ENA revealed high CT students had significantly more coherent SRL cognitive networks spanning planning, execution, and self-reflection than low CT students¶

Claim · All claims
Evidence · 1 study · associational r1 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 The subtracted ENA network showed a significant group difference in average X-dimension positions (t(41.75) = −5.52, p <.001, Cohen's d = 1.53), with high CT networks dense and connected across all three SRL phases and low CT networks sparse with seeking help dominant. → Shu Zhao 2026

Evidence¶

Shu Zhao 2026¶

Shu Zhao, Ziqi Wang, Tingting Chen, Chunchen Kang, Yu-Ju Lan. (2026). Exploring the effect of computational thinking levels on students' learning performance, cognition, and behavior when using AI coding assistants. Education Tech Research Dev. https://doi.org/10.1007/s11423-026-10663-6

q2 · i3 · associational · r1

Epistemic Network Analysis of reflection journals visualized on the ENA platform, comparing the two groups' cognitive networks. The article reports "t(41.75) = −5.52, p <.001, Cohen's d = 1.53" on the subtracted network's X dimension, a large printed effect size.

"Significant differences existed between the high CT and low CT groups in their average positions on the X dimension of the network (t(41.75) = −5.52, p <.001, Cohen's d = 1.53)."

Discussion¶

Related Claims¶

• Behavioral trends across three tasks showed high CT students maintained stable independent coding while low CT students' AICA use fluctuated — related
• High CT students significantly exceeded low CT students in task strategies, elaboration, and self-assessment during AICA interactions — related
• Self-efficacy, reflective thinking, and help-seeking were associated with topic understanding at the cohort level (r = 0.427, 0.369, 0.168; d = 0.479) — related
• University students with high versus low GenAI literacy scores show significantly different ENA interaction structures during an AI-assisted abstract writing task — related
• Epistemic network analysis shows high-learning groups integrate re-annotations with knowledge-confirming discourse more densely than low-learning groups — related
• On the final exam, high performers had more dominant strategy–time-management links than low performers, who showed dominant catching-up, ahead-with-errors, and video-revision links — related
• High- and low-performing students differed significantly on the final-exam ENA network X dimension but not the Y dimension — related
• High- and low-performing students did not differ significantly on the midterm-exam ENA network dimensions despite detected network differences — related
• Intrinsically motivated students form reflective-iterative metacognitive co-occurrence networks, but overall ENA centroid positions do not differ significantly between motivational groups — related
• High CT students used the AICA for code understanding and debugging while low CT students used it for direct answer retrieval — related
• Students' SRL profile under automated scoring was uneven, and only Execution-and-Monitoring was significantly associated with score gains — related
• High CT students significantly outperformed low CT students on the post-test after AICA-assisted coding instruction — related
• ENA contrasting-cases analysis shows high- and low-effectiveness studies differ significantly in their design-element and intention networks — related