Weakness Targeting and Maximize Usual Case Improvement yield the lowest overpractice among unconstrained strategies in the equation-solving dataset¶

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

Subclaims¶

q2 i? In the equation-solving dataset, Weakness Targeting and Maximize Usual Case Improvement performed slightly better than other strategies, yielding the lowest overpractice, at 1.26 and 1.29 average practice opportunities respectively. → 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 · i? · design · r2

Baseline simulation results without constraints (Figure 2) across eight strategies in the equation-solving dataset. The article states these strategies "performed slightly better than other strategies". No effect size is printed for this contrast.

"in the equation-solving dataset, Weak- ness Targeting and Maximize Usual Case Improvement per- formed slightly better than other strategies, yielding the low- est overpractice of 1.26 and 1.29 average practice opportuni- ties, respectively (Figure 2)"

Discussion¶

Related Claims¶

• In simulated mastery learning, system constraints left most other task-selection strategies' overpractice stable while sharply reducing it under the outlier risk-averse strategy — related
• The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning — related