Hierarchical Multi-Armed Bandits for the Concurrent Intelligent Tutoring of Concepts and Problems of Varying Difficulty Levels

TL;DR

This paper introduces an open-source hierarchical multi-armed bandit algorithm for intelligent tutoring, capable of concurrently guiding students through concepts and problems of varying difficulty, improving success rates in simulated environments.

Contribution

It presents a novel hierarchical MAB algorithm for intelligent tutoring that integrates concept and problem difficulty adaptation, addressing gaps in open-source educational recommendation systems.

Findings

Difficulty-agnostic mode significantly boosts student success.

Adding problem-difficulty adaptation further improves success rates.

Algorithm performs well in simulated student groups of 500.

Abstract

Remote education has proliferated in the twenty-first century, yielding rise to intelligent tutoring systems. In particular, research has found multi-armed bandit (MAB) intelligent tutors to have notable abilities in traversing the exploration-exploitation trade-off landscape for student problem recommendations. Prior literature, however, contains a significant lack of open-sourced MAB intelligent tutors, which impedes potential applications of these educational MAB recommendation systems. In this paper, we combine recent literature on MAB intelligent tutoring techniques into an open-sourced and simply deployable hierarchical MAB algorithm, capable of progressing students concurrently through concepts and problems, determining ideal recommended problem difficulties, and assessing latent memory decay. We evaluate our algorithm using simulated groups of 500 students, utilizing Bayesian Knowledge Tracing to estimate students' content mastery. Results suggest that our algorithm, when turned difficulty-agnostic, significantly boosts student success, and that the further addition of problem-difficulty adaptation notably improves this metric.