Leveraging Skill Hierarchy for Multi-Level Modeling with Elo Rating System

Abstract

In this paper, we are discussing the case of offering retired assessment items as practice problems for the purposes of learning in a system called ACT Academy. In contrast to computer-assisted learning platforms, where students consistently focus on small sets of skills they practice till mastery, in our case, students are free to explore the whole subject domain. As a result, they have significantly lower attempt counts per individual skill. We have developed and evaluated a student modeling approach that differs from traditional approaches to modeling skill acquisition by leveraging the hierarchical relations in the skill taxonomy used for indexing practice problems. Results show that when applied in systems like ACT Academy, this approach offers significant improvements in terms of predicting student performance.