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Editor’s Note
:  Use of concept maps is explored as a means of focusing student activity to promote effective learning. It also assists in transition from a teacher control to a learner managed environment. The value of this technique is supported by an extensive body of research.

Concept Map Provision for E-learning

Chao Boon Kheng Leng TEO, Robert GAY


The transition of instructions from traditional teacher-directed learning to online learner-directed learning is anything but smooth. In traditional classroom learning, students are almost entirely guided and have for ages, depend mostly on teachers for their motivations, directions, goals, progress monitoring and corrections. In contrast, for the e-learners to enjoy the same, if not better, learning benefits that online learning brings, they have to assume greater responsibilities over their own learning. Gone are the teachers’ assuring presence, inspiration, guidance and encouragement. As learning becomes more and more a self-directed experience, the community of learners and educators is looking for tools that support self-driven knowledge acquisition and provide sufficient learning aids to mitigate such losses.  

Keywords: E-learning, Personalized learning, Concept Maps, Research, Knowledge Model, Self-directed learning, Lifelong learning


In education, we need deliberate learning and deliberate teaching and an educational transaction occurs when the learners and the teaching programs are brought together. When the learning environmental context is face-to-face, with the medium of communication being ‘unprocessed’ (without technological aids) human voice, constrains by time and space boundaries, we have the traditional education environment. When the medium of communication is replaced by electronic means and conducted without time and space constraints, we have distance learning. While the student/teacher characteristics, subject content or communication variables in theory does not affect the fundamental theory of learning, the mode of educational transaction does require some implicit requirements from the learners.

The E-education Requirements

The e-education transaction, characterized by the absence of a teacher mentor, has imposed some responsibilities on the part of the students. In order for an e-learner to succeed, he is assumed to possess some form of self-discipline, ability to work alone, good time management, learning independence, readiness, the ability to plan for himself and the ability to assess his strengths and weaknesses (Watkins, 2005; Dunlap & Grabinger, 2003). However, many learners see such learning responsibilities as undue burdens. But we truly believe that such underlying philosophy of self-advocacy, self-determination or self-directed learning ability is consistent with what we know to be essential for our prevailing education philosophy. While this view is also upheld by many educators, it is not realized. In schools, many teachers still fail to assist students in becoming self-directed in learning. As a result it is very common, as Knowles (1970), way back in the 1970s, has pointed out, to leave school adult in other ways, but still dependent, or at least retarded in independence, as a learner. Unfortunately, as we step into the 21st century, the situation has not improved. While research has shown that an increasing number of educational institutions are finding ways to support self-directed study through open-learning programs, individualized study options, non-traditional course offerings, distance learning, and other innovative programs (Hiemstra, 1998), learners still experience a lack of the important learning factors of motivation and persistence (Súilleabhain & Coughlan, 2004) and have expressed feelings of isolation, lack of self-direction and increased management problems (Bennett et al., 1999; Abrami & Bures, 1996; Harasim et al., 1995) when they are called upon to take control over their learning.

The introduction of e-education and the student-centered approach to learning has also presented both educators and learners with a number of concerns. In the traditional education approach, the students’ learning activities, curriculum and learning routes are largely static, constrain and prescribe by the teachers. Little, if any, opportunities exist for the students to plan their own educational goals or to consider their own learning priorities, needs and preferences. Often, in a traditional setting, the students are spoon-fed and the exact topics to be learned are made explicit. However, for e-education, such guidance is reduced. Instead, the students are encouraged to construct their own learning paths, consider their own needs and learning goals and to rely less on the teacher for direction. This type of transition from a teacher-centered approach to a learner-centered approach is applauded by many and has even been viewed as the future vision for e-learning (Gay & Teo, 2006). However, while this type of transition is essential, the evolution is too abrupt. The sudden influx of freedom coupled with a lack of guidance and support has made the students feel daunted. As Hammond and Collins (1991) points out, “learners accustomed to teacher directed learning may have no experience of self-management of learning so it may initially be intimidating.” Furthermore, the skills (the ability to plan, the ability to manage learning, the ability to review and reflect on reasoning and research skills, etc.) associated with self-centered learning have placed too much cognitive demands on the students. Another issue, given such freedom, is the lack of awareness of the amount of knowledge or even the specific syllabus to follow.

Problems with regard to stimulating and sustaining learner motivation are also well documented in the literature of e-learning and the broader context of distance learning (Visser, 1998; Rowntree, 1992; Zvacek, 1991), especially when learners are working independently at a distance. Learner’s motivation is important for the learning process as it has been shown that students will only restructure or assimilate new data only if accommodation fails and when he or she is motivated to reconcile anomalies and to reduce inconsistencies (Wankat & Oreovicz, 1993). However, overcoming these motivational challenges can be difficult because of the complexity of human motivation and the vast number of motivational concepts and theories that exist (Keller & Suzuki, 2004). While the challenge of motivation and drop-out rates are typically answered through the provision of traditional face-to-face communications (Súilleabhain & Coughlan, 2004), the luxury of such provision is not available, or at least minimized, in the context of e-learning.

We believe that such frustrations that most e-learners experienced stemmed from the lack of self-direction.

Self-directed Learning

Most learners spend considerable amount of time acquiring information and learning new skills. The rapidity of change, the continuous creation of new knowledge, increased realization of knowledge as the core competence (Prahlad & Hamel, 1990) and an ever-widening access to information make such acquisitions necessary. Much of this learning takes place at the learner's initiative, even if available through formal settings. A common notion given to such activity is self-directed learning. In essence, according to Abdullah (2001), self-directed learners are "responsible owners and managers of their own learning process". Such individuals have the skills to access and process the information they need for a specific purpose. Self-directed learning integrates self-management (management of the context, including social setting, resources, and actions) with self-monitoring (the process whereby learners monitor, evaluate, and regulate their cognitive learning strategies) (Garrison, 1997; Bolhuis, 1996).

Self-directed learning is often associated with the skill of self-reflection. McNamara (1999) advocates that a highly motivated, self-directed learner with skills in self-reflection can approach any situation and treat it as a continual classroom from which knowledge can be acquired. While most forms of self-directed learning are informal, self-directed learning can become a powerful form of learning when it is performed in a systematic way of planning (what areas of knowledge and skills we need to gain in order to get something done), implementing (how we will gain the areas of knowledge and skills) and evaluating (how we will know that we've gained the areas of knowledge and skills) (McNamara, 1999).

Self-directed learning also incorporates the significant role of motivation and volition in initiating and maintaining learners' efforts. Motivation drives the decision to participate, and volition sustains the will to see a task through to the end so that goals are achieved (Corno, 1992; Garrison, 1997). It is similar to scaffolding (Teo and Gay, 2006) in the sense that control also gradually shifts from teachers to learners. Learners exercise a great deal of independence in setting learning goals and deciding what is worthwhile learning as well as how to approach the learning task within a given framework (Morrow et al., 1993). Domain-specific knowledge as well as the ability to transfer conceptual knowledge to new situations can also be learned as self-directed learning seeks to bridge the gap between school knowledge and real-world problems by considering how people learn in real life (Bolhuis, 1996; Temple & Rodero, 1995).

Literature on self-directed learning further asserts that self-directed learners demonstrate a greater awareness of their responsibility in making learning meaningful and monitoring themselves (Garrison, 1997). They are curious and willing to try new things (Hunt & Lyman, 1997), view problems as challenges, desire change, and enjoy learning (Taylor, 1995). Taylor (1995) also found self-directed learners to be motivated and persistent, independent, self-disciplined, self-confident and goal-oriented. Furthermore, Morrow, et al. (1993) observes that with proper planning and implementation, self-directed learning can encourage students to develop their own rules and leadership patterns.

Self-directed Learning for e-learning?

It is imperative to observe that it is not a coincidence that all the frustrations experienced by e-learners can be seemingly resolved by the adoption of self-directed learning. As pointed out in the previous section, we reiterate our belief that the e-education transaction has indeed imposed some responsibilities on the part of the e-learner and the trait of a self-directed learner is strongly anchored at the heart of the imposed responsibilities. However, while we aim to nurture all e-learners (or in a wider context, all students) to be self-directed learners, it is the trait or disposition that we want e-learners to develop, rather than a laundry list of observable behaviors we wish students to exhibit.

With a clear need to nurture self-directedness, the next question to ask is: Can self-directedness be taught? If possible, how can we teach self-directedness without imposing addition burdens on the learners? Is there any proven teaching or pedagogical means that we can adopt?

It is in this vein that this research sets out to investigate an effective mean to exploit our current advancement in technology to assist in the provision of self-directed learning. We state in our previous work (Teo & Gay, 2006; Teo et al., 2006), a scaffolding framework that can exist as one effective mean in the provision of self-directed learning. Here in this paper, we extend our earlier arguments (Teo & Gay, 2004) to investigate a concept mapping delivery methodology that can coexist with our scaffolding framework to provide a more comprehensive learning system.

Literature Review on Contemporary Concept Mapping Techniques

An examination of what concept maps are will lead to a broader discussion of the cognitive processes involved in their construction, maintenance and synthesis. However, as there are considerable literature on concept mapping (see, for example, Bogden, 1997; De Bono, 1993; Deikhoff, 1982; Gold, 1984; Kommers, 1992; Kozama, 1987; Novak, 1998; Reader & Hammond, 1993), this paper does not attempt to make an exhaustive analysis of all the aspects of the concept mapping techniques. Not only that, this paper does not even look at the learning benefits that are associated with the adoption of concept mapping (once again has been widely documented). Instead, this paper relies on picking out the factors of concept map which appeared to be essential because they cut across varied points of view that constitutes the conditions for providing quality self-directed learning. Hence, stemming from the basic concepts of concept map, the discussion will begin from the point of view of e-learning. Based on the unique characteristic of e-learning technology (i.e. the distributed nature of the distance learning modality, the physical separation of learners from instructor, the asynchronous communication paradigm, etc.), evidences supporting how the application of concept mapping techniques can complement the nature of instruction specifically designed for that modality will be presented.

Basic concepts

Concept mapping is a technique for representing knowledge in graph and was developed by J. D. Novak of Cornell University in the early 1980's. Cognitive theory underlying concept mapping in science grow out of two related traditions, namely Ausubel’s hierarchical memory theory (Ausubel, 1968) that posited a hierarchical knowledge structure and Deese’s associationist memory theory (Jonassen et al., 1993) that posited a network knowledge structure that did not take the form of a hierarchy (Ruiz-Primo & Shavelson, 1996).

Ausubel’s hierarchical memory theory or hierarchical concept map provides a basis whereby Novak and his colleagues worked from and coined the term “concept map”. Concept maps are intended to ‘tap into a learner’s cognitive structure and to externalize it’. It is recognized that not all concepts or proposition can be represented. Thus, such maps only provide a ‘workable representation’. Deese’s associationist memory theory or network concept maps on the other hand provide a beginning for characterizing cognitive structure as a set of concepts and their interrelations. Concepts are represented as nodes in a network linked by the associative overlap of two concepts. This theory acts as the basis for an indirect approach to elicit representations of cognitive structure such as word associations, similarity judgments, and tree building. Such methods produce networks with unlabeled lines. This network characterization led naturally to the current view of propositional knowledge known as the “semantic network” with concept nodes linked directionally by labeled arrows to produce propositions.

While both theories started off based on different perspectives, both played a key part that led to the development of concept map from which student’s knowledge structures are inferred. Concept Maps are graphical representations of knowledge comprised of concepts and relationships between them. Graphically, it consists of nodes and labeled lines which purport to represent some important aspect of a learner’s propositional knowledge in a subject domain. The node corresponds to important terms or concepts in the domain while the lines are connecting links that indicate the relationships between two concepts. A proposition is a meaningful statement about some objects or events and is formed by a concept-link-concept triple (a combination of two nodes and a labeled line). A concept is defined generally as a perceived regularity in events or objects, or record of events or objects, designated by a label. Concepts are usually enclosed in circles or boxes, and the relationships between the concepts are indicated by connecting lines that link them together.

Concept Mapping Provision for Self-Directed Learning

Although concept mapping is a very old form of presenting information, we believe its application holds the potential to uphold the contemporary practices in teaching and learning. More importantly, we see concept mapping techniques as one effective means to provide the provision of self-directed learning. Besides self-directed learning, evidences supporting how concept maps can be used to support learner-centered learning and the enhancement of learning experiences such as self-reflection, stimulating and sustaining learner’s motivation, learning independence and the ability to plan for themselves are also discussed in this section.

Concept mapping can connect the learner with the content and provide a wider learner engagement with the intent of providing a learner-centered experience (Laight, 2004). Furthermore, it complements and advances self-directed learning. As articulating-, reflective- and problem-based learning focus on self-direction and tends to be mainly unstructured, the use of concept mapping can be used to structure the information/knowledge acquired during these educational approaches (Farrand et al, 2002). Concept maps as a learning strategy thus supports and fosters new trends in higher education since it allows learners to externalize their thinking in a visual/verbal outline which enables them to review, reflect and revise their thinking.

The usage of concept maps also promotes self-reflection through its explicit structural model. It is important to note that learning only takes place through cognitive restructuring whereby the student revises their model of the world. Such self-reflection can only occur when students are able to comprehend and integrate the newly acquired knowledge into their cognitive structure. However, not all new concepts can be easily integrated. Often, the introduction of a new concept creates learning disequilibrium. However, it has been shown that when the new concept is structured together with a plausible and understandable concept map model, the model can aid in eliminating the disequilibrium by explaining and housing the new data (Wankat & Oreovicz, 1993). The reorganization is also aided as the information is presented in a hierarchical form with explicitly stated rules for generating hierarchies (Kurfiss, 1988). Learning new material in a form which is easy to recall from memory is aided if students are given objectives which help them key in on important material and if the material is presented in a well-organized fashion (Kiewra, 1987).

The important learning factors of motivation and persistence can also be catered for by concept mapping. Reports examining student attitudes to concept maps have indicated important non-cognitive influences such as academic workload, motivation and contextual institutional issues (Farrand et al., 2002; Santhanam et al., 1998). Learning style is another important student diagnostic target that has prognostic implications for student engagement and motivation to learn (Martinez-Pons, 2001). With regard to Keller’s attention-relevance-confidence-satisfaction (ARCS) model of motivation, accommodating various learning styles using a variety of teaching/learning activities can be expected to ensure relevance to the individual learner by facilitating ownership of and thereby engagement with learning content (Keller, 1987). We will show in the later section how concept maps can be used to cater for different learning style and hence, aid in stimulating the learner’s motivation.

Other important traits of confidence and independence can also be nurtured through the usage of concept maps. Harpaz, Balik and Ehrenfeld (2004), assessed student responses through the use of concept maps and found out that concept maps encourage students to think independently, increase their orientation in knowledge, aid in finding connections between the different areas, and give them more confidence in implementing their knowledge. Smith (1992) evaluated the use of concept mapping in an immunology nursing course and reported that concept maps encourage students to learn by themselves and provide the knowledge to implement into the clinical field. A major finding of Daley’s study (2002) also states that concept mapping helps adult students to understand their own learning processes and gain independence. Additionally, they were able to explain their learning processes through the use of learning strategies such as linking, developing interrelationships, creating meaning schemes, and constructing knowledge. It is reported that the maps help them to understand how they think, to think in a broader fashion, to search out complicated relationships, and to organize information so that they remember it in a much more comprehensive way. Daley (2002) also advocates that once the learners are able to explain their own learning, they were much better prepared to function as educational professionals promoting learning and change.

Proposed Concept Mapping Usage for E-learning

In view of our preceding arguments on the importance of cultivating self-directed learning traits for e-learners and the supporting evidences that concept mapping techniques can serve as one effective means for the provision of self-directed learning, we advocate that concept maps can serve as the missing bridge between e-learners and their learning needs. However, due to the nature of e-learning (i.e. minimized teacher-student interaction, geographical separation between teacher and student), it does not seem practical to incorporate concept mapping techniques if it requires frequent student-teacher interaction. Hence, in contrast to most concept mapping software that acts either as a drawing tool for the learners to facilitate knowledge construction or as an assessment means whereby teachers correct the learners’ learning assumptions, we work around the concept map limitations by using concept maps as a form of knowledge delivery mechanism. That is, instead of using concept maps as a drawing tool to facilitate the externalization of the students’ understanding, we elicit concept maps from the content expert’s perspective and use it as a knowledge roadmap to guide the learners. Specifically, to minimize the impediments to e-learning (and to aid learning independence and promote self-directed learning), 5 ways in which concept maps are being employed is presented.

1. Knowledge Roadmap

As stated in the preceding section, one impediment to learning independence and self-directed learning is the inability of the learner to understand what to learn, how much to learn and when to stop. For traditional learning, the amount of relevant curriculum materials to learn is usually finite and knowable. Furthermore, there will always be a teacher who is guiding and monitoring the student’s progress. However, when it comes to e-learning, the rate of knowledge expansion is exponential. This couples with the absence of the teacher and the inability of the student to plan for himself is one big hindrance to e-learning. Furthermore, the learning experience of continuity, integration and interaction that is often associated with traditional learning is now being replaced by a fragmented, episodic and discontinuous learning experience that comes with e-learning.

Although the pre-defined curriculum arrangement and teacher’s guidance is an effective learning facilitator, it inhibits the opportunities and freedom for the student to exercise planning or goal seeking. It also does not nurture the student’s creativity and willingness to explore new learning frontiers. Furthermore, it does not prepare the student for lifelong learning. It is important to note that learning is not a spectator sport, and students must take an active role in their own educational planning. However, as the students have not been exposed to such roles of planning, concept maps can be exploited to serve as explicit knowledge roadmaps of expert to smoothen the abrupt transition. The knowledge roadmap, categorizes in terms of academic domains, elicits the experts’ guidance and advices by placing key learning advices such as learning concepts interrelationships, recommended learning sequence, proposed curriculum planning and abstraction level into the learning concept’s metadata. For example, using a particular subject that the student is interested in as the main desired knowledge point (hereby called main course for discussion purposes), the expert guidance and advices (stored in the main course’s metadata in terms of course relationships) are externalized as a form of course concept map. Through the usage of 5 relationships, a roadmap of the expert can be mapped radically outwards from the main course. This roadmap effectively gives an overview of the domain knowledge that initiates from the point of view of the subject experts. Hence, it can serve as a guide whereby the learners can extend, plan and formulate their own learning curriculum.

Five  course relationships are discussed:

a. Associated learning course

The ‘associated’ relationship relates courses that reside at the same hierarchical level (i.e. same expertise level, content coverage) as the main course.

b. Essential pre-requisite learning course

The “essential pre-requisite” relationship relates courses that cover the essential learning concepts that must be mastered first before the main course (higher level) can be mastered. Hence, this relationship links the two courses and restricts the lesson plan in a particular sequence; a ‘sequence in time’ relation. That is, the essential pre-requisite learning course must always be taken first before attempting the main course. Essential pre-requisite learning courses are typically theoretical in nature and cover all fundamental learning theories and concepts such as facts, laws and principles. It is usually delivered at the conception of the learning and form a bulk portion of the learner’s initial academic learning phrase. It is characterized typically by knowledge impartation (from content) – cognitive awareness (student) – cognitive reorganization (student).

c. Supplementary pre-requisite learning course

The “supplementary pre-requisite” relationship is similar to the “essential pre-requisite” relationship in the sense that such courses (if taken) should be taken first before the main course. This relationship also has a ‘sequence in time’ relation. However, as opposed to the essential pre-requisite learning courses, supplementary pre-requisite learning courses are not essential to understand the learning concepts that reside at the main course. Instead, these courses cover some good-to-know learning concepts that supplements and provide additional learning materials or practice that can be taken at the learner’s convenience. The supplementary pre-requisite learning course focuses on helping the students to link and interconnect their learning. Opportunities are provided by such events to break away from the compartmentalization of knowledge and skills and to help the students to construct a better network of knowledge and skills. Furthermore, such learning events are an authentic form of learning that prepares the students for the increasing demands of the workplace (Kwok & Tan, 2004).  

d. Augment post-requisite learning course

The “augment post-requisite” relationship cover learning concepts that augment the learning concepts taught in the main course; that is, the learning concepts covered in the augment post-requisite learning course must make greater, more numerous, larger, or more intense arguments on the learning concepts that are taught in the main course. Such courses emphasize more on the theoretical aspects of the learning concepts and are inclined towards advancing the theoretical and abstract concepts imparted in the previous education setting. This mode of learning is extremely beneficial in conveying paradigms of thinking and information. The application of knowledge and the development of communication skills are however secondary. Such learning courses are characterized by learning contents that are hierarchically organized and aim to guide the learning process through structured syllabi and tests. The training is usually housed in a certain context but the students are assessed both inside and outside the arena in which they hope to minister.

e. Utilize post-requisite learning course

The “utilize post-requisite” relationship cover learning concepts that utilize the learning concepts taught in the main course. These learning courses look at the practical usage of the knowledge. It focuses more on the application of the knowledge rather than its fundamentals. This mode of training is based on the premise that students learn most effectively through experiences and practice in a deliberately organized program. It uses real life examples as the basis for purposeful training. Such mode of learning is highly relational yet unstructured in the sense that training is focused on the working towards problem solving and the teaching materials are structured outside the normal school curriculum. The student has complete control over his learning and this mode is participatory in nature.

2.   Knowledge Snapshot

Most educational systems face challenges that warrant the reform of learning contents. The increasingly complex nature of the objects that make up the educational landscape and the amazing progress technology and the specification of standards for various aspects of educational technology has made is alarming. Knowledge is becoming more and more complex and needs to be continually up-dated. Learning content hence, needs to be revised accordingly to improve its relevance and quality and keep up with the changing pace.

Currently, as the learning contents are revised, the previous link between the learning concepts are removed and replaced by new associations. For example, using a computer course as an illustration, a computer course back in the 1980s will contain links between computers and storage devices such as floppy disk and drive (5.25-inch and 3.5-inch). However, a revisit of such courses now will see the storage device link being replaced by CD-ROM, mircodrive, thumbdrive and other usb-powered devices. Such snapshots of knowledge in time are often lost (except in history courses).

Hence, besides utilizing the dynamic nature of concept maps, we also propose the employment of concept maps as a static knowledge snapshot in time. The usage of a static concept maps (to store previous links and associations) can also help the learner monitors, modifies and plans his self-directed learning curriculum. By reviewing past curriculum planning and the learning acquired, the learner can recap or advance his knowledge.

3.   Visualization Mechanism

a. Course structure

The course concept maps have to be visualized in terms of an n-dimensional space – not planar or Cartesian space. The relationships between the courses can be thought of as ‘deep’ as opposed to ‘surface’ linkages. Each course concept map will be centered on one main course. All its related courses and relationships will be mapped radically outwards from the main course. This mapping should be done dynamically at real time; that is, a selection of a different learning concept as the main course will automatically change the structure of the course concept map.

As the entire lecture topic, unit, course or even curriculum can be treated in this format, the holistic relatedness of ideas can readily be illustrated. Furthermore, the use of concept maps in large class teaching represents varied instruction that can be expected to enrich lectures, inspire interest and attention and promote receptivity and cooperation (Biggs, 1999; Buzan & Buzan, 2000).

b. Cognitive Structure Externalization

The term ‘cognitive structure’ stemmed from Piaget’s general theoretical framework "genetic epistemology" that looks into how knowledge is developed in human organisms. The concept of cognitive structure is central to his theory and comprises of four primary cognitive structures; i.e. sensorimotor, preoperations, concrete operations, and formal operations. Cognitive structures change through the processes of adaptation. Adaptation can be further decomposed into the process of assimilation and accommodation. Assimilation involves the interpretation of events in terms of existing cognitive structure whereas accommodation refers to changing the cognitive structure to make sense of the environment. Hence, cognitive development consists of a regular attempt that aims to adapt to the environmental input changes through the process of assimilation and accommodation. In this sense, Piaget's theory is similar in nature to other constructivist perspectives of learning such as Bruner (1996; 1990; 1986; 1983; 1973; 1966; 1960) and Vygotsky (1978; 1962) which states that learning is an active process in which the learner is constantly creating and revising his or her internal representation of knowledge (Duffy & Jonassen, 1992). Constructivism is a major influence in current science education and an inspiration for its reform (Deboer, 1991; Duschl, 1990; Driver, 1989; Osborne & Wittrock, 1985).

The cognitive structure theory is also in line with an enormous body of research that focuses on science education and the ideas students bring with them into the classroom. As constructivist theory contends, students are not empty vessels. Hence, they come into classroom or any educational setting with a wide range of prior ideas and conceptions. Therefore before any knowledge can be imparted, in a learner-centric learning environment, it is crucial to invite the learners to clarify where they stand, at present, in terms of the new knowledge. This stage, called the cognitive structure externalization, is very important.

Many prior studies have demonstrated the importance of informal and formal prior knowledge (see Dochy, 1992). Essentially, prior knowledge activation sets the stage for learning by sharpening the perception of the learner. It tells the learner not only what s/he has to learn but also what the person perceives s/he already knows. Sometimes this is a rude awakening, sometimes a corroborating experience. The result of such reflection when compared with records of the learner’s past expertise can aid in the identification of the appropriate starting point of his learning route. The activation of prior knowledge is being applied as a sort of learning preparation. It is used to connect the new knowledge with the existing ones; to synthesize prior knowledge with the new content. Hence, the main design consideration of this part is to effectively plan the querying methods to accurately retrieve the learner’s prior knowledge and investigate how to connect the learner’s prior knowledge to the new knowledge.

Once the prior knowledge correlation is achieved, then training can begin. The prior knowledge (stored in the cognitive structure) will be externalized in the form of concept maps. Research has shown that concept maps have been widely accepted as an effective aid to help students externalized their knowledge in a domain effectively (White & Gunstone, 1992) and evolve and support meta-cognitive activities (Novak, 1990).

4.         Personalized Learning Routes

Using concept maps to create personalized learning path is the main motivation underlying this research. We argued that in consideration of all the learning benefits that concept mapping brings, the provision of personalized learning routes stand at the heart of the student-centered or self-directed learning approach. The ability to provide personalized learning routes can make or break the e-learning vision. In view of the importance imposed on the term, personalization, it is imperative to classify our stand on creating personalized learning routes.

Personalized learning routes are tailored to the learning needs and interest of each individual student. It honors and recognizes the unique gifts, skills, and prior knowledge of each student. It is dedicated to providing individualized learning programs for each student whose intent is to engage each student in the learning process in the most productive and meaningful way to optimize each student’s learning potential.

5.   Learning Preferences Catering

Education research confirms beyond any semblance of doubt that not all students are able to learn successfully at the same pace, with the same approach, in the same environment, on the same path, and in the same style and manner. The underlying assumptions of a one-size-fit-all traditional learning model are flawed. Every individual assimilates information according to their own unique learning style, needs, and interests. While some learn at faster pace, others need more time. Learning preferences also vary.

There is a large body of research on learning styles or preferences. Some popular models for describing and eliciting learning styles are presented below.

a.     Index of Learning Styles (ILS)

ILS is an online instrument used to access learning preferences. It is based on a learning style model that consists of four dimensions (active/reflective, sensing/intuitive, visual/verbal, and sequential/global).

b. VAK (Visual, Auditory, Kinesthetic) Model

The VAK learning Style uses the three main sensory receivers - Vision, Auditory, and Kinesthetic (movement) to determine the dominate learning style.

c. Kolb's Learning Inventory

Kolb's learning theory sets out four distinct learning styles (or preferences), which are based on a four-stage learning cycle. The four-stage cycle is Concrete Experience - (CE), Reflective Observation - (RO), Abstract Conceptualization - (AC) and Active Experimentation - (AE) while the four-type definition of learning styles consists of Diverging (CE/RO), Assimilating (AC/RO), Converging (AC/AE) and Accommodating (CE/AE).

d. Carl Jung and Myers Briggs Type Indicator (MBTI)

MBTI uses four dichotomous dimensions namely Extroversion (E) versus Introversion (I), Sensing (S) versus iNtuition (N), Thinking (T) versus Feeling (F) and Judging (J) versus Perceptive (P) to determine a person’s learning preference.

Besides making the associative knowledge structure of topic or subject matter explicit, concept maps can also be exploited to support students in making appropriate connections to their learning preferences. Hence, besides acting as a virtual interface, the concept map nodes can also be used to house different learning preferences presentation for a particular learning concept; that is, when the learner selects a particular node in the concept map, the node can automatically presents the course offering presentation that caters to the particular learning preference of the learner.


The biggest challenge for e-learning programs is to continually monitor and reform the teaching approaches to keep up with the changing needs of learners. Through an analysis of current e-learning literature, we believe that the current high attrition and drop out rates continues to be the Achilles heel for e-learning. This, we believe, stemmed from the fact that the very nature of e-learning requires a high degree of self-motivation, persistence, commitment from the learners. These requirements are creating a serious problem of high attrition and drop out rates as it is not recognized and managed. Currently, too many learners lack the adequate skills and mindset for the rigors of e-learning. Hence, this necessities a revisit (and possible reform) of the current e-learning model to provide a more self-directed learning experience that supports self-driven knowledge acquisition.


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About the Authors

Teo Chao Boon

Teo Chao Boon is a Ph.D candidate in Information Communication Institute of Singapore (ICIS) at School of Electrical & Electronic Engineering, Nanyang Technological University. He received the B.S. degrees in Electrical Engineering from Nanyang Technological University, Singapore in 2003. His present research interests centered on distance learning technologies and practices, e-learning, pedagogical issues in e-learning and personalized learning.

Email Address: teoc0005@ntu.edu.sg



Robert Gay Ph.D.

Professor Robert Gay obtained his PhD in Electronics Engineering from the University of Sheffield in 1970. He was a lecturer at Singapore University from 1972 to 1979.He was also Deputy Director of the Computer Centre from 1976 to 1979. From 1979 to 1982 he was a Research Associate at Rutherford and Appleton Laboratory in the UK. Since 1982 he has been a Professor at NTU. He was also the Research Director of Gintic Institute of Manufacturing Technology from, 1989 to 1999. From 2000 to 2003, he was Director of the ASP Centre and from 2003 to 2006 he was Director of the Managed Computing Competency Centre (MC3) at NTU. MC3 was successfully spun off, as a business unit, to a publicly listed company in February.

His current academic interests are: Web Services, Grid Technology Applications, Knowledge Based Systems, E-learning and Integrated Manufacturing Systems and Services.

Professor Gay has more than 150 publications in conference proceedings and Journals.

He is an executive council member of the Singapore Computer Society and a fellow of the Institute of Engineers of Singapore.

He was awarded the Grouped Scholarship in Engineering and Metallurgy from the University of Sheffield from 1967 to 1970 and was also a key member of the LEAD (Leadership and Excellence in the Application and Development of Computer Integrated Manufacturing Curriculum) Award winning team, awarded by the Society of Manufacturing  Engineers, USA in 1992.

Email Address: EKLGay@ntu.edu.sg

Correspondence should be sent to Teo Chao Boon, InfoComm Research Lab, ICIS, EEE, Nanyang Technological University, Blk S2.1, Level B4-03, S2.1-B4-03, Nanyang Avenue, Singapore 639798, and email: teoc0005@ntu.edu.sg

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