Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning
This work provides a cost-effective method for testing algorithmic constraints in shared-control tutoring systems before classroom deployment, addressing the problem of inefficient mastery learning due to diverse learner strategies.
The paper proposes a simulation-based framework to examine how learner task-selection strategies and system constraints affect mastery learning efficiency. Using data from 261 students, they found that risk-averse strategies cause higher overpractice, especially for complex problems, and targeted system constraints reduce inefficiencies for maladaptive strategies.
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.