The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Without system constraints, the Minimize Worst Case Loss strategy yields overpractice of 30.28 (equation-solving) and 28.93 (graph-interpretation), versus averages of 1.733±0.383 and 3.223±0.050 for the other seven strategies. → 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 of 1,000 simulated learners per condition, grounded in AFM/BKT parameters fit to real student interaction data from two math ITS datasets. The risk-averse strategy was a clear outlier: "the average of the other seven strategies' overpractice amounts were 1.733±0.383 and 3.223±0.050". No effect size is printed for this baseline contrast.
"Without system constraints, using the Minimize Worst Case Loss strategy produced overpractice levels of 30.28 times in equation-solving and 28.93 times on average in graph-interpretation (Figure 2). In contrast, the average of the other seven strategies' overpractice amounts were 1.733±0.383 and 3.223±0.050"
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
- 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
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
risk-averse-task-selection-high-overpractice |
title |
The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning |
description |
The risk-averse Minimize Worst Case Loss task-selection strategy produces far higher overpractice than other strategies in simulated mastery learning |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — haley-noh-2026 |
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