Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning

2026-05-20

AI Summary

This study addresses the inefficiency in mastery learning caused by learners’ suboptimal task-selection strategies—such as challenge avoidance—and their interaction with system-imposed algorithmic constraints. For the first time, it integrates empirically grounded models of real student strategic behavior into a mastery learning simulation framework. Leveraging behavioral models derived from authentic interaction data, the work simulates distinct learning strategies (e.g., weakness-focused practice and interleaving) and employs over-practice metrics to quantify how system constraints modulate inefficient behaviors. Findings reveal that risk-averse strategies substantially exacerbate over-practice in multi-step complex tasks, yet targeted system constraints effectively mitigate such inefficiencies without compromising the performance of more effective strategies. This approach offers a low-cost validation method for intelligent tutoring systems prior to deployment.

Abstract

Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. Prior work suggests learners exhibit diverse task-selection strategies, such as avoiding challenge, which may interact with mastery learning systems that optimize task selection based on estimated knowledge. Algorithmic constraints on problem selection may help mitigate these effects, but testing such constraints in classrooms is costly. We propose a simulation-based framework to examine how learner task-selection strategies and system constraints shape mastery learning efficiency. Using interaction data from 261 students across two mathematical domains (equation solving and graph interpretation), we simulate strategies such as Weakness Targeting and Interleaving. We evaluate how these strategies affect overpractice as a measure of efficiency. Results show substantial variability across strategies, with risk-averse strategies producing higher levels of overpractice, especially for complex multi-step problems. Targeted system constraints significantly reduce inefficiencies for maladaptive strategies while minimally affecting already efficient strategies. These findings show how simulation grounded in student data can guide the redesign of shared-control tutoring systems before classroom deployment.

Problem

• mastery learning
• task-selection strategies
• intelligent tutoring systems
• learning efficiency
• system constraints

Innovation

• simulation-based framework
• task-selection strategies
• mastery learning
• system constraints
• overpractice