Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization

TL;DR

This paper introduces GRATE, a novel knowledge tracing method that dynamically aggregates student attempts to better model complex problem solving and improve performance prediction.

Contribution

GRATE is a new tensor factorization approach that selectively aggregates student attempts, enhancing modeling of complex knowledge states and concept discovery.

Findings

GRATE outperforms state-of-the-art baselines in performance prediction.

Attempt aggregation reduces fluctuations in knowledge states.

The method uncovers complex latent concepts in problems.

Abstract

Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students' historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, they do not perform well for modeling complex problem solving in students.M ost importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge.However, for complex problems that involve many concepts at the same time, this assumption is deficient. In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students' performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students' discovered knowledge states and helps in discovering complex latent concepts in the problems.