Continuous Student Knowledge Tracing Using SVD and Concept Maps

Abstract

One of the critical aspects of building intelligent tutoring systems regards proper monitoring of student's activity and academic performance. This paper presents a continuous student knowledge tracing method implemented for Tesys e-Learning platform at the Faculty of Automation, Computers and Electronics in the University of Craiova. The student's knowledge level is continuously monitored and, after each recommended test by the SVD-based mechanism, a new set of knowledge weights are computed. We aim to achieve a comprehensive monitoring environment which can provide an accurate insight upon the student's knowledge level at any moment. In our approach, we added weights for both students and tests to improve the student's evolution monitoring and provide more accurate feedback. The setup for validation consisted of ten tests with eight questions per test and we used both current and past year tests data. Results revealed that assigning weights to questions, tests and students and using them in the recommendation process offers a better view of the student's evolution along with more accurate recommendations. Progress in this direction will provide more insight into available teaching materials and SVD-based recommender system such that the e-learning platform that integrates the presented mechanism will provide a better learning experience.

Keywords

• data preprocessing
• distance learning
• knowledge representation
• human computer interaction
• recommender systems