Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps

2025-06-21

AI Summary

In mastery-based learning, students frequently over-practice already-mastered skills—termed “over-practice”—reducing instructional efficiency. To address this, we propose a step-level adaptive fast-forwarding mechanism that dynamically skips redundant problem-solving steps during practice, without modifying course structure or interfering with existing item selection algorithms, thereby enabling precise practice reduction while preserving mastery guarantees. This work introduces, for the first time, step-level fast-forwarding into mastery learning frameworks. We construct a learner model and a problem-solving path graph grounded in real student interaction data, and employ simulation-based analysis to support fine-grained, stepwise skipping decisions. Experimental results show a 33% reduction in over-practice under simulation; the improvement is most pronounced for difficulty-preferring item selection strategies, while simultaneously enhancing both practice efficiency and learner motivation.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Reducing overpractice in mastery learning systems
• Enhancing problem selection with step-level adaptivity
• Improving efficiency without curriculum redesign

Innovation

• Fast-Forwarding skips mastered problem-solving steps
• Enhances existing problem selection algorithms
• Reduces overpractice by up to one-third