Growing Achievers used StatBot significantly more than Declining Performers, driven mainly by initiating new inquiries¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2 · i2 medium
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i2 Cluster membership explained about 9% of variance in total usage ranks (ε2 = .091), with Growing Achievers (Median = 27.5) exceeding Declining Performers (Median = 4). → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 q2 i2 Initial Queries showed significant cluster differences (ε2 = .081), while Follow-Up Inquiry differences were only marginal (p = .052). → 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 · i2 · associational · r2

Kruskal–Wallis tests across the four achievement profiles found significant differences in total StatBot use, H(3) = 11.49, p = .009, ε2 = .091; Bonferroni-adjusted post hoc comparisons showed Growing Achievers (Median = 27.5) used the chatbot significantly more than Declining Performers (Median = 4).

"The Kruskal–Wallis test for Total Inquiry Count was significant, H(3) = 11.49, p = .009. The resulting effect size, ε2 = .091, indicates a medium-sized effect, suggesting that cluster membership explained approximately 9% of the variance in total usage ranks."

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 · i2 · associational · r2

Differences in Initial Inquiry behavior were significant, H(3) = 10.50, p = .015, ε2 = .081, with Growing Achievers highest (Median = 26.0) versus Declining Performers (Median = 3.0); Follow-Up Inquiry was only marginally significant, H(3) = 7.70, p = .052, ε2 = .051.

"the clusters demonstrated significant differences in their use of Initial Queries , H(3) = 10.50, p = .015. This finding was also associated with a medium effect size ε2 = .081)."

Discussion¶

Related Claims¶

• K-means clustering of standardized prior knowledge and final grades yields four performance profiles: Consistent Achievers, Growing Achievers, Declining Performers, and Struggling Performers — related
• Voluntary StatBot use was highly uneven and deadline-driven, with about 27% of students never using the chatbot — related
• Female gender, higher autonomous motivation, lower prior knowledge, and higher final course grades each predict more frequent voluntary StatBot use among graduate statistics students — related
• Higher-achieving graduate students were less likely to abstain entirely from StatBot use — related
• Student chatbot queries were overwhelmingly task-oriented initial inquiries, dominated by definition requests and pasted assignment questions rather than iterative critique — 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
• High-frequency LLM users show higher minimum course outcomes and reduced score variability, though interaction level is not a dominant outcome predictor — related
• Motivational variables rarely explain more than 10% of the variance in behavior and outcomes — a broader claim this one bears on