Semantic Predictive Model of Student Dynamic Profile Using Fuzzy Concept

Abstract

Student profile describes the best way a student prefers to learn. It includes information on student’s characteristics such as background knowledge, learning preference, styles, interest, goals etc. The major challenge that the students face in learning system is that they are unable to retrieve relevant information based on their requirements. One of the methods used to obtain the requirement of the students is to construct an efficient student profile which would reflect the true student needs. The proposed work is to develop an intelligent ontology-based dynamic student profile that provides semantic retrieval using fuzzy concepts. The approach starts with the collection of both static and dynamic data of students. The dynamic dataof students particularly student interest and learning style are obtained by weblog analysis using algorithms such as semantic based representation using WordNet and decision tree classifier algorithm based on Felder-Silverman learning style model (FSLSM). The retrieved data is then used to construct student profile using ontology in which automatic student profile updating is obtained using ontology -based semantic similarity algorithm using WordNet. Finally, semantic retrieval of student information from ontology is achieved by integrating fuzzy concepts using fuzzy linguistic variable and ‘fuzzy IF THEN’ rules. Fuzzy linguistic variable is used to make precise representation on the existing ontology concepts which facilitate more specific classification and semantic retrieval of information. The predictive model of student profile is designed with the implementation of ‘fuzzy IF THEN’ rules using forward chaining reasoning process in the existing ontology model. The inference engine predicts the preference of a new student based on the reasoning process done for specific conditions particularly on student interest and learning style. The experiments were performed using NetBeans IDE, OWL API and Protégé 4.2 beta editor. The experiment result shows the successful completion of student profile generation, updating, fuzzy semantic retrieval and prediction through utilization of fuzzy concepts in student profile ontology.