Longitudinal middle-school ITS log dataset with weekly engagement targets¶
Product or Programme · All products and programmes
Evidence · 2 claims (1 for, 1 mixed) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A longitudinal dataset of web-based intelligent tutoring system activity from 425 middle-school mathematics students, providing timestamped interaction logs and weekly practice-effort and skill-mastery targets for learning-analytics research.
Components¶
Claims¶
- Feature-based ML models reduce next-week forecasting MAE by 22-33% over heuristic baselines for weekly minutes practiced and new skills mastered [+M]
- Ablations show every feature group contributes to skills forecasting, while recent engagement activity alone nearly suffices for minutes [~W]
Related Products and Programmes¶
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Key Sources¶
- Eric S. Qiu, Danielle R. Thomas, Boyuan Guo, Vincent Aleven, Conrad Borchers. (2026). From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning. https://arxiv.org/abs/2605.12788
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
longitudinal-middle-school-its-log-dataset-with-weekly-engagement-targets |
title |
Longitudinal middle-school ITS log dataset with weekly engagement targets |
description |
A longitudinal dataset of web-based intelligent tutoring system activity from 425 middle-school mathematics students, providing timestamped interaction logs and weekly practice-effort and skill-mastery targets for learning-analytics research. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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