External E-Mail Facility 28% 21% 50% 17% 67%

Table 3: Comparison of the teaching-learning methods and general communication mechanisms between all courses and each course type.

The creation of online presentations, similar to the courses within the tool-based course type, requires the use of an external software tool. With respect to the use of online presentations, it was noticeable that the course type which was distinctively different and better than the general trend of all courses, contained the tool-based courses. Teachers of theory and practical courses may not have found this method to be vital in communicating the course information. More fittingly, the time for development and conversion to online of properly constructed e-tutorial presentations had a directly proportional effect to its effectiveness. The experience or knowledge which exists for the tool-based and mathematical courses could possibly lessen the time for the development of similar unit of course content. Overall when one considers the course delivery method employed by the open learning campus, the most distinctive course type is the tool-based. This alerts us to the fact that changes to the LMS in the area of course delivery may have the greatest or least impact on tool-based courses. It is important that further determination be made to ensure which changes to the LMS would cause most positive impact.

For other necessary communication not specific to the general course delivery, the survey revealed that the two least used methods were the LMS chat facility and the external e-mail facility. As a principle, due to the covertness of the use of an e-mail facility, Open Campus discourages its significant use. The consideration would be that it is possible that an improvement in an area in the LMS would minimize the use of the external e-mail facility.

The cognitive (knowledge-based goals) and affective (affective goals) domains of educational objectives were identified during the analyses. In view of Theory, Tool-based, Mathematical and Practical courses, the learners within theory and tool-based courses appear to receive greater benefits within the online learning environment. Whereas the specific types of learners were not ascertained, commonalities were identified across the types of courses. The learner participaction within practical courses were notably less than other types of courses within the online environment. Within the online environment, in the order of most negatively affected and therefore a greater need for the increase in the level of learning, the learners in the following types of courses must be considered for assistance: practical, mathematical, tool-based and theory.

With respect to the teaching-learning methods, the most distinctive course type is Tool-based and may be most affected by changes to the online learning environment or the specific LMSs. Concerning learner-centric versus teacher-centric course delivery, LMSs with great effort may cater to most learning styles; however, they favour in different levels, a minimal number of learning styles. Learners are expected to receive the material within that which is facilitated by the teacher or the learning majority and then ensure that knowledge acquisition takes place. Within the LMS, the learner does not get the option to choose the teaching style or the method believed to be most beneficial for the material to be learned. In view of learning styles, LMSs may be used in the ways described because of the limitation of the LMS or the lack of a standard to ensure learner-centric course delivery and assessment. The findings with respect to the learner involvement, teaching-learning methods, the learner-centric versus teacher-centric online learning environments, and the OLECENT Approach and user analyses are used to outline the desired components of the Learning Mechanism Aggregation Framework.

The Components of the Framework

The Learning Mechanism Aggregation (LMA) Framework spans classifying the instruments that will be used to assess the type, method or mechanism of learning, through to applying the determined best fit mechanism to enhance learning. Consequently, the components of the LMA Framework includes Classify Learning Instruments, Define Learning Indexes per Instruments Class, Perform Analyses to Determine Best Fit Learning Index(es), and Administer Knowledge Transfer as per Best Fit Learning Index(es), see [Figure 3].

Classify Learning Instruments

The Classify Learning Instruments component of the LMA Framework includes Identify Learning Instruments and Partition Instruments. A number of accepted or other learning instruments, mechanisms, approaches and/or tools that assess learning are identified. The learning mechanism represents a central facet or unit of a learning instrument, such as “load” in Sweller’s Cognitive Load Theory of Multimedia Learning, “need” in Maslow's Hierarchy of Needs, “mind” in Cognitivism, “styles” in Kolb’s Experiential Learning and Fleming’s VARK Model, and “intelligences” in Gardner’s Multiple Intelligences Theory. The learning instruments are then partitioned using the adopted software testing technique, equivalence class partitioning (ECP). Equivalence partitioning or ECP is a

software testing technique that divides the input data of a software unit into partitions of equivalent data from which test cases can be derived (Tsui et al, 2018).

Figure 3: The Learning Mechanism Aggregation Framework includes Classify Learning Instruments, Define Learning Indexes per Class, Determine Service Requesters and Providers Best Fit Learning Index(es), and Administer Knowledge Transfer.

The equivalence partitioning of the learning instruments identifies commonalities among learning instruments, grouping such learning instruments together, and ensuring that each learning instrument exists in a single equivalence class. An equivalence partitioning of the learning instruments reflects equivalence class one (1) with Kolb’s Experiential Learning, Fleming’s VARK Model and Gardner’s Multiple Intelligences Theory, equivalence class two (2) with Maslow's Hierarchy of Needs, and Cognitivism, and equivalence class three (3) with Sweller’s Cognitive Load Theory of Multimedia Learning. The commonality among the learning instruments of equivalence class one is the possible representation of the learning mechanisms as factors/measures of auditory, visual and kinesthetic. The commonality among equivalence class two is the possible representation of the learning mechanisms by mental decisions. The OLECENT Approach is derived from equivalence class one. The determined instruments equivalence classes serves as output to the Classify Learning Instruments component and input to the Define Learning Indexes per Class component.

Define Learning Indexes per Instruments Class

The Define Learning Indexes per Instruments Class component of the LMA Framework includes Determine Primitives per Partition and Define Learning Indexes in terms of the primitives. In computing, language primitives are the simplest elements available in a programming language. A primitive is the smallest 'unit of processing' available to a programmer of a given machine, or can be an atomic element of an expression in a language. A number of primitives or commonalities among instruments per equivalence class is determined. The list of primitives is enhanced with any uniqueness of a particular learning instrument thereby ensuring that the learning mechanism of each learning instrument may be defined in terms of the primitives. Where there is a uniqueness of primitive within a learning instrument, other learning instruments may be considered to have none of that primitive. The OLECENT Approach (derived from equivalence class one) defined the learning indexes in terms of the primitives of Visual, Auditory, Kinesthetic, Reading, Feeling, and Thinking. Equivalence class two defined the learning indexes as Perception, Physiological, Human Intelligence, and Memory.

The learning mechanisms of the instrument are each defined as a relative make-up of the existence of equivalence class primitives. Where there is empirical evidence to support the level of existence of the equivalence class primitive, the factor of each primitive may be included. Without averages by historical data or other forms of empirical evidence, the factors for each primitive may be setup as based on ratio of best guesses and worst guesses, or a True-False value indicating existence. Using Kolb’s Model, the OLECENT Approach defined Activists as a make-up of Kinesthetic and Thinking; Accommodators were also setup as the existence of Kinesthetic and Feeling. The learning index(es) (defined in terms of the primitives) per instruments class serve as output to the Define Learning Indexes per Class component and input to the Perform Analyses to Determine Best Fit Learning Index(es) component. Using Maslow’s Model, Self Actualization is defined as a make-up of Perception and Physiological.

Perform Analyses to Determine Best Fit Learning Index(es)

The Perform Analyses to Determine Best Fit component of the LMA Framework includes Perform Diagnostic Analyses of Service Requesters and Service Providers and Determine Best Fit Learning Index for Service Requesters and Providers. Diagnostic analyses of the learners or service requesters and teachers or service providers are performed. The analyses are performed to each service requester and provider in order to determine a best fit learning index or a best fit set of learning indexes.

The pre-defined survey, instrument or data gathering tool for a particular learning instrument is administered to each service requester and provider. The result of the data gathering tool has a direct mapping to a learning mechanism within the learning instrument, and a mapping to one or more best fit learning indexes. The Online Learner-Centric approach allows ease of changing or implementing of any other learning instrument in a learning instruments equivalence class. The learning index(es) for each service requester and provider serve as output to the Perform Analyses to Determine Best Fit Learning Index(es) component and input to the Administer Knowledge Transfer component.

Administer Knowledge Transfer as per Best Fit Learning Index(es)

The Administer Knowledge Transfer as per Best Fit Learning Index(es) component includes Provide Knowledge Transfer and Vary Knowledge Transfer as a Best Fit to Service Requesters. The provision ofknowledge as a best fit to service requesters is done as per determined learning index(es). During the delivery of knowledge, allowance is made for knowledge transfer via other learning indexes as per the desire of the service requester. During the knowledge transfer process, there is constant re-evaluation of best bit learning index. The knowledge transfer may be dynamically adjusted based on a new best fit learning index determined. The learned service requester and provider preferences per instrument class serve as output to the Administer Knowledge Transfer component and input to the Classify Learning Instruments component; thereby creating a cycle within Learning Mechanism Aggregation Framework and a dynamic review and improvement of the process.

Evaluation of the Framework

As the Learning Mechanism Aggregation Framework was applied in different extents to different equivalence classes, both the ex post and the ex ante perspectives (Pries-Heje, Baskerville, & Venable, 2008) are employed. With use of the work done in the OLECENT Approach, the ex post perspective is applied to equivalence class one (1) with Kolb’s Experiential Learning, Fleming’s VARK Model and Gardner’s Multiple Intelligences Theory. Ex post perspectives are applied to equivalence class two (2) with Maslow's Hierarchy of Needs, and Cognitivism, and equivalence class three (3) with Sweller’s Cognitive Load Theory of Multimedia Learning. By

applying the LMA Framework within the OLECENT research, institutions were allowed to setup multiple learning style instruments, with seamless adoption of a new learning style instrument within the same equivalent class. An institution could seamlessly employ Kolb’s Experiential model in one course period and Fleming’s VARK Model in another period due to the learning style indexes as the main drivers throughout the LMA Framework. With greater analyses and definition of new learning indexes, the principle may be applied to the other equivalence classes.

Benefits of the Learning Mechanism Aggregation Framework

The Learning Mechanism Aggregation (LMA) Framework allows for the aggregation of best-practice learning assessment instruments and identifying the commonalities or primitives where such learning instruments are deemed to be a similar equivalence class. The framework allows any of accepted learning style assessment or inventory model to be employed and ease of transition among learning instruments of the same equivalence class. The LMA Framework determines the transference of knowledge based on the determined best fit learning mechanisms but allows the service requester or learner the flexibility of receiving knowledge transference in other learning indexes within the learning instrument.

Provision is made for a dynamic reshaping of the knowledge transfer per learning mechanism as other learning preferences are determined. Institutionally-determined existing or self-created Learning mechanism/style/approach instruments may be setup with a comparative level of the primitive learning mechanism or styles such as visual, auditory, kinesthetic, thinking, feeling, reading among some learning instruments, as perception, physiological, human intelligence, and memory among another set of learning instruments, or deemed commonalities among other learning instruments.

Conclusion

The standard and quality of course delivery and the level of learning or knowledge transfer which takes place are attributed to the parties (learners and teachers, or service requesters and service providers). Where learners do not access knowledge in the mode most suited to their form of knowledge acquisition, there may be non-optimal learning. Teaching in an online environment requires a special set of teaching skills since many of the strategies and tactics associated with best teaching practices are somewhat constrained by the primarily text-based environment. Further, a debate persists whether there is a need to examine learning mechanisms such as styles or intelligences or whether there is any impact to learning by applying the determined learning mechanisms. Whereas there is support for and against the topic of matching teaching style to learning style, it is concurred that what is needed is an approach that diagnoses a student learning style and attempts to do a match to the type of content with the flexibility of dynamic update as new information concerning the learner is gathered.

In the interest of a positive effect on the level of learning, we propose OLECENT, an approach that employs a tool (OLeCenT) for learner-centric course delivery in the online environment. We embrace the integration of learning styles to achieve a maximal matching with the teaching styles. OLeCenT generates a learning path consistency and disparity analysis for each course in view one or more teaching-learning mechanisms or styles, activities, disparity allowance time, the diagnosed or chosen learning style, and consistency check criterion of learning path or learning object. The Online Learner-Centric (OLECENT) approach is based on the Learning Mechanism Aggregation (LMA) Framework which embodies the analyzing of learning mechanism instruments, identifying commonalities or primitives among these instruments, defining central themes or indexes and enhancing knowledge transfer as per determined best central theme or index. The Learning Mechanism Aggregation Framework allows for the aggregation of best-practice learning assessment instruments and identifying the commonalities or primitives where such learning instruments are deemed to be a similar equivalence class. The Framework determines the transference of knowledge based on the determined best fit learning mechanisms but allows the service requester or learner the flexibility of receiving knowledge transference in other learning indexes within the learning instrument. Application of the Learning Mechanism Aggregation Framework should enhance the level of learning within a batch of learners or learning service requesters.

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