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An Affective Model for Personalized Learning in Adaptive Educational Hypermedia Systems

The educational process is a complicated interaction between the teacher, the student and the environment where it takes place. It is important for a successful educational transaction for the teacher to recognize the learning and emotional needs of his student and to adapt the teaching and his behavior accordingly. In this poster we present an affective model, which augments an Adaptive Educational Hypermedia System (AEHS), with affective dimensions. This model correlates the individual preferences of a student with his personality and his emotional states. For this purpose, the model recognizes these affective factors and adapts accordingly its teaching and pedagogical strategy. The AEHS are intelligent systems that improve student’s performance by adapting their operation according to the student’s needs and interests supporting them with the appropriate learning strategy. An AEHS interacts dynamically with the student, using adaptation techniques like adaptability and adaptivity [3] and uses the student model, the domain knowledge, and the teaching model to achieve the adaptation of the system via the adaptive engine [2]. Thereby, an AEHS determines the educational content and the teaching process in a way that appertains the teaching in a real classroom. In a live educational process, the teacher takes into consideration the emotional state of his student by motivating him effectively in order to achieve the desirable learning goals. Many researchers have demonstrated the pedagogical value of emotions and the personality of the student and they have incorporated this perception in their educational systems [1], [4], [6]. Our proposed model is being realized by a module called Affective Module (AM), which interacts with an AEHS. 2 The Affective Model The operation of the AM is heavily based on the five factor model of personality (FFM) [7] and the OCC cognitive model of emotions [8]. The AM is being attached to the AEHS providing the system with the essential “emotional” information in order to determine the strategy of learning in collaboration with the cognitive information. The AM has three main components: The Emotional Component (EC), the Mentor Component (MC) and the Visualization Component (VC), which are respectively responsible for a) the recognition of student’s personality, mood and emotions during the learning process, b) the selection of the suitable teaching and pedagogical strategy and c) the appropriate visualization of the educational environment. The EC is composed of three subcomponents, the Personality Recognizer (PR), the Mood Recognizer (MR) and the Emotion Recognizer (ER), which are respectively responsible for the recognition of personality, mood and emotions of the student during the learning process. When the student uses the system for the first time, the PR subcomponent selects a suitable dialogue specified by the FFM in order to assess the type of student's personality. As a result, the student's traits are being recognized and are being used by the MC for the suitable selection of the pedagogical and the teaching strategy. The MR subcomponent provides the system with a dialogue that can elicit emotions depending upon the semantics and its context. Based on this dialogue the student's mood is recognized either as positive or negative. In our approach good mood consists of