A prefer-harder problem-selection constraint reduces overpractice for the risk-averse strategy in the equation-solving dataset¶

Claim · All claims
Evidence · 1 study · design r2 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 Pairing Minimize Worst Case Loss with the Prefer Harder constraint reduced overpractice from 30.28 to 1.98 in the equation-solving dataset, a large Cohen's d of 1.86. → Haley Noh 2026

Evidence¶

Haley Noh 2026¶

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 doi:10.48550/arxiv.2605.21613

q2 · i3 · design · r2

Simulation result (Figure 3) for problem-selection constraints, which bias sampling toward harder problems within a chosen skill. The article reports "a large Cohen's d effect size value of 1.86" for the equation-solving dataset. No effect size is printed for graph-interpretation under this constraint.

"When paired with the Prefer Harder constraint, Minimize Worst Case Loss showed a greatly reduced overpractice value from 30.28 to 1.98 times in the equation-solving dataset, a large Cohen's d effect size value of 1.86, leveling the other strategies without the constraint"

Discussion¶

Related Claims¶

• A closer-to-mastery task-selection constraint sharply reduces overpractice for the risk-averse strategy, with large Cohen's d effect sizes — related
• In simulated mastery learning, system constraints left most other task-selection strategies' overpractice stable while sharply reducing it under the outlier risk-averse strategy — a broader claim this one bears on
• The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning — a broader claim this one bears on