Pith Number

pith:AC2J2EMP

pith:2026:AC2J2EMPDSP2ZUEW7SRVLJTWEY

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From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning

Boyuan Guo, Conrad Borchers, Danielle R. Thomas, Eric S. Qiu, Vincent Aleven

Feature-based models forecast weekly student effort and progress in tutoring systems with 22-33 percent lower error than heuristic rules.

arxiv:2605.12788 v1 · 2026-05-12 · cs.LG · cs.CY

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\pithnumber{AC2J2EMPDSP2ZUEW7SRVLJTWEY}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

feature-based models reduce mean absolute error (MAE) by 22-33% relative to heuristic baselines, including fixed-percentile rules adapted from prior work

C2weakest assumption

The 425-student log dataset and the chosen features are representative enough for the models to generalize to new students and new weeks without substantial distribution shift.

C3one line summary

Feature-based ML models forecast weekly student effort and progress in ITS with 22-33% lower MAE than percentile heuristics on data from 425 middle-school students.

References

57 extracted · 57 resolved · 0 Pith anchors

[1] M. A. Adams, J. C. Hurley, M. Todd, N. Bhuiyan, C. L. Jarrett, W. J. Tucker, K. E. Hollingshead, and S. S. Angadi. Adaptive goal setting and financial incentives: a 2×2 factorial randomized controlled 2017

[2] B. Albreiki, N. Zaki, and H. Alashwal. A systematic literature review of student’ performance prediction us- ing machine learning techniques.Education Sciences, 11(9):1–27, 2021 2021

[3] V. Aleven, C. Borchers, Y. Huang, T. Nagashima, B. McLaren, P. Carvalho, O. Popescu, J. Sewall, and K. Koedinger. An integrated platform for studying learning with intelligent tutoring systems: Ctat+t 2025

[4] I. Arroyo, H. Meheranian, and B. P. Woolf. Effort-based tutoring: An empirical approach to intelligent tutoring. InProceedings of the 3rd International Conference on Educational Data Mining (EDM), pag 2010

[5] R. S. Baker, A. T. Corbett, and K. R. Koedinger. Detecting student misuse of intelligent tutoring sys- tems. InProceedings of the 7th International Confer- ence on Intelligent Tutoring Systems (ITS), 2004

Receipt and verification

First computed	2026-05-18T03:09:12.990494Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

00b49d118f1c9facd096fca355a676261b2d1f896204262cb88be4ae0ab7b526

Aliases

arxiv: 2605.12788 · arxiv_version: 2605.12788v1 · doi: 10.48550/arxiv.2605.12788 · pith_short_12: AC2J2EMPDSP2 · pith_short_16: AC2J2EMPDSP2ZUEW · pith_short_8: AC2J2EMP

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AC2J2EMPDSP2ZUEW7SRVLJTWEY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 00b49d118f1c9facd096fca355a676261b2d1f896204262cb88be4ae0ab7b526

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "de47497925f2fdf77f8913cdae2de9995345986121241324905557ea8d7fca65",
    "cross_cats_sorted": [
      "cs.CY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-12T22:04:06Z",
    "title_canon_sha256": "6db6e6e76a2727fd175631e610a8f927584b541c65c710788df838403226a4ed"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12788",
    "kind": "arxiv",
    "version": 1
  }
}