Adaptive cognitive-based selection of learning objects Pythagoras Karampiperisa, Taiyu Linb, Demetrios G. Sampsonc* and Kinshukb aCentre
for Research and Technology, Hellas, Greece; of Piraeus, Greece
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Taylor Innovations 10.1080/14703290600650392 1470-3297 Original 202006 43
[email protected] PythagorasKarampiperis 00000May & Article Francis (print)/1470-3300 in 2006 Education (online) International RIIE_A_165014.sgm and Francis Ltd and Teaching
bMassey University, New Zealand; cUniversity
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Adaptive cognitive-based selection is recognized as among the most significant open issues in adaptive webbased learning systems. In order to adaptively select learning resources, the definition of adaptation rules according to the cognitive style or learning preferences of the learners is required. Although some efforts have been reported in literature aiming to update the adaptation logic used for a specific learner by updating his/her profile through the use of complex questionnaires that estimate the cognitive characteristics of learners, still the cognitive profile used for a learner remains static for a significant period, leading to the same selection decisions independent from the previous interactions of the learner with the system. In this paper, we address the learning object selection problem based on learners’ cognitive characteristics, proposing a cognitive-based selection methodology that is dynamically updated based on the navigation steps of learners in a set of hypermedia objects. The proposed approach utilizes the Cognitive Trait Model, that is, an approximation model for learner’s cognitive capacity that provides a concrete method for identifying learner’s cognitive characteristics based on learners’ navigation steps. In our experiment we simulate different learner behaviors in navigating a hypermedia learning objects space, and measure the selection success of the proposed selection decision model as it is dynamically updated using the simulated learner’s navigation steps. The simulation results provide evidence that the proposed selection methodology can dynamically update the internal adaptation logic leading to refined selection decisions.
Introduction The high rate of evolution of e-learning platforms implies that new types of learning services need to be developed and provided. To meet the current needs, such services should satisfy a diverse range of requirements, for example, personalization and adaptation (Dolog et al., 2004). In web-based education literature, the main category of learning systems that aim to deliver adaptive and personalized instructional services is the Adaptive Educational Hypermedia Systems (AEHS). AEHS attempt to personalize the learning experience for the * Corresponding author. Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Street, Piraeus, GR-18534, Greece. Email:
[email protected]
122 P. Karampiperis et al.
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learner (De Bra et al., 2004). This learner empowerment can help to improve learner satisfaction with the learning experience (Brusilovsky, 2001). The core component of AEHS is the Adaptation Model, which is responsible for setting the principles of instructional planning according to personal parameters, e.g. the cognitive style or learning preferences of the learners. Although some efforts have been reported in the literature aiming to update the adaptation logic used for a specific learner, by updating his/her profile through the use of complex questionnaires that estimate the cognitive characteristics of learners, still the cognitive profile used for a learner remains static for a significant period, leading to the same adaptation decisions independently from the previous interactions of the learner with the system. In this paper, we address the learning object selection problem influenced by learners’ cognitive characteristics, proposing a cognitive-based selection methodology that is dynamically updated based on the navigation steps of learners. The proposed approach uses learning technology specifications for user and content profiling, namely the IMS Learner Information Package (IMS LIP) specification (IMS, 2001) and the IEEE Learning Object Metadata (IEEE LOM) standard (LTSC, 2002). For modeling learner cognitive characteristics, we use the Cognitive Trait Model (Kinshuk & Lin, 2004), an approximation model for learner’s cognitive capacity that provides a concrete method for identifying learner’s cognitive characteristics. In our experiment we simulate different learner behaviors in navigating a hypermedia learning objects space, and measure the selection success of the proposed selection decision model as it is dynamically updated using the simulated learner’s navigation steps. The simulation results provide evidence that the proposed selection methodology can dynamically update the internal adaptation logic leading to refined selection decisions. The paper is structured as follows: initially, we set up our proposed approach as an extension to the generalized architecture of an adaptive educational hypermedia system. The second part presents the main elements of the Cognitive Trait Model, namely the working memory capacity and inductive reasoning ability. In this section we analyze each cognitive characteristic and present a set of pedagogical rules from the literature in order to create an initial decision model for learning object selection. The third part presents the proposed approach for dynamically updating the decision model for learning object selection according to the learner’s navigation steps and it constitutes the main contribution of this paper. Finally, we present simulation results of the proposed approach and discuss our findings and the conclusions that can be offered. The architecture of the cognitive-based selection In our previous work (Karampiperis & Sampson, 2005) we have presented the generalized architecture of adaptive educational hypermedia systems consisting of three main models, namely the User Model, the Domain Model and the Adaptation Model. In this paper we propose a modification of this generic architecture for supporting the cognitive-based selection of learning objects. The proposed approach is depicted in Figure 1. The key elements of this architecture are (1) the Cognitive Trait Model that analyses the learner’s navigational patterns in order to update the cognitive characteristics of the learner, stored in the User Model and (2) the LO (Learning Object) Selection Decision Model that constructs a suitability function which maps
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Adaptive cognitive-based selection of learning objects 123
Proposed approach for cognitive-based selection of learning objects
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Figure 1.
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learning object characteristics over learner characteristics and vice versa using tracked information on learners’ navigation steps. The innovation in the proposed approach is that real-life navigation steps can be used to determine both User Model characteristics and the LO Selection Decision Model, instead of selecting learning objects based on static rules that have been a priori defined at the design phase of the learning system. In the following sections we will analyze our proposed methodology for cognitive-based selection of learning objects.
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Figure 1. Proposed approach for cognitive-based selection of learning objects
Adaptation to learner’s cognitive characteristics Learners with different cognitive characteristics require different filtering of content to suit their learning needs. There is a wide range of cognitive characteristics, such as the Structure of Intellect Model (Guilford, 1967), bearing influence to one’s learning. Two of them, namely working memory capacity and inductive reasoning ability, are chosen in this study for two reasons— firstly, they are available from the Cognitive Trait Model, and secondly, they are highly relevant to learning which will be discussed hereinafter. We will describe these characteristics along with associated pedagogical adaptation possibilities, before discussing the cognitive-based selection of learning objects.
124 P. Karampiperis et al. Working memory capacity
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Working memory is the cognitive system that allows us to keep active a limited amount of information (roughly, 7 ± 2 items) for a brief period of time (Miller, 1956) to temporarily store the outcomes of intermediate computations during problem solving and to perform further computations on these temporary outcomes (Baddeley, 1986). The research on working memory (Huai, 2000; Kearsley, 2001) shows that the speed of learning, the memorization of learned concepts, the effectiveness of skill acquisition and many other learning abilities are all affected by the capacity of working memory which is mainly comprised of two components; the limited storage system, and the central execution unit carrying out the cognitive operation efforts. There are already many guidelines for designing learning systems written by human– computer interaction experts, devoted to addressing the relationship between the storage aspect of the working memory and good interface designs. The effort to facilitate learning with regard to working memory is mainly focused on the instructional design to assist the synchronized operation with the central execution unit by assisting the formation of higher-order rules, a building up of the mental model and of course not to overload the storage system of the working memory as suggested by the cognitive load theory (Sweller, 1998). The working memory is analysed below with respect to learning.
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When the working memory capacity of the learner is low. The number of paths and the amount of information presented to the learner should decrease to protect the learners from getting lost in the vast amount of information, and from overloading the working memory with complex hyperspace structure (cognitive load theory; Sweller, 1988; Kashihara et al., 2000). This would help to reduce the frustration experienced during learning, and allow more time for the learner to re-view essential content if necessary. The relevance of the information should increase to raise the possibility that the learners will get the most important information and thus increase the effectiveness of learning. The concreteness of the information should increase so the learner can grasp the fundamental rules first and use them to generate higher-order rules as suggested by the structured learning theory (Scandura, 1973). The structure of the information should stay unchanged for the following reason. The increase of structure-ness could facilitate the building of a mental model and assist future recall of the learned information. In the literature (Huai, 2000) it is indicated that the versatile learners tend to have smaller short-term memory (storage aspect of working memory) than serial learners, and the increase of structure-ness limits their navigational freedom, which is the primary way they learn. Therefore, the net effect cancels out and the structure of the information is recommended to stay unchanged. The number of the information resources should increase, so the learners can choose the media resources that work best alongside their cognitive styles and allow deeper understanding of the subject domain (Craik & Lockhart, 1972; Cronbach & Snow, 1989).
When the working memory capacity of the learner is high. The number of paths and the amount of information should increase and the relevance of the information should decrease to enlarge the exploration and domain space of the learning process so that more knowledge is available to
Adaptive cognitive-based selection of learning objects 125 Table 1.
Working memory capacity formalization
Path
Content
Level
Number
Relevance
Amount
Concreteness
Structure
No. of information resources
Poor Good
− \+
+ \−
− +
+ −
\ \
+ \
+, should increase; −, should decrease; \+, should slightly increase (recommend only), or could increase; \ −, should slightly decrease (recommend only), or could decrease.
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the learners who process more higher-order rules which ‘account for creative behaviour (unanticipated outcomes and accidental learning) as well as the ability to solve complex problems by making it possible to generate (learn) new rules’ (Kearsley, 2001). The concreteness of the information should decrease to increase learning efficiency and to avoid boredom for the learners resulting from too many examples for learned concepts. The structure of the information and the number of information resources should stay unchanged because there are no direct and apparent benefits associated. Table 1 summarizes the formalization of working memory capacity. It provides guidelines on how to adapt the six aspects of the learning environment (number of paths, relevance of paths, amount of content, concreteness of content, structure of content, and number of information resources) depending on whether the learner’s working memory capacity is poor or good. Pedagogical adaptation to working memory using IEEE LOM
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In order to provide tailored selection of learning objects to each individual learner, it is essential to match the properties of learning objects, according to their metadata specified in LOM, to the attributes of the learner. In this study, we focus on the working memory capacity of the learner, and categorize learners into groups of high working memory capacity (HWMC) and low working memory capacity (LWMC). The LOM metadata that are relevant to the pedagogical adaptation to working memory capacity are listed as follows: 5.1 5.3 5.4 5.8
Educational.InteractivityType Educational.InteractivityLevel Educational.SemanticDensity Educational.Difficulty
InteractivityType specifies the predominant mode of learning supported by a learning object. It can contain values of active, expositive, and mixed. Active learning objects require active involvement of the learner such as manipulating a simulation, finding a solution for an exercise, and answering a questionnaire. Expositive (passive) learning objects require only passive absorption of the information presented to the learner. Active learning objects impose extra information to be learned in order to induce meaningful productive action of the learner. Regardless of structural or operational perspective, active learning objects create greater load,
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hence occupying already limited working memory and reducing the channel/operational capacity and thus creating only a hindrance to the absorption of what needs to be learned. Therefore, learners in LWMC group are not suggested to have active learning objects. InteractivityLevel refers to the degree by which the learner can influence the aspect/behavior of the learning object. Learning objects with InteractivityType equal to ‘active’ can have low InteractivityLevel (e.g. an active simulation with few/no manipulatable parameters for a user to change simulation’s behavior) or high InteractivityLevel (e.g. an active simulation with many parameters). Likewise, expositive learning objects can have either low or high InteractivityLevel. Highly interactive learning objects offer additional different views to a learner so understanding can be constructed holistically. However, in order to absorb more than one view, extra cognitive processing-power/resources are required that could not be affordable for learners in the LWMC group. This line of reasoning is in accordance with the finding of Huai (2000) that learners with low working memory capacity do not learn holistically. SemanticDensity represents the degree of semantic conciseness of a learning object. A paragraph with high semantic density explains the same concept, with the same level of detail, in a short text-span than a semantically dilute one. As a result, it must employ a certain format to join up the pieces succinctly, and often results in complex and long sentences. Paragraphs and sentences are used as examples here because they are still very commonly used as a medium to teach, but the same would apply to other media as well. Reading a semantically dense sentence requires high cognitive processing. In order to understand the meaning of the whole sentence, a learner has to retrieve the entire sentence from memory at the end of reading it. Structural perspective of working memory (Baddeley, 1992) tells us that if pushed into more than what working memory can bear, it will overflow and confusion will ensue, whereas operational perspective (Salthouse & Babcock, 1991) says that if the pieces read earlier have already decayed, there is no way that the meaning of the whole sentence can be reconstructed when the reader reaches the end. From these arguments, our learning system would not suggest semantically dense learning objects to learners with LWMC. The difficulty of a learning object indicates how hard it is to work with the typical intended target audience. Structured learning theory (Scandura, 1973) suggests learning fundamental concepts before proceeding to higher-order concepts that are characterized to be more difficult. The rationale behind structured learning theory is that to process a familiar concept requires less cognitive load. Therefore, after familiarization with fundamental concepts, it requires aggregatively much less cognitive load to process a higher-order concept. Therefore, for learners with LWMC, our learning system schedules a path that guides them to master fundamental concepts before showing them learning objects that teach difficult concepts. The above discussion is summarized in Table 2. Inductive reasoning skill Induction is to figure out the rules/theories/principles from observed instances of an event, described as working opposite to deduction, moving from specific observations to broader generalizations and theories (Heit, 2000; William, 2001). It is a bottom-up approach and has an open-ended and exploratory nature.
Adaptive cognitive-based selection of learning objects 127 Table 2.
Initial Adaptation Model based on working memory capacity Working memory capacity
InteractivityType InteractivityLevel SemanticDensity Difficulty
Low
High
Expositive Very low, low Very low, low Very easy, easy
Active Very high, high Very high, high Very difficult, difficult
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The research on inductive reasoning skill shows that the higher the inductive reasoning ability, the easier it is to build up the mental model of the information learned (Salthouse & Babcock, 1991; Baddeley, 1992). Mental model, also called cognitive structure, ‘provides meaning and organization to experiences and allows the individual to go beyond the information given’ (Kearsley, 1998). From the constructivist’s point of view, the learner’s selection and transformation of information, constructs, hypotheses, and the decision process, all rely on the mental model (Bruner, 1973). For students who possess better inductive reasoning skill, it is easier for them to recognize a previously known pattern, generalize higher-order rules and as a result, the load on working memory is reduced and the learning process is more efficient. For its educational value, there is a need to specify means to support those with lower inductive reasoning skill and to maximize the learning for those who are already good at induction. The following discussion analyses the effect of poor and good inductive reasoning skill on the learning process.
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When the learner’s inductive reasoning skill is poor. The number of paths in the learning system should increase to give the learner more opportunity for observation and thus promote induction. The relevance of paths should decrease whereas the concreteness of the information should increase so the learner can have more diverse observations to increase the possibility of induction. The amount of information should increase to give detailed and step-by-step explanation to the learners, so they can see the rules/theories easier without the attempt to generate and test hypotheses by their own efforts. This could increase the learning effectiveness for learners who are not good at induction. The structure of the information should increase so that it is easier for the learner to build up the mental model and see the sequential relationship of the topics and relationship of concepts. The number of information resources does not need to change because there are no direct and apparent benefits associated. When the learner’s inductive reasoning skill is good. The number of paths and the amount of the information should decrease to increase the efficiency of the learning process. The concreteness of the information should also decrease to avoid the boredom resulting from too many examples while the learner can already learn from an abstract means of presentation. The structure of the information, the number of information resources and relevance of paths do not need to change because there are no direct and apparent benefits associated. Table 3 summarizes the formalization of inductive reasoning skill.
128 P. Karampiperis et al. Table 3.
Inductive reasoning skill formalization
Path
Content
Level
Number
Relevance
Amount
Concreteness
Structure
No. of information resources
Poor Good
\+ \−
\− \
+ −
+ \−
+ \
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+, should increase; −, should decrease; \+, should slightly increase (recommend only), or could increase; \ −, should slightly decrease (recommend only), or could decrease.
Pedagogical adaptation to induction using IEEE LOM
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Learners with low inductive reasoning skill are suggested to have expositive learning objects because they are less likely to induce, and hence understand, the concepts from interacting with active learning objects. Hulshof (2001) describes that learners of this type will struggle and remain in experiment space (e.g. a simulation to explain dual-planet rotation), and very unlikely go to hypothesis space (i.e. understanding of the principles of dual-planet dynamics). For the similar reason, lowly-interactive learning objects should be given to learners with low inductive reasoning skill, and highly-interactive ones to learners with high inductive reasoning skill. High-order rules in structured learning theory (Scandura, 1973) correspond to mental models in the deeper level (more specific) of the default hierarchy (Holland et al., 1987). They are usually based on mental model(s) in the upper level (more general). It would be improbable, if not impossible, that a learner can learn difficult/advanced concepts without knowing the fundamentals. Learners with low inductive reasoning skill are not good at grasping common patterns in instances and therefore are slower in building their default hierarchy than learners with high inductive reasoning skill. In other words, learners with low inductive reasoning skill build the foundation to learn more advanced concepts slower, and thereby they should be suggested to take learning objects with a low difficulty level. SemanticDensity of a learning object is related to memory, as explained in the previous section, but is not relevant to various levels of inductive reasoning skill. Therefore, no suggestion is made for selecting learning objects with different semantic density based on inductive reasoning skill. The above discussion is summarized in Table 4.
Table 4.
Initial Adaptation Model based on inductive reasoning ability Inductive reasoning ability
InteractivityType InteractivityLevel SemanticDensity Difficulty
Low
High
Expositive Very low, low – Very easy, easy
Active Very high, high – Very difficult, difficult
Adaptive cognitive-based selection of learning objects 129 Cognitive-based learning object selection Typically, the design of adaptive learning systems requires a huge set of rules, since dependencies between educational characteristics of learning objects and learners are rather complex (Karampiperis & Sampson, 2005). This complexity introduces several problems on the definition of the rules required (Wu & De Bra, 2001), namely: ● ● ●
Inconsistency, when two or more rules are conflicting. Confluence, when two or more rules are equivalent. Insufficiency, when one or more rules required have not been defined.
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Furthermore, in adaptive learning systems the cognitive profile used for a learner remains static for a significant period, due to the fact that it is typically built based on complex questionnaires that estimate the cognitive characteristics of learners, and thus, its update would require significant overload. As a result, the system’s adaptation logic is not frequently updated and leads to the same selection decisions independently from the previous interactions of the learner with the system. The proposed cognitive-based selection methodology is based on a decision-making mechanism that instead of using predefined static selection rules, produces a decision model for the selection of learning objects that is dynamically updated based on the navigation steps of learners within a space of hypermedia objects. To do so, we have used a suitability function presented in our previous work (Karampiperis & Sampson, 2005) that maps learning object characteristics over learner characteristics and vice versa. In this paper, for the calculation of the above-mentioned function we use preference rating information directly extracted from simulated learner navigation steps, under the assumption that the learners’ cognitive characteristics and preferences stored in the User Model, as well as the structure of the Educational Resource Description Model, have already been defined. More specifically, for the elements of the User Model we use the Cognitive Trait Model elements mapped to elements of the IMS LIP specification (Table 5), and for the elements of the Educational Resource Description Model we use a sub-set of the IEEE LOM standard elements (Table 6). Simulation results For our simulations we created two sets of learning object metadata records, called Learning Object Training Set (LOTS) and Learning Objects Estimation Set (LOES). The LOTS is used for generating the selection decision model and contains metadata records for 250 simulated learning objects with normal distribution over the value space of each metadata element. The LOES is used for measuring the efficiency in selecting resources which are external from the Table 5.
User Model elements used
Category
IMS LIP path
Explanation
Accessibility
LIP/Accessibility/Preference/WMC LIP/Accessibility/Preference/IRA
The working memory capacity of a learner The inductive reasoning ability of a learner
130 P. Karampiperis et al. Table 6.
Resource Description Model used
Category
IEEE LOM element
Explanation
General
Structure Aggregation_Level InteractivityType InteractivityLevel
Underlying organizational structure of a learning object The functional granularity of a learning object Predominant mode of learning supported by a learning object The degree to which a learner can influence the behavior of a learning object The degree of conciseness of a learning object How hard it is to work with or through a learning object Typical time it takes to work with or through a learning object Specific kind of learning object
Educational
N
SemanticDensity Difficulty TypicalLearningTime LearningResourceType
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reference set of the decision model. This set consists of 25,000 metadata records of simulated learning objects, again with normal distribution over the value space of each metadata element. Additionally, the learning object metadata records have been classified, for both testing and estimation sets, in two classes according to their aggregation level. The classification is based on the value space of the ‘General/Aggregation_Level’ element of the IEEE LOM standard. The LOTS contains 125 metadata records with aggregation level 1 and 125 metadata records with aggregation level 2. The LOES contains 12,500 metadata records with aggregation level 1 and 12,500 metadata records with aggregation level 2. In our experiment we simulated different learner behaviors in navigating a hypermedia learning objects space, as learning object rating sequences produced by randomly generated adaptation models. The adaptation model used for each simulated learner is calculated using random variations of adaptation decisions presented in Tables 2 and 4. Each simulated navigational sequence, contains n number of learning objects. In our simulations, we intend to measure the selection success for different values of navigational sequence length (n). To this end, we have defined an evaluation criterion, as follows: CorrectLearningObjectsSelected Success (%) = 100 × n
where n is the number of the desired learning objects from the Media Space, that is equivalent to the number of learning objects contained in a navigational sequence. Figure 2 presents the average success of the selection decision model when a learner is acquiring a list of learning objects containing a maximum of 20 (n = 20) and 50 (n = 50) learning objects, respectively, from the Learning Object Estimation Set. The feedback loops in this figure express how many times the simulated learner navigational steps have been used to train the selection decision model. These results indicate that the proposed approach can dynamically update the decision model used for the selection of learning objects according to the learner’s navigation steps. From these results we conclude that the selection success rate of the resulting learning objects is depending on the number of learning objects contained in a navigational sequence. The lower the number of resources that are requested, the lower the probability of
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Average LO Selection Success per Num. of Feedback Loops 100
90 Num of LOs in Navigational Sequence
85 80
n=20 n=50
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selection mismatches. Additionally, the more times the learner navigational steps are used to train the selection decision model, the more efficient is the selection. If we consider the different combinations of learning objects and learner instances calculated as the multiplication of the value instances of characteristics presented in Tables 5 and 6, these lead to more than 50,000 combinations. It is therefore evident that it is almost unrealistic to assume that an instructional designer can manually define the full set of selection rules required for adaptive content selection. The simulation results provide evidence that the proposed selection methodology can dynamically update the internal adaptation logic leading to refined selection decisions. In order to investigate in more detail these results, we have evaluated the selection success after 10 feedback loops, for both the training set and the estimation set of learning objects. Additionally, we have evaluated the selection success for the two different classes of each set, according to the aggregation level of learning objects. Figures 3 and 4 present average simulation results for learning object selection, using learning objects with aggregation levels 1 and 2, respectively. The results presented in Figures 3 and 4 show that when the desired number of learning objects (n) is relatively small (less than 20), the selected learning objects by the refined decision model are almost similar to those the learner would select. On the other hand, when the desired number of learning objects is relatively large (about 50), the success of the selection is affected, but remains at an acceptable level (about 97%).
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Figure 2. Average evolutionary selection success for learning objects (LOs) in estimation set
Figure 4. 3. Average simulation results for learning objects (LOs) with aggregation level 21 after 10 feedback loops
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Average simulation results for learning objects (LOs) with aggregation level 1 after 10 feedback loops
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Figure 4.
Average simulation results for learning objects (LOs) with aggregation level 2 after 10 feedback loops
Adaptive cognitive-based selection of learning objects 133 A critical parameter affecting the selection success has proved to be the granularity of learning objects. Granularity mainly affects the capability of a learner to express selection preferences over learning objects. Learning objects with small aggregation level have bigger possibilities of producing ‘gray’ decision areas, where the learner cannot decide which learning object matches the most with his/her cognitive style or learning preferences. In our simulations, learning objects with aggregation level 2, which can be small, or even bigger collections of learning objects with aggregation level 1, appear to have less possibility of producing indifference relations, enabling us to make more secure selection decisions. Conclusions and future research
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In this paper, we address the learning object selection problem based on learners’ cognitive characteristics, proposing a cognitive-based selection methodology that is dynamically updated based on the navigation steps of learners in sets of hypermedia objects. The proposed approach uses learning technology specifications for user and content profiling, namely the IMS Learner Information Package (IMS LIP) specification and the IEEE Learning Object Metadata (IEEE LOM) standard. For modeling learner cognitive characteristics, we use the Cognitive Trait Model, that is, an approximation model for learner’s cognitive capacity that provides a concrete method for identifying learner’s cognitive characteristics based on learners’ navigation steps. In our experiment we simulate different learner behaviors in navigating a hypermedia learning objects space, and measure the selection success of the proposed selection decision model as it is dynamically updated using the simulated learner’s navigation steps. The simulation results provide evidence that the proposed selection methodology can dynamically update the internal adaptation logic leading to refined selection decisions. Future research includes the incorporation of other working memory capacity and inductive reasoning skill elements in the cognitive profile of learners, as well as the investigation of correlations between these elements (Dodero et al., 2004) and their effect on selecting learning objects.
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Acknowledgements
The work presented in this paper is partially supported by the European Community under the Information Society Technologies (IST) programme of the 6th FP for RTD (project ICLASS contract IST-507922). Notes on contributors Pythagoras Karampiperis, holds a Diploma (2000) and MSc on Electronics and Computer Engineering (2002) and an MSc on Operational Research (2002), all from the Technical University of Crete, Greece. Currently, he is a PhD candidate at the Department of Technology Education and Digital Systems, of University of Piraeus, Greece and a Member of the Advanced Electronic Services for the Knowledge Society Research Unit (ASK) at the Informatics and Telematics Institute (ITI) of the Center of Research and Technology Hellas (CERTH). His main scientific interests are in the areas of Technology-enhanced Learning. He is the co-author of more than 30 publications in scientific books, journals and
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conferences with at least 27 citations. He received the Best Paper Award in IEEE International Conference on Advanced Learning Technologies ICALT04, Joensuu, Finland (August 2004). He is a Student Member of IEEE, a Member of the International Forum of Educational Technology and Society (ETS) and the Technical Chamber of Greece. Taiyu-Lin is in the Advanced Learning Technologies Research Centre, Massey University, Palmerston North, New Zealand. Demetrios G. Sampson holds a Diploma in Electrical Engineering (1989) from Demokritus University of Thrace and Ph.D. in Multimedia Communications (1995) from University of Essex, UK. He is the Head of the Advanced eServices for the Knowledge Society Research Unit (ASK) at the Informatics and Telematics Institute (ITI) of the Center of Research and Technology Hellas (CERTH) and an Assistant Professor of eLearning at the Department of Technology Eduction and Digital Systems of the Univerity of Piraeus. He is also the Vice Chairman of the IEEE Computer Society Technical Committee on Learning Technology. His main scientific interests are in the areas of Technology-enhanced Learning. He is the co-author of more than 160 publications in scientific books, jounrals and conferences with at least 185 citations. He received the Best Paper Award in IEEE International Conference on Advanced Learning Technologies ICALT01, Madisson, USA (August 2001) and in IEEE International Conference on Advanced Learning Technologies ICALT04, Joensuu, Finland (August 2004). He is a Senior Member of IEEE, Co-Editorin-Chief of the Educational Technology and Society Journal, and a Member of the Editorial Board of eight (8) International Journals. Kinshuk is Director of the Advanced Learning Technology Research Centre at the Massey University, New Zealand. He is also Associate Professor of Information Systems. He has been involved in large-scale research projects in adaptivity in e-learning and has published over 115 research papers in international refereed journals, conferences and book chapters. He is currently chairing the IEEE Technical Committee on Learning Technology, New Zealand chapter of ACM SIGCHI, and the International Forum of Educational Technology & Society. He is also editor of the Journal of Educational Technology & Society and Learning Technology Newsletter. He is an editorial board member of more than a dozen international journals and a programme committee member of several conferences in the educational technology area every year. References Baddeley, A. D. (1986) Working memory (Oxford, Oxford University Press). Baddeley, A. D. (1992) Working memory, Science, 255, 556–559. Bruner, J. (1973) Going beyond the information given (New York, Norton). Brusilovsky, P. (2001) Adaptive hypermedia. Methods and techniques of adaptive hypermedia, International Journal of User Modeling and User-adapted Interaction, 11(1/2), 87–110. Craik, F. & Lockhart, R. (1972) Levels of processing: a framework for memory research, Journal of Verbal Learning & Verbal Behavior, 11, 671–684. Cronbach, L. & Snow, R. (1989) Aptitude–treatment interaction as a framework for research on individual differences in learning, in: P. Ackerman, R. J. Sternberg & R. Glaser (Eds) Learning and individual differences (New York, W.H. Freeman), 13–60. De Bra, P., Aroyo, L. & Cristea, A. (2004) Adaptive web-based educational hypermedia, in: M. Levene & A. Poulovassilis (Eds) Web dynamics, adaptive to change in content, size, topology and use (New York, Springer), 387–410. 60
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