K* detectors identify no-transfer-shallow learners at student-level cross-validated A′ of 0.766–0.791¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2013 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A K* multiplicative-interactions model cross-validated at the student level distinguishes students who perform better on the problem-solving test than the transfer test 76.6% of the time, with kappa 0.550. → Gowda 2013

Evidence¶

Gowda 2013¶

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

q2 · i? · design · r2

Cross-validated detector development on tutor log data from 71 undergraduates using the Genetics Cognitive Tutor three-factor cross module. The K* multiplicative-interactions model achieved "an acceptable cross-validated kappa of 0.550 (55 % better than chance)", precision 0.873 and recall 0.815.

"The A ′ value for the K* multiplicative-interactions model is 0.766, which indicates that the model can differentiate a student who performs better on the problem-solving test than the transfer test from a student who does not perform better on the problem-solving test than the transfer test, 76.6 % of the time."

Discussion¶

Related Claims¶

• K* models outperform step regression for detecting transfer-shallowness, with no-interactions and interaction models each best on different metrics — related
• K* detectors identify no-PFL-shallow learners with cross-validated A′ of 0.790 and kappa 0.55 — related
• Shallowness detectors transfer between the transfer-shallow and PFL-shallow constructs with minimal A′ degradation — related