Explaining educational recommendations through a concept-level knowledge visualization

In this demo paper, we present a visual approach for explaining learning content recommendation in the personalized practice system Mastery Grids. The proposed approach uses a concept-level visualization of student knowledge in Java programming to demonstrate why specific practice content is recommended by the system. The visualized student knowledge is estimated by a Bayesian Knowledge Tracing approach, which traces student problem-solving performance. The visual explanatory components, which show both a fine-grained and aggregated knowledge level, are presented to the students along with textual explanations. The goal of this approach is to display the suitability of each recommended item in the context of a student's current knowledge and goal, i.e., the current topic they are studying.