Genetics Cognitive Tutor three-factor cross dataset with transfer, PFL and problem-solving post-tests¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A dataset of Carnegie Mellon undergraduates’ interactions with the Genetics Cognitive Tutor three-factor cross module and subsequent problem-solving, transfer, and preparation-for-future-learning assessments, released for training and validating shallowness detectors.
Components¶
Claims¶
- K* detectors identify no-PFL-shallow learners with cross-validated A′ of 0.790 and kappa 0.55 [+W]
- K* detectors identify no-transfer-shallow learners at student-level cross-validated A′ of 0.766–0.791 [+W]
Related Products and Programmes¶
-
Key Sources¶
- Gowda, S. M., Baker, R. S., Corbett, A. T., & Rossi, L. M. (2013). Towards Automatically Detecting Whether Student Learning is Shallow. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-013-0006-4
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
genetics-cognitive-tutor-three-factor-cross-dataset-with-transfer-pfl-and-problem-solving |
title |
Genetics Cognitive Tutor three-factor cross dataset with transfer, PFL and problem-solving post-tests |
description |
A dataset of Carnegie Mellon undergraduates’ interactions with the Genetics Cognitive Tutor three-factor cross module and subsequent problem-solving, transfer, and preparation-for-future-learning assessments, released for training and validating shallowness detectors. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-28 |
trust |
unverified — no one has confirmed this page's content |
Frontmatter is the page's machine-readable record — see the Schema & Guide.