Modelling Student Knowledge in Blended Learning

Abstract

Blended learning offers a diverse learning experience through multiple activities inside and outside the classroom, which can improve student knowledge, as there are multiple opportunities for learning. However, managing these activities requires an integrated approach to ensure its effectiveness, that is, taking into account learning data from different sources. Disregarding any of these sources may lead to incomplete/incorrect information on the current levels of students' understanding of courses topics. This paper proposes an approach to student modelling that incorporates both streams of student activity performed during both modes of blended learning. To maintain a mode meaningful representation of students' knowledge, reflecting differences in focuses of in-class and at-home assessment, the proposed approach divides student knowledge into three cognitive levels based on Bloom's taxonomy, namely, Remember, Understand, and Apply. The Elo Rating System is used as the main method of student knowledge estimation; it is enriched with knowledge propagation between the Bloom's levels of cognitive activity to account for their inter-dependency. The propagation parameters are optimised. The result shows that the model is capable to distinguish between positive and negative results of student attempts well enough.