Decision Support System Framework for Personalized Adaptive Learning based on Behavioral Modelling

Abstract

Learning style (LS) is a method of how students receive and process information. It describe how they collect, sift through, organize and interpret the information. Each student has a different LS, thus teachers are now prioritizing identifying the LS of students. An appropriate learning method can engage and sustain students’ interest and further enhance their academic achievement. The conventional method using questionnaires to identify LS is ineffective where students may have differences in understanding and interpretation while it is hard to convey feelings and emotions on paper. To overcome the limitations of questionnaires, this study proposed an automated approach to identify LS based on student’s behavior. A Decision Support System (DSS) is experimented on user behavior demonstrating their unique LS. The framework of the LS detection is based on student behavioral modeling and employs the parameters of student behavior under the visual auditory-kinesthetic (VAK) model. The proposed framework considers three main phases: identifying the behavior of each learning style, determine the learning style of the behavior, and predicting the learning style. The framework is implemented on an application which was developed with three modules: the interface, process, and decision modules, which serve as a DSS tool to automatically predict the LS of students as the users. This paper presents the framework of VAK and the architectural model of DSS to automatically identify student’s LS as a tool to assist teachers in providing correct guidance to students based on their unique LS. The experimental results will be presented in future publication.