In simulated mastery learning, system constraints left most other task-selection strategies' overpractice stable while sharply reducing it under the outlier risk-averse strategy¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? Blocking, Interleaving, Strength Targeting, and Weakness Targeting maintained stable overpractice levels across constraint conditions in simulations parameterized by student data, while constraints had their strongest effect on the risk-averse strategy (Minimize Worst Case Loss), cutting its overpractice from 30.28 to 1.83 and from 28.93 to 3.22 in the two datasets (Cohen's d 1.87 and 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 · i? · design · r2

Simulation study (learner models parameterized with student interaction data; no intervention with real learners) comparing constraint conditions (Figure 3) and showing an asymmetry: constraints exert their strongest effects on risk-averse learners while "already efficient strategies show minimal change". Without constraints the risk-averse strategy overpracticed about 30 times, against an average of 1.7 and 3.2 for the other seven strategies (Strength Targeting was second-worst in equation solving). No effect sizes are printed for the stable strategies.

"By contrast, most other strategies were relatively unaffected by these constraints. Blocking, Interleaving, Strength Targeting, and Weakness Targeting all maintained stable levels of overpractice across conditions"

Discussion¶

Related Claims¶

• Personalized Learning Effects Vary With Fidelity — the general claim this finding bears on, with the studies behind it
• A closer-to-mastery task-selection constraint sharply reduces overpractice for the risk-averse strategy, with large Cohen's d effect sizes — a narrower finding that bears on this claim
• The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning — a narrower finding that bears on this claim
• A prefer-harder problem-selection constraint reduces overpractice for the risk-averse strategy in the equation-solving dataset — a narrower finding that bears on this claim
• Weakness Targeting and Maximize Usual Case Improvement yield the lowest overpractice among unconstrained strategies in the equation-solving dataset — related
• Children who switched to the efficient method largely reverted to the inefficient method over subsequent trials — related
• The acquired strategy-selection knowledge did not make PROTO-TEG more efficient in terms of student performance — reports the opposite