On the Fraction dataset, InitialD outperforms random sampling, uncertainty sampling and a normal adaptive test in cold-start response-pattern reconstruction¶
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Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person
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q2 i? Using the Fraction dataset, the InitialD DPP-based strategy achieves lower log-loss and a narrower confidence interval than Random, Uncertainty and CAT strategies when the first questions are asked to a newcomer. → Jill-Jêenn Vie 2018
Evidence¶
Jill-Jêenn Vie 2018¶
Jill-Jêenn Vie, Fabrice Popineau, Éric Bruillard, Yolaine Bourda. (2018). Automated Test Assembly for Handling Learner Cold-Start in Large-Scale Assessments. Int J Artif Intell Educ. https://doi.org/10.1007/s40593-017-0163-y
q2 · i? · design · r2
Numerical cross-validation results on the Fraction dataset (536 middle school students, 20 fraction-subtraction questions), reported in the Results section: "InitialD performs better than the other strategies", with 8 questions reconstructing "82% of answers"; log-loss tables give 0.376 ± 0.027 for InitialD after 8 questions versus 0.515 ± 0.06 for CAT.
"In Fig. 3, InitialD performs better than the other strategies, and with a narrow confidence interval. 8 questions seem enough to reconstruct correctly 82% of answers, and converge to a minimal log-loss."
Discussion¶
Related Claims¶
- InitialD converges faster toward the final diagnosis only for small question counts; a standard CAT converges faster beyond about 14 questions — related
- On the TIMSS dataset, InitialD reaches the same estimation error with fewer questions than random sampling or a traditional adaptive test — related
- On the TIMSS dataset, random sampling performs well compared to a normal adaptive test, possibly because the test is already balanced — related
- Meta-behavioral pattern extraction outperforms knowledge-concept-based collaborative extraction, which fails to consistently boost performance due to data sparsity — related
- Cold-start initialization converges faster than starting from an inconsistent student model — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
initiald-outperforms-strategies-fraction-cold-start |
title |
On the Fraction dataset, InitialD outperforms random sampling, uncertainty sampling and a normal adaptive test in cold-start response-pattern reconstruction |
description |
On the Fraction dataset, InitialD outperforms random sampling, uncertainty sampling and a normal adaptive test in cold-start response-pattern reconstruction |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2018 |
generated |
by process:wiki-ingest on 2026-10-27 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — jill-jêenn-vie-2018 |
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