Konstantina Chrysafiadi

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner’s knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner’s knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

In this paper, we evaluate the effectiveness and accuracy of the student model of a web-based educational environment for teaching computer programming. Our student model represents the learners knowledge through an overlay model and uses a fuzzy logic technique in order to define and update the students knowledge level of each domain concept, each time that s/he interacts with the e-learning system. Evaluation of the student model of an Intelligent Tutoring System (ITS) is an aspect for which there are not clear guidelines to be provided by literature. Therefore, we choose to use two well-known evaluation methods for the evaluation of our fuzzy student model, in order to design an accurate and correct evaluation methodology. These evaluation models are: the Kirkpatricks model and the layered evaluation method. Our system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus, in order to learn how to program in the programming language C. The results of the evaluation were very encouraging.

Dynamically Personalized E-Training in Computer Programming and the Language C

This paper describes ELaC, a fully implemented and evaluated novel integrated environment for personalized e-training in programming and the language C. Software development relies on many different programming languages and tools, ranging from procedural to object-oriented and query languages; an individual learning a new language may already know a range of other languages, or may know no other languages at all. Given the variety of backgrounds of prospective learners of programming, developing learning environments for all of them is not easy. In the light of these problems, this work has focused on the development of an original integrated e-training environment for programming and the language C, incorporating a student model responsible for identifying and updating the students knowledge level, which takes into account each individual users pace of learning. The system can adapt dynamically to each individual learners needs by scheduling the sequence of learning lessons on the fly. This personalization allows each learner to complete the e-training course on at their own pace and according to their ability.

Modeling Student’s Knowledge on Programming Using Fuzzy Techniques

In this paper we describe the student modeling component of a web-based educational application that teaches the programming language Pascal using fuzzy logic techniques. To build a student model we have to diagnose the needs, misconceptions and cognitive abilities of each individual student. However, student diagnosis is fraught with uncertainty, and one possible approach to deal with this is fuzzy student modeling. Thus, we choose fuzzy logic techniques to describe student’s knowledge level and cognitive abilities. Furthermore, we use a mechanism of rules over the fuzzy sets, which is triggered after any change of the students’ knowledge level of a domain concept, and update the students’ knowledge level of all related with this concept, concepts.

Advances in Personalized Web-Based Education

This book aims to provide important information about adaptivity in computer-based and/or web-based educational systems. In order to make the student modeling process clear, a literature review concerning student modeling techniques and approaches during the past decade is presented in a special chapter. A novel student modeling approach including fuzzy logic techniques is presented. Fuzzy logic is used to automatically model the learning or forgetting process of a student. The presented novel student model is responsible for tracking cognitive state transitions of learners with respect to their progress or non-progress. It maximizes the effectiveness of learning and contributes, significantly, to the adaptation of the learning process to the learning pace of each individual learner. Therefore the book provides important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems.

Fuzzy Logic in Student Modeling

The significant development of the e-learning systems has changed the ways of teaching and learning. In nowadays, everyone can have access to e-learning systems from everywhere. Therefore, the e-learning systems have to adapt the learning material and processes to the needs of each individual learner. However, learning and student’s diagnosis are complex processes, which deal with uncertainty. A solution to this is the use of fuzzy logic, which is able to deal with uncertainty and inaccurate data. This chapter explains how fuzzy logic can be used to automatically model the learning or forgetting process of a student, offering adaptation and increasing the learning effectiveness in Intelligent Tutoring Systems. In particular, it presents a novel rule-based fuzzy logic system, which models the cognitive state transitions of learners, such as forgetting, learning or assimilating. The operation of the presented approach is based on a Fuzzy Network of Related-Concepts (FNR-C), which is a combination of a network of concepts and fuzzy logic. It is used to represent so the organization and structure of the learning material as the knowledge dependencies that exist between the domain concepts of the learning material.

Student Modeling for Personalized Education: A Review of the Literature

The rapid development of computer technology and e-learning reinforces the need of dynamic adaptation to the needs of each individual student. Adaptation is performed through the student model, which is a crucial module of an Intelligent Tutoring System. There are many student modeling techniques and approaches. In this chapter, a review of the literature concerning student modeling during the past decade is presented. The aim is to answer the three basic questions on student modeling: what to model, how and why. This chapter presents comparative tables that are the results of a 10-year review study in student modeling. They reveal either the most common modeled student’s characteristic, or the student modeling approaches that are preferred in relation to student modeling characteristics. So, the particular chapter can be, also, used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system.

Authoring Technological and Platform Independent Learning Material and Student’s Progress Profile Using Web Services

Nowadays the rapid development of computer and Internet technologies in the field of education has changed the ways of teaching and learning. The production of adaptive educational applications and systems that enrich the tutoring and learning processes with “intelligence”, in order to adapt either the learning material or the tutoring and learning processes to each individual student’s needs and abilities, offering her/him a personalized learning experience. However, building an adaptive educational environment and/or application is difficult and complex, since either technical knowledge and programming skills or the expertise of tutors are demanded. In this paper an authoring tool, which offers the instructors the possibility to create learning material and student’s profile that are technological and platform independent, is presented. The innovative operation of the presented authoring tool is based on web services and is due to the following facts: (i) the created learning content can be used by any educational application regardless of the system’s technological characteristics or programming language, (ii) the created progress profile of a student, who uses two different educational applications that call the same web services of the authoring tool, is recognized by both applications. Therefore, if the knowledge level of a student is changing during her/his interaction with the one application, then the other application will, also, recognize the updated knowledge level of the particular student.

Machine Learning and Fuzzy Logic Techniques for Personalized Tutoring of Foreign Languages.

Intelligent computer-assisted language learning employs artificial intelligence techniques to create a more personalized and adaptive environment for language learning. Towards this direction, this paper presents an intelligent tutoring system for learning English and French concepts. The system incorporates a novel model for error diagnosis using machine learning. This model employs two algorithmic techniques and specifically Approximate String Matching and String Meaning Similarity in order to diagnose spelling mistakes, mistakes in the use of tenses, mistakes in the use of auxiliary verbs and mistakes originating from confusion in the simultaneous tutoring of languages. The model for error diagnosis is used by the fuzzy logic model which takes as input the results of the first or the knowledge dependencies existing among the different domain concepts of the learning material and decides dynamically about the learning content that is suitable to be delivered to the learner each time.

A Novel Hybrid Student Model for Personalized Education

The goal of each web-based educational system is to offer effective learning such as real-classroom education and further more. To achieve this goal, the web-based educational system has to adapt dynamically to each individual student’s needs and preferences. A solution to this is the student model, which allows the understanding and identification of each individual student’s needs. In this chapter a novel student model, which is called F.O.S., is presented. F.O.S. combines three different student modeling approaches. It combines an overlay model with stereotypes and a rule-based mechanism. Furthermore, F.O.S. has been fully implemented in a web-based educational application, which teaches the programming language ‘C’. The particular hybrid student model allows each individual learner to complete the training program in her/his own learning pace and abilities.