K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles¶
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Evidence · 1 study · associationalr2·q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person · recheck by 2027: tool and model accuracy changes quickly
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q2 i? Clustering students in Grades 1–8 on percentage-based engagement features identified seven unique learner profiles, with seven clusters for Grades 2–5 and six for other grades. → Er 2024
Evidence¶
Er 2024¶
Er, E., Silik, S., & Cansiz, S. (2024). Uncovering Engagement Profiles of Young Learners in K–8 Education through Learning Analytics. Journal of Learning Analytics, 11(1), 101–115. https://doi.org/10.18608/jla.2024.8133
q2 · i? · associational · r2
Observational log analysis of anonymized activity data from 67,686 students in 26,742 classes across 10,872 Turkish schools, reduced from 82,189 records after cleaning. K-means clustering on daily percentage features for lessons, practices, exams, and games "yielded seven clusters for Grades 2 to 5 and six for the other grade levels."
"The analysis yielded seven clusters for Grades 2 to 5 and six for the other grade levels."
Discussion¶
Related Claims¶
- Game-oriented engagement declines with grade level while assessment-driven engagement peaks in Grade 8 — related
- No K–8 student profile showed balanced engagement across all four platform components — a narrower finding that bears on this claim
- Three distinct learner profiles emerge from function art activities: Repetitivists, Simplists, and Multifunctionists — related
- The 96 thinking styles profiles cluster into three groups, with Achievement Motivation Thinking the most common — related
- K-means clustering of factor scores identifies 10 distinct types of teacher feedback content, mostly scaffolding-oriented — related
- Exploratory K-means clustering identifies four heterogeneous student AI-user profiles differing in experience, competence, usefulness, trust, and control, but the solution's optimality and stability are not established — related
- Teachers' post-workshop platform engagement fell into three behavioral profiles, with most teachers showing minimal creation and browsing — related
- Clustering yields a small number of behaviorally distinct team clusters that instructors can use for cluster-differentiated feedback — related
- Four distinct session-level engagement types emerge in GenAI Tutor use: Deep, Shallow, Routine-Learning, and Exam-Driven — related
- K-Means++ clustering of self-reported behavioral proxies yields six distinct, stable learner profiles (k = 6, Silhouette = 0.62) — related
- Four distinct LLM learner profiles emerge from latent class analysis of learning contexts, tasks, and devices — related
- Three learning profiles emerge among university students: organized-deep, unorganized-deep, and dissonant — related
- Affinity propagation clustering of gameplay attempts yielded an interpretable six-cluster solution (part.6)
- K-means clustering of standardized prior knowledge and final grades yields four performance profiles: Consistent Achievers, Growing Achievers, Declining Performers, and Struggling Performers — related
- The theory-into-game profile appears only in Grades 1–5 and the traditional comprehensive profile is absent in Grade 1 — related
- Trace data is the dominant data type LA uses to support feedback in higher education (36% of coded data types) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
seven-engagement-profiles-k8-elearning |
title |
K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles |
description |
K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
recheck_by |
2027 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — er-2024 |
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