Gaussian Mixture Modeling identifies two latent clusters of delayed start behavior, outperforming three-cluster and unimodal models¶
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Evidence · 1 study · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
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q2 i? A two-cluster GMM model best fits the distribution of students' average delayed start, with clusters centered at 6.0 and 12.1 minutes. → Jordan Gutterman 2026
Evidence¶
Jordan Gutterman 2026¶
Jordan Gutterman, Ashish Gurung, Lee Branstetter, Kenneth Koedinger, and Vincent Aleven. (2026). Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior. https://arxiv.org/abs/2606.25308 doi:10.48550/arxiv.2606.25308
q2 · i? · associational · r2
Mixture modeling of the delayed start distribution (mean 8.96 minutes, tail to 27.6): the "two-cluster model (BIC = 4076.1) performs best"; clusters held n=426 (60%, centered 6.0 min, SD 2.1) and n=285 (40%, mean 12.1 min, SD 4.4), intersecting at 9.0 minutes.
"A two-cluster model (BIC = 4076.1) performs best, outperforming both three-cluster (BIC = 4090.0), and unimodal (BIC = 4196.6) models."
Discussion¶
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Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
gmm-two-clusters-delayed-start |
title |
Gaussian Mixture Modeling identifies two latent clusters of delayed start behavior, outperforming three-cluster and unimodal models |
description |
Gaussian Mixture Modeling identifies two latent clusters of delayed start behavior, outperforming three-cluster and unimodal models |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — jordan-gutterman-2026 |
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