Dimitris Georgiou

Identification of learning styles supports Adaptive Educational Hypermedia Systems compiling and presenting tutorials custom in cognitive characteristics of each individual learner. This work addresses the issue: identifying the learning style of students, following the Kolb’s learning cycle. To this purpose, we propose a three-layers Fuzzy Cognitive Map (FCM) in conjunction with a dynamic Hebbian rule for learning styles recognition. The form of FCMs is designed by humans who determine its weighted interconnections among concepts. But the human factor may not be as reliable as it should be. Thus, a FCM model of the system allowing the adjustment of its weights using additional learners’ characteristics such as the Learning Ability Factors. In this article, two consecutively interconnected FCM (in the form of a three layer FCM) are presented.  The schema’s efficiency has been tested and compared to known results after a fine-tuning of the weights of the causal interconnections among concepts. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. The online recognition of learning styles by using three-layer Fuzzy Cognitive Map improves the accuracy of recognition obtained using Bayesian Networks that uses quantitative measurements of learning style taken from statistical samples. This improvement is due to the fuzzy nature of qualitative characterizations (such as learning styles), and the presence of intermediate level nodes representing Learning Ability Factors. Such factors are easily recognizable characteristics of a learner to improve adjustment of weights in edges with one end in the middle-level nodes.  This leads to the establishment of a more reliable model, as shown by the results given by the application to a test group of students.

The present contribution demonstrates the design and development of the
educational multimedia presentetion with the title �Genesis and Architectural Types of the Orthodox Temples
�for the teaching of Religious Education in Secondary Schools in Greece. The aim for the
development of this tool is to offer the secondary school students a computer supported
learning environment suitable for the representation of religion subject and moreover,
to lead them to the formulation of arguments and reasoning contexts for constructing their
own basic concepts in the thematic area of the Temples. It is a multimedia cross-reference
educational application supported by databases of sources, instructional scenarios and
activities.  The students in order to accomplish their tasks they have to search the
content of the multimedia representation and choose the correct information. Alternatively,
students can look for information on the
internet (predifined URLs appear on the screen, whenever it is appropriate). The multimedia presentetion,
in order to facilitate the students find the
information, includes a detailed Flow Chart and a Glossary which contains all the unknown
terms that appear in the texts and further additional information. The multimedia presentetion
proposes the students a number of activities so as to help them express their views and
test their ideas. It provides a variety of tools for sources elaboration and for the study of chosen topics of knowledge domain through
scenarios, activities and tasks incorporated in the software. The students can make
connections related to the shape of the temples and their symbolic meaning. Their answers
are saved in a file on the hard disk. The development of the software is based on modern
educational principles and suggests "teaching with the advandages of using multimedia"
This paper presents the application, its educational scope, the design and development
methodology as prescribed by the pedagogical approach, the way of usage, the target group,
the educational material used, the architectural design of the software and  the functional characteristics of the software

Aiming to design an AEHS component capable to take advantages from the user’s learning profile recognition in order to provide the best possible course presentation, we made use of a suitable p-timed Petri net. Learning Style (LS) is one of the learning profile components.

Research in the area of Adaptive Educational Hypermedia Systems is aiming to develop sophisticated methods for adaptive content presentation. The educational content forms a sequence of Learning material presentations that are collected from a distributed over the net Learning Object repositories and are presented adaptively to every isolated user. Here, adaptive content presentation is considered in terms of i) the user’s style, ii) the user’s knowledge background monitoring and iii) the user educational needs. To the purpose of establishing a flexible structure for content adaptive presentation we propose a P- timed Petri Net with inhibitor edges A path that has been ran by a token represents the optimum flow of learning content tailored to the cognitive and learning needs of each learner.

Adaptation and personalization of the information and instruction offered to the users -individuals or groups- in on-line e-learning environments are considered to be the turning point of recent research efforts. The “one-size-fits-all” approach has some important drawbacks, from the educational point of view. Adaptive Educational Hypermedia Systems (AEHSs) in World Wide Web became a very active research field and the need of standardization of user assessment, instruction material and e-learning environments arose, as the continually augmenting research efforts lacked the interoperability dimension. In this chapter, authors classify some basic assessment aspects (adaptivity parameters) of an AEHS user, extracted from research work that refers to commonly accepted e- learning standards, such as SCORM. Also, authors provide a starting point for the development of a generic architecture for the retrieval of standardized Learning Objects (LOs) from disperse Learning Objects Repositories (LORs) to an e-learning environment, which can support collaboration activities. The retrieved LOs will comply with user assessment stored in the user model.

Adaptation and personalization of the information and instruction offered to the users in on-line e-learning environments are considered to be the turning point of recent research efforts. Collaborative learning may contribute to adaptive and personalized asynchronous e-learning. In this chapter authors intend to introduce the Virtual co Learner (VcL) that is a system designed on a basis of distributed architecture able to imitate the behavior of a learning companion who has suitable to the user’s cognitive and learning style and behavior. Matching human and simulated learners who have cognitive characteristics of the same type, one can find that learning procedure becomes more efficient and productive. Aiming to establish such VcL, one faces a number of questions. An important question is related to the user’s cognitive or learning characteristics diagnosis. Authors of this chapter have published work concerning learning style diagnosis based on Bayesian networks, and Fuzzy Cognitive Maps. Both are based on David Kolb’s learning cycles.  Other questions concern the VcLs’ responses and reactions to learner, the evaluation of VcL’s influence to the learning procedure, and the optimization of VcL’s actions. Adaptive Educational Hypermedia Systems (AEHSs) in World Wide Web became a very active research field and the need of standardization of user assessment, instruction material and e-learning environments arose, as the continually augmenting research efforts lacked the interoperability dimension. VcL design takes under consideration such issues aiming to contribute to AEHS efficiency

In order to establish an online diagnostic system for Learning Style Estimation that contributes to the adaptation
of learning objects, we propose an easily applicable expert system founded on Bayesian Networks. The
proposed system makes use of Learning Style theories and associated diagnostic techniques, simultaneously
avoiding certain error sources. It takes under consideration learners’ answers to a specific questionnaire, as well
as a classification of learners who have been previously examined. System application based on Kolb’s Learning
Style Inventory is also presented, in order to study the changes to the final estimation made by the system. The
questionnaire in use is the David Kolb’s Learning Style Inventory revised, an on-line form which avoids logical
fault implications. With this aim, a case study has been carried out involving 885 university students, from
which conclusions are drawn. As a result of the expert system use, the influence of factors, such as reflection of
cross-cultural environment differences and/or of slippery or lucky answers’ influence on learning style
estimation is reduced.

Adaptation and personalization services in e-learning environments are considered the turning point of recent
research efforts, as the “one-size-fits-all” approach has some important drawbacks, from the educational point of
view. Adaptive Educational Hypermedia Systems in World Wide Web became a very active research field and the
need of standardization arose, as the continually augmenting research efforts lacked interoperability capabilities.
This paper concentrates and classifies recent research work and notices important points that can lead to an open,
modular and generic architecture of a Learning Management System based on widely accepted standards.

In Adaptive Educational Hypermedia Systems,
among other parameters, the user’s Learning Style plays a
crucial role in effective on-line asynchronous learning.
Cognitive psychology provides tools, such as questionnaires,
that can monitor user’s learning style. In this paper we
introduce an adjustable tool for Learning Style recognition. It
is built upon a well known and generally accepted Learning
Style Inventory and applies a three Layer Fuzzy Cognitive Map
Schema, which allows experts of cognitive psychology or
experienced educators to tune up the system’s parameters to
adjust the accuracy of the learning style recognition.

Adaptation and personalization services in e-learning environments are considered the turning point of recent research efforts, as the “one-size-fits-all” approach has some important drawbacks, from the educational point of view. Adaptive Educational Hypermedia Systems in World Wide Web became a very active research field and the need for standardization arose, as the continually augmenting research efforts lacked the interoperability dimension. To this end, we propose an adaptive hypermedia educational system architecture strongly coupled to existing standards that overcomes the above mentioned weakness. Part of such architecture is the development of diagnostic tools capable to recognize certain learner’s characteristics to the purpose of providing learning material tailored to the learner’s specific needs in an asynchronous learning environment. This paper describes Learning Style diagnosis which can be approached either by the use of probabilistic expert systems or by the use of fuzzy systems.

Adaptive Educational Hypermedia Systems among the emerging technologies serves Knowledge and Learning Systems. Aiming to improve pervasiveness and efficiency of asynchronous e-learning we give emphasis to the development of diagnostic tools capable to recognize certain learner’s characteristics to the purpose of providing learning material tailored to the learner’s specific needs. Learning Style diagnosis can be approached either by the use of probabilistic expert systems or by the use of fuzzy expert systems. In order to establish a probabilistic Learning Style’s diagnostic expert system we propose a method based on Bayesian Networks. Our system analyzes information supplied by the system’s descendant users, as well as providing a stochastic analysis of the problem. It is a system that utilizes what appear to be reasoning capabilities so as to reach Learning Style’s estimations. In order to classify learners in a predefined set of classes, the proposed system takes under consideration learners’ answers to a certain questionnaire, as well as a classification of learners who have been previously examined. As it was expected, our method takes advantage of previously accumulated knowledge and proves to be more accurate than Learning Style’s direct estimation, i.e. an estimation based only on user responses. Further to this, we propose a Fuzzy Cognitive Maps technique, which modifies the Learning Style into appropriate quantitative characteristics. The importance of Fuzzy Cognitive Map’s technique becomes even greater since this method takes into consideration the previously gained experience on learners’ style diagnoses.

We examine the oscillatory behavior of the solutions of the n-order neutral delay differential equation                   

[y(t)+P(t)y(t-τ)]^(n)+Q(t)y(t-σ)=0

where P,Q are real continuous functions for t>to and delays τ and σ are nonnegative real numbers. Moreover, we improve and extend  known resultsby relaxing hypotheses that P is constant and Q is τ-periodic.