Makis Leontidis

Integrating Learning Styles and Personality Traits into an Affective Model to Support Learner's Learning

The aim of this paper is to present a model in order to integrate the learning style and the personality traits of a learner into an enhanced Affective Style which is stored in the learners model. This model which can deal with the cognitive abilities as well as the affective preferences of the learner is called Learner Affective Model (LAM). The LAM is used to retain learners knowledge and activities during his interaction with a Web-based learning environment and also to provide him with the appropriate pedagogical guidance. The proposed model makes use of an ontological approach in combination with the Bayesian Network model and contributes to the efficient management of the LAM in an Affective Module.

e-Learning Issues under an Affective Perspective

The aim of this paper is to present an Affective Educational Module for distance learning which is called MENTOR. MENTOR constitutes of three main components, the Emotional Component, the Teacher Component and the Visualization Component and its main purpose is to motivate appropriately the student in order to accomplish his learning goals. The basic concern of MENTOR is to retain the students emotional state positive during the learning process. To achieve this, it recognizes the emotions of the students and takes them under consideration to provide them with the suitable learning strategy. This kind of strategy is based both on the cognitive abilities and the affective preferences of the student and is stored in the students model. The student model supplies the educational system with necessary information with the aim to adapt itself successfully to the students needs.

Using an affective multimedia learning framework for distance learning to motivate the learner effectively

The aim of this paper is to present the affective multimedia learning framework of the MENTOR as well as the web and multimedia technologies which were used for its implementation. The MENTOR is an affective web-based adaptive educational system for distance learning. The basic concern of MENTOR is to retain the student|s emotional state positive during the learning process. To achieve this, MENTOR incorporates an affective module which enhances the traditional learning practices with an affective multimedia dimension. The foremost and endmost goal of MENTOR is to provide the learner with a more personalised and friendly multimedia environment for learning, according to his personality, mood and emotions.

Supporting Learner's Needs with an Ontology-Based Bayesian Network

This paper presents a study of MENTOR, a Web Adaptive Educational Environment (WBES), where the learner’s needs and preferences are diagnosed using an Ontology-based Bayesian Network approach during the learning process. Firstly, the proposed method uses an OWL Ontology to store the Affective Knowledge regarding the learner. Then, this Ontology is extended appropriately to deal with uncertainty so that a Bayesian Network (BN) can be constructed from it. Finally, using the derived BN we can make inferences and reasoning on the learner’s individual needs. In this way we form a schema where the uncertain Affective Information (AfI) can be represented efficiently and exploited properly in order to maintain the efforts of a learner. Therefore, based on this model we can detect the learner’s affective model and to support his efforts during the learning process by suggesting the proper pedagogical guidance.

MENTORing Affectively the Student to Enhance his Learning

In this paper a Web-Based Adaptive Educational System to support personalized distance learning, which is named MENTOR is presented. The main purpose of MENTOR is to support learner’s actions during the learning process in an affective way. To achieve this MENTOR incorporates an affective module which enhances the traditional learning practices with an affective dimension. The affective module makes use of an ontological approach in combination with the Bayesian Network model in order to provide learner with the properly affective guidance. In this way the foremost goal of MENTOR, which is to supply the learner with a personalized and emotional awareness learning environment, is achieved.

An ontological approach to infer student's emotions

This paper presents a method which is based on an Ontological Approach in combination with the Bayesian Network (BN) model in order to elicit students emotion during the learning process. The produced Ontology serves as a basis for the formal representation of emotions and it is stored in the Learner Affective Model (LAM). The use of BNs contributes to the identification of students affective state and deals with affective information (emotions, personality) which involves uncertainty. The proposed method is exploited by an Affective Module of a Web-Based Adaptive Educational System, which is called MENTOR, to support personalized distance learning.