K-means clustering of standardized prior knowledge and final grades yields four performance profiles: Consistent Achievers, Growing Achievers, Declining Performers, and Struggling Performers¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 The four-cluster solution was validated by large between-cluster differences in prior knowledge (η2 = .77) and final grades (η2 = .72). → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 q2 i? Growing Achievers began with low prior knowledge but reached high final grades, while Declining Performers started higher and ended low. → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 (2)

Evidence¶

Yuan-Hsuan Lee and Jiun-Yu Wu 2026¶

Yuan-Hsuan Lee and Jiun-Yu Wu. (2026). Differential engagement with generative artificial intelligence in higher education: Gender, motivation, and achievement trajectories. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00615-8

q2 · i3 · associational · r2

ANOVA validation of the k-means four-cluster solution (95 students tested; two lacked pretest scores) confirmed clear separation on both clustering variables, with printed effect sizes η2 = .77 for prior knowledge and η2 = .72 for final grades.

"Results supported significant differences among the four clusters with large effect sizes ( F = 102.10, p < .01, η2 = .77 for prior knowledge; F = 78.29, p < .01, η2 = .72 for final course grades)"

Yuan-Hsuan Lee and Jiun-Yu Wu 2026 (2)¶

Yuan-Hsuan Lee and Jiun-Yu Wu. (2026). Differential engagement with generative artificial intelligence in higher education: Gender, motivation, and achievement trajectories. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00615-8

q2 · i? · associational · r2

The four profiles were labeled from standardized means: Consistent Achievers (n = 27, high-high), Growing Achievers (n = 30, low prior knowledge to high final grades), Declining Performers (n = 19, higher start to low finish), and Struggling Performers (n = 19, low-low).

"Students who began with low prior knowledge ( M = − 0.37) but reached high final grades (M = 0.69) demonstrated substantial improvement and effective learning gains, earning the label Growing Achievers (n = 30 in purple squares)."

Discussion¶

Related Claims¶

• Growing Achievers used StatBot significantly more than Declining Performers, driven mainly by initiating new inquiries — related
• The six clusters differ substantially in academic performance: Final Grade ANOVA η² = 0.770, with C0 exceeding C1 by 40.17 exam-score points — related
• K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles — related
• K-Means++ clustering of self-reported behavioral proxies yields six distinct, stable learner profiles (k = 6, Silhouette = 0.62) — 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