Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps

TL;DR

This paper introduces Fast-Forwarding, a step-level adaptivity technique that reduces overpractice in mastery learning by skipping fully mastered steps, thereby improving efficiency without extensive curriculum redesign.

Contribution

The paper presents Fast-Forwarding, a novel method that enhances existing problem selection algorithms to reduce overpractice through step-level adaptivity, validated by simulation studies.

Findings

Fast-Forwarding can reduce overpractice by up to one-third.

It is most effective with algorithms that select difficult problems.

Motivation and engagement are important for practical impact.

Abstract

Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused practice tasks. However, few efforts have concentrated on reducing overpractice through step-level adaptivity, which can avoid resource-intensive curriculum redesign. We propose and evaluate Fast-Forwarding as a technique that enhances existing problem selection algorithms. Based on simulation studies informed by learner models and problem-solving pathways derived from real student data, Fast-Forwarding can reduce overpractice by up to one-third, as it does not require students to complete problem-solving steps if all remaining pathways are fully mastered. Fast-Forwarding is a flexible method that enhances any problem selection algorithm, though its effectiveness is highest for algorithms that preferentially select difficult problems. Therefore, our findings suggest that while Fast-Forwarding may improve student practice efficiency, the size of its practical impact may also depend on students' ability to stay motivated and engaged at higher levels of difficulty.