An adaptive personalized learning system

Abstract

Classroom assessment enables instructors to determine learners’ needs, adjust instruction, and provide feedbacks to learners on their learning progress. To be truly effective, assessment should be blended as a part of the teaching sequence in order to verify learners’ perception of the exposed context. However, this could be time-consuming and resulting in delay and discontinuity of class lectures; thus, making the practice difficult to carry out, especially for a large class size. This paper presents an adaptive personalized learning system that integrates learning technologies with classroom teaching in order to enable a dynamic response learning environment. With an easy-to-use interface, the system supports instructors in continuous assessment of learner’s learning progress and automatic selection of supplementary learning materials to suit individual learners based on their performance in a “feedback loop” fashion. In addition, learners’ comments and questions are collected and classified into relevant topic categories using Support Vector Machine (SVM)-based text categorization for further review and lecture improvement.