Adaptive teaching strategy for online learning

Abstract

Finding the optimal teaching strategy for an individual student is difficult even for an experienced teacher. Identifying and incorporating multiple optimal teaching strategies for different students in a class is even harder. This paper presents an Adaptive tutor for online Learning, AtoL, for Computer Science laboratories that identifies and applies the appropriate teaching strategies for students on an individual basis. The optimal strategy for a student is identified in two steps. First, a basic strategy for a student is identified using rules learned from a supervised learning system. Then the basic strategy is refined to better fit the student using models learned using an unsupervised learning system that takes into account the temporal nature of the problem solving process. The learning algorithms as well as the initial experimental results are presented.