Simulation-based framework for evaluating learner task-selection strategies and system constraints in mastery-based ITS¶
Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 design),q2· 1 of 1 report an effect size · 2 claims rest on one study
Description¶
A simulation framework that models mastery learning as a repeated problem-level decision cycle in which simulated learners select skills via predefined strategies, the system selects problems, performance is generated via AFM, and mastery is updated via BKT with a 95% mastery threshold. It is grounded in real student interaction data from two math ITS datasets and enables "controlled comparison of learner strategies and system constraints under standardized conditions" prior to classroom deployment. The framework supports close-the-loop experimentation by identifying which learner behaviors warrant targeted system constraints.
Accounts¶
- Simulation-based framework for evaluating learner task-selection strategies and system constraints in mastery-based ITS: A simulation framework that models mastery learning as a repeated problem-level decision cycle in which simulated learners select skills via predefined strategies, the system selects problems, performance is generated via AFM, and mastery is updated via BKT with a 95% mastery threshold. It is grounded in real student interaction data from two math ITS datasets and enables "controlled comparison of learner strategies and system constraints under standardized conditions" prior to classroom deployment. The framework supports close-the-loop experimentation by identifying which learner behaviors warrant targeted system constraints. (Haley Noh et al. (2026))
Claims¶
- The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning [+M]
- A closer-to-mastery task-selection constraint sharply reduces overpractice for the risk-averse strategy, with large Cohen's d effect sizes [+M]
Related Research Methods¶
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Key Sources¶
- Haley Noh, Aarna Chowdhary, Jeroen Ooge, Vincent Aleven, Conrad Borchers. (2026). Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning. arXiv preprint. https://arxiv.org/abs/2605.21613
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
simulation-based-framework-for-evaluating-learner-task-selection-strategies-and-system-con |
title |
Simulation-based framework for evaluating learner task-selection strategies and system constraints in mastery-based ITS |
description |
A simulation framework that models mastery learning as a repeated problem-level decision cycle in which simulated learners select skills via predefined strategies, the system selects problems, performance is generated via AFM, and mastery is updated via BKT with a 95% mastery threshold. |
status |
review |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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