pith. sign in

arxiv: 2605.21613 · v2 · pith:WDKFNYQCnew · submitted 2026-05-20 · 💻 cs.HC

Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning

Haley Noh , Aarna Chowdhary , Jeroen Ooge , Vincent Aleven , Conrad Borchers This is my paper

Pith reviewed 2026-06-30 16:53 UTC · model grok-4.3

classification 💻 cs.HC
keywords simulation frameworkmastery learningtask selection strategiesoverpracticeintelligent tutoring systemssystem constraintsshared controllearner behavior
0
0 comments X

The pith

Targeted system constraints reduce overpractice for maladaptive learner strategies in mastery learning simulations while minimally affecting efficient ones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses simulations based on data from 261 students to examine how different ways learners choose tasks in intelligent tutoring systems affect the efficiency of mastery learning. It finds that some strategies, like avoiding challenge, lead to more overpractice especially on complex problems. Adding targeted constraints on what the system allows learners to select can cut down on this overpractice for the less efficient strategies. This approach lets designers test changes to shared-control systems without needing expensive real-world trials first.

Core claim

A simulation framework grounded in real student interaction data from two math domains demonstrates that learner task-selection strategies vary widely in the overpractice they produce, with risk-averse strategies causing higher inefficiency. Targeted system constraints significantly reduce these inefficiencies for maladaptive strategies while having minimal impact on already efficient strategies.

What carries the argument

The simulation-based framework that models common task-selection strategies such as Weakness Targeting and Interleaving, applies system constraints, and measures overpractice as the indicator of mastery learning efficiency.

If this is right

  • Risk-averse strategies produce higher levels of overpractice, especially for more complex multi-step problems.
  • Targeted constraints mitigate inefficiencies mainly for maladaptive strategies.
  • Simulation grounded in real data can support redesign of shared-control tutoring systems before classroom deployment.
  • Variability in task-selection strategies may lead to undesirable differences in student learning outcomes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Systems could potentially detect a learner's strategy in real time and apply constraints adaptively.
  • The framework might extend to non-math domains if similar interaction data is available.
  • Reducing overpractice for certain strategies could help address differences in learning outcomes across learner types.

Load-bearing premise

The simulated task-selection strategies accurately capture the range of real learner behaviors in the 261-student dataset.

What would settle it

A controlled study in actual classrooms implementing the constrained system and comparing measured overpractice levels against the simulation predictions for learners using different strategies.

Figures

Figures reproduced from arXiv: 2605.21613 by Aarna Chowdhary, Conrad Borchers, Haley Noh, Jeroen Ooge, Vincent Aleven.

Figure 1
Figure 1. Figure 1: Simulation Methodology Summary. corresponding problems, but does not capture all possible forms of task-selection [2, 12]. The simulated learner then attempts the selected problem, which may consist of multiple steps, each mapping to at least one skill. The steps are simulated sequentially, with per￾formance generated probabilistically based on the learner’s current knowledge state and step difficulty. Mas… view at source ↗
Figure 2
Figure 2. Figure 2: Baseline Average Overpractice Across Strategies [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average Overpractice for the Minimize Worst Case [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. This choice brings motivational and metacognitive benefits. At the same time, past literature suggests that learners exhibit diverse preferences and strategies in selecting tasks, for instance, by avoiding challenge. Although underexplored, differences in learner task-selection strategies may interact with mastery learning systems that optimize task-selection based on estimated knowledge, potentially leading to undesirable student-level differences in learning outcomes. Algorithmic constraints on problem selection may help mitigate this issue. However, this possibility has not been comprehensively explored in prior work, in part because testing such constraints in real-world classrooms is costly. We propose a simulation-based framework to observe how varying learner task-selection strategies combined with system constraints shape mastery learning efficiency. Using interaction data from 261 students across two mathematical domains with different problem structures (equation solving, graph interpretation), we simulate common task-selection strategies such as Weakness Targeting and Interleaving, grounded in prior literature. We then evaluate how these strategies affect overpractice as a common measure of mastery learning efficiency. Results show substantial variability in efficiency across strategies, with risk-averse strategies producing higher levels of overpractice, especially for more complex multi-step problems. Targeted system constraints significantly reduce these inefficiencies for maladaptive strategies while having minimal impact on already efficient strategies. Together, these findings demonstrate how simulation grounded in real student data can support data-driven redesign of shared-control tutoring systems prior to classroom deployment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a simulation framework to examine how learner task-selection strategies (e.g., Weakness Targeting, Interleaving) interact with mastery-learning constraints in intelligent tutoring systems. Drawing on interaction traces from 261 students across two mathematical domains, the authors simulate literature-grounded strategies, measure resulting overpractice, and report that targeted system constraints substantially reduce overpractice for maladaptive strategies while leaving efficient strategies largely unaffected.

Significance. If the simulated behaviors prove faithful to real learners, the work supplies a practical, low-cost method for pre-deployment testing of shared-control designs in ITS. It quantifies how strategy variation can produce unequal efficiency outcomes and demonstrates that modest algorithmic constraints can mitigate those disparities without penalizing well-adapted learners. The grounding in real domain structure and interaction data is a clear strength.

major comments (2)
  1. [Abstract / Methods] Abstract and Methods: The task-selection strategies are stated to be 'grounded in prior literature' and then simulated on the 261-student dataset, yet no section reports fitting strategy parameters to observed selections or testing whether the simulated choice distributions match the empirical distributions (especially once constraints restrict the available problem set). Because the headline result—that constraints reduce overpractice for maladaptive strategies while having minimal impact on efficient ones—rests on the assumption that the simulated sequences are realistic, this omission is load-bearing for the central claim.
  2. [Results] Results: The abstract reports 'substantial variability in efficiency across strategies' and a differential effect of constraints, but provides no information on how overpractice is operationalized, what statistical tests or effect sizes support the 'significantly reduce' and 'minimal impact' statements, or sensitivity analyses to parameter settings. These details are required to evaluate whether the reported differences are robust.
minor comments (1)
  1. [Abstract] The abstract could name the precise strategies simulated and the two domains (equation solving, graph interpretation) earlier for immediate clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. The comments highlight important aspects of the simulation's validity and the clarity of the results. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and Methods: The task-selection strategies are stated to be 'grounded in prior literature' and then simulated on the 261-student dataset, yet no section reports fitting strategy parameters to observed selections or testing whether the simulated choice distributions match the empirical distributions (especially once constraints restrict the available problem set). Because the headline result—that constraints reduce overpractice for maladaptive strategies while having minimal impact on efficient ones—rests on the assumption that the simulated sequences are realistic, this omission is load-bearing for the central claim.

    Authors: We agree that the manuscript does not report parameter fitting to observed selections or formal tests of simulated vs. empirical choice distributions. The strategies are implemented as deterministic or rule-based procedures drawn directly from the cited literature (e.g., Weakness Targeting selects unsolved problems on the lowest-mastery skill; Interleaving cycles through skills), applied to the actual problem sets and mastery thresholds present in the 261-student traces. The goal is to explore the downstream consequences of these literature-derived strategies under different system constraints rather than to produce a fitted behavioral model of individual learners. Nevertheless, the referee correctly identifies that stronger evidence of realism would bolster the central claim. In revision we will (1) add an explicit subsection in Methods detailing the precise decision rules and any tunable parameters for each strategy, (2) report aggregate statistics comparing the distribution of skills and difficulty levels chosen in simulation versus the empirical traces (both with and without constraints), and (3) include a sensitivity analysis varying the main tunable parameters. These additions will be incorporated in the next version. revision: yes

  2. Referee: [Results] Results: The abstract reports 'substantial variability in efficiency across strategies' and a differential effect of constraints, but provides no information on how overpractice is operationalized, what statistical tests or effect sizes support the 'significantly reduce' and 'minimal impact' statements, or sensitivity analyses to parameter settings. These details are required to evaluate whether the reported differences are robust.

    Authors: We acknowledge that the abstract and the current Results section do not fully specify the operational definition of overpractice, the exact statistical procedures, effect sizes, or sensitivity checks. Overpractice is defined in the manuscript as the count of problems completed on a skill after the mastery threshold has already been reached. Comparisons across strategies and constraint conditions are performed with repeated-measures ANOVA (or paired t-tests for two-condition contrasts) accompanied by Cohen’s d effect sizes; p-values are reported with Bonferroni correction. A limited sensitivity analysis on the mastery threshold parameter appears in the appendix but is not highlighted in the main text. In revision we will (1) expand the abstract to include a concise operational definition and mention of the statistical approach, (2) add a dedicated “Operationalization and Analysis” paragraph at the start of Results, (3) report all effect sizes and exact test statistics in the main tables or text, and (4) move and expand the sensitivity analysis into the main Results section. These changes will be made. revision: yes

Circularity Check

0 steps flagged

No circularity: simulation framework uses external dataset and literature-grounded strategies without reducing claims to internal fits or self-citations

full rationale

The paper's derivation chain consists of (1) importing domain structure and interaction traces from an independent 261-student dataset across two domains, (2) instantiating task-selection strategies (Weakness Targeting, Interleaving, etc.) directly from prior literature rather than fitting parameters to the present data, and (3) running forward simulations to measure overpractice under varying constraints. None of these steps matches the enumerated circularity patterns: there is no self-definitional loop, no fitted parameter renamed as a prediction, no load-bearing self-citation, and no ansatz or uniqueness result imported from the authors' own prior work. The central efficiency claims are therefore outputs of an externally anchored simulation rather than tautological restatements of inputs. This is the normal, non-circular case for a simulation study grounded in real data and external literature.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on domain assumptions about strategy representativeness and metric validity rather than free parameters or new entities; these are pulled from educational research literature.

axioms (2)
  • domain assumption Overpractice serves as a valid common measure of mastery learning efficiency.
    Explicitly used to evaluate simulation outcomes in the abstract.
  • domain assumption Simulated strategies such as Weakness Targeting and Interleaving represent common real-world learner behaviors.
    Stated as grounded in prior literature for the simulation design.

pith-pipeline@v0.9.1-grok · 5801 in / 1156 out tokens · 47078 ms · 2026-06-30T16:53:54.443598+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

25 extracted references · 14 canonical work pages · 1 internal anchor

  1. [1]

    Koedinger, and Vincent Aleven

    Conrad Borchers, Kexin Yang, Jionghao Lin, Nikol Rummel, Kenneth R. Koedinger, and Vincent Aleven. Combining dialog acts and skill modeling: What chat interactions enhance learning rates during ai-supported peer tutoring? In Benjamin Paaßen and Carrie Dem- mans Epp, editors,Proceedings of the 17th Interna- tional Conference on Educational Data Mining, pag...

    2024

  2. [2]

    How learner control and explainable learn- ing analytics about skill mastery shape student desires to finish and avoid loss in tutored practice

    Conrad Borchers, Jeroen Ooge, Cindy Peng, and Vin- cent Aleven. How learner control and explainable learn- ing analytics about skill mastery shape student desires to finish and avoid loss in tutored practice. InPro- ceedings of the 15th International Learning Analytics and Knowledge Conference, pages 810–816, 2025. doi: 10.1145/3706468.3706484

    work page doi:10.1145/3706468.3706484 2025

  3. [3]

    Student perceptions of adaptive goal setting recom- mendations: a design prototyping study

    Conrad Borchers, Cindy Peng, Qianru Lyu, Paulo F Carvalho, Kenneth R Koedinger, and Vincent Aleven. Student perceptions of adaptive goal setting recom- mendations: a design prototyping study. InInterna- tional Conference on Artificial Intelligence in Educa- tion, pages 244–251. Springer, 2025. doi: 10.1007/ 978-3-031-98462-4 31

    2025

  4. [4]

    Ai in education, learner control, and human-ai collaboration.International Journal of Ar- tificial Intelligence in Education, 34(1):122–135, 2024

    Peter Brusilovsky. Ai in education, learner control, and human-ai collaboration.International Journal of Ar- tificial Intelligence in Education, 34(1):122–135, 2024. doi: 10.1007/s40593-023-00356-z

    work page doi:10.1007/s40593-023-00356-z 2024

  5. [5]

    There are open learner models about! IEEE Transactions on Learning Technologies, 13(2): 425–448, 2020

    Susan Bull. There are open learner models about! IEEE Transactions on Learning Technologies, 13(2): 425–448, 2020. doi: 10.1109/TLT.2020.2978473

    work page doi:10.1109/tlt.2020.2978473 2020

  6. [6]

    Controlling the false dis- covery rate: A practical and powerful approach to multiple testing,

    Deborah L Butler, Beverly Beckingham, and Helen J Novak Lauscher. Promoting strategic learning by eighth-grade students struggling in mathematics: A report of three case studies.Learning disabilities re- search & practice, 20(3):156–174, 2005. doi: 10.1111/j. 1540-5826.2005.00130.x

    work page doi:10.1111/j 2005

  7. [7]

    Learning factors analysis–a general method for cogni- tive model evaluation and improvement

    Hao Cen, Kenneth Koedinger, and Brian Junker. Learning factors analysis–a general method for cogni- tive model evaluation and improvement. InInterna- tional conference on intelligent tutoring systems, pages 164–175. Springer, 2006. doi: 10.1007/11774303 17

    work page doi:10.1007/11774303 2006

  8. [8]

    Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement.Contemporary educational psychol- ogy, 33(4):733–756, 2008

    Gemma Corbalan, Liesbeth Kester, and Jeroen JG Van Merri¨enboer. Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement.Contemporary educational psychol- ogy, 33(4):733–756, 2008. doi: 10.1016/j.cedpsych.2008. 02.003

    work page doi:10.1016/j.cedpsych.2008 2008

  9. [9]

    Knowl- edge tracing: Modeling the acquisition of proce- dural knowledge

    Albert T Corbett and John R Anderson. Knowledge tracing: Modeling the acquisition of procedural knowl- edge.User modeling and user-adapted interaction, 4(4): 253–278, 1994. doi: 10.1007/BF01099821

    work page doi:10.1007/bf01099821 1994

  10. [10]

    Why does interleaving improve math learning? the contribu- tions of discriminative contrast and distributed prac- tice.Memory & Cognition, 47(6):1088–1101, 2019

    Nathaniel L Foster, Michael L Mueller, Christopher Was, Katherine A Rawson, and John Dunlosky. Why does interleaving improve math learning? the contribu- tions of discriminative contrast and distributed prac- tice.Memory & Cognition, 47(6):1088–1101, 2019. doi: 10.3758/s13421-019-00918-4

    work page doi:10.3758/s13421-019-00918-4 2019

  11. [11]

    The 5 percent problem.Education Next, 24(4):26–31, 2024

    Laurence Holt. The 5 percent problem.Education Next, 24(4):26–31, 2024

    2024

  12. [12]

    A general multi-method ap- proach to design-loop adaptivity in intelligent tutor- ing systems

    Yun Huang, Vincent Aleven, Elizabeth McLaughlin, and Kenneth Koedinger. A general multi-method ap- proach to design-loop adaptivity in intelligent tutor- ing systems. pages 124–129, 2020. doi: 10.1007/ 978-3-030-52240-7 23

    2020

  13. [13]

    Tanja K¨aser and Giora Alexandron. Simulated learners in educational technology: A systematic literature re- view and a turing-like test.International Journal of Ar- tificial Intelligence in Education, 34(2):545–585, 2024. doi: 10.1007/s40593-023-00352-1

    work page doi:10.1007/s40593-023-00352-1 2024

  14. [14]

    Koedinger, Sidney K

    K. Koedinger, Sidney K. D’Mello, Elizabeth Mclaugh- lin, Zachary A. Pardos, and Carolyn Penstein Ros´ e. Data mining and education.Wiley interdisciplinary reviews. Cognitive science, 6 4:333–353, 2015. doi: 10.1002/wcs.1350

    work page doi:10.1002/wcs.1350 2015

  15. [15]

    A data repository for the edm community: The pslc datashop.Handbook of educational data mining, 43:43–56, 2010

    Kenneth R Koedinger, Ryan SJd Baker, Kyle Cunning- ham, Alida Skogsholm, Brett Leber, and John Stam- per. A data repository for the edm community: The pslc datashop.Handbook of educational data mining, 43:43–56, 2010

    2010

  16. [16]

    An astonishing regularity in student learning rate.Proceedings of the National Academy of Sciences, 120(13):e2221311120, 2023

    Kenneth R Koedinger, Paulo F Carvalho, Ran Liu, and Elizabeth A McLaughlin. An astonishing regularity in student learning rate.Proceedings of the National Academy of Sciences, 120(13):e2221311120, 2023. doi: 10.1073/pnas.2221311120

    work page doi:10.1073/pnas.2221311120 2023

  17. [17]

    Enhancing learn- ing outcomes through self-regulated learning support with an open learner model.User Modeling and User- Adapted Interaction, 27:55–88, 2017

    Yanjin Long and Vincent Aleven. Enhancing learn- ing outcomes through self-regulated learning support with an open learner model.User Modeling and User- Adapted Interaction, 27:55–88, 2017. doi: 10.1007/ s11257-016-9186-6

    2017

  18. [18]

    Metamorphictestingoflarge languagemodelsfornaturallanguageprocessing.doi:10.48550/arXiv

    Christopher J MacLellan. Model human learners: Com- putational models to guide instructional design.arXiv preprint arXiv:2502.02456, 2025. doi: 10.48550/arXiv. 2502.02456

    work page internal anchor Pith review doi:10.48550/arxiv 2025

  19. [19]

    Bayesian knowledge tracing, logis- tic models, and beyond: an overview of learner mod- eling techniques.User modeling and user-adapted interaction, 27(3):313–350, 2017

    Radek Pel´ anek. Bayesian knowledge tracing, logis- tic models, and beyond: an overview of learner mod- eling techniques.User modeling and user-adapted interaction, 27(3):313–350, 2017. doi: 10.1007/ s11257-017-9193-2

    2017

  20. [20]

    Learner con- trol in hypermedia environments.Educational Psy- chology Review, 19(3):285–307, 2007

    Katharina Scheiter and Peter Gerjets. Learner con- trol in hypermedia environments.Educational Psy- chology Review, 19(3):285–307, 2007. doi: 10.1007/ s10648-007-9046-3

    2007

  21. [21]

    Experimental evaluation of automatic hint generation for a logic tutor.International Jour- nal of Artificial Intelligence in Education, 22(1-2):3–17,

    John Stamper, Michael Eagle, Tiffany Barnes, and Marvin Croy. Experimental evaluation of automatic hint generation for a logic tutor.International Jour- nal of Artificial Intelligence in Education, 22(1-2):3–17,

  22. [22]

    doi: 10.3233/JAI-130029

    work page doi:10.3233/jai-130029

  23. [23]

    Theories of moti- vation in education: An integrative framework.Educa- tional Psychology Review, 35(2):45, 2023

    Detlef Urhahne and Lisette Wijnia. Theories of moti- vation in education: An integrative framework.Educa- tional Psychology Review, 35(2):45, 2023. doi: 10.1007/ s10648-023-09767-9

    2023

  24. [24]

    Optimizing mastery learning by fast- forwarding over-practice steps

    Meng Xia, Robin Schmucker, Conrad Borchers, and Vincent Aleven. Optimizing mastery learning by fast- forwarding over-practice steps. InEuropean Confer- ence on Technology Enhanced Learning, pages 549–563. Springer, 2025. doi: 10.1007/978-3-032-03870-8 37

    work page doi:10.1007/978-3-032-03870-8 2025

  25. [25]

    An ai-learner shared control model design for adaptive practicing

    Hongxin Yan, Fuhua Lin, and Kinshuk. An ai-learner shared control model design for adaptive practicing. In International Conference on Intelligent Tutoring Sys- tems, pages 272–280. Springer, 2024. doi: 10.1007/ 978-3-031-63028-6 21

    2024