The TUGA, motivation, and self-efficacy jointly explain 75.3% of the variance in undergraduates’ self-directed learning¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? R² for SDL was 0.753, which the article classifies as substantial explanatory power. → Hui Zhao & Huijuan Gu 2026
Evidence¶
Hui Zhao & Huijuan Gu 2026¶
Hui Zhao & Huijuan Gu. (2026). Thoughtless use of generative artificial intelligence and college students’ self-directed learning: a multi-group SEM analysis of gender differences. Scientific Reports. https://doi.org/10.1038/s41598-026-54337-y
q2 · i? · associational · r1
Coefficient of determination reported in the structural model assessment (Table 7 also lists R² = 0.628 for MOV and 0.414 for SE). The article applies Hair's thresholds calling 0.67 substantial.
"the TUGA, MOV , and SE jointly explain 75.3% of the variance in SDL, indicating strong explanatory power of the structural model."
Discussion¶
Related Claims¶
- The effects of thoughtless GenAI use on motivation, self-efficacy, and self-directed learning differ significantly by gender — related
- Self-efficacy and motivation positively predict self-directed learning, with motivation the strongest direct predictor — related
- Thoughtless use of generative AI is negatively associated with undergraduates' self-directed learning in a cross-sectional survey model (β = −0.42) — related
- Thoughtless use of generative AI significantly and negatively predicts both self-efficacy and motivation — related
- An emotional connection to nature, rather than conservation knowledge, is a powerful predictor of children's conservation behavior — related
- The integrated model explains substantial variance in student engagement (R² = 0.700) in GenAI-supported learning — related
- Self-efficacy and motivation partially mediate the negative relationship between thoughtless GenAI use and self-directed learning, including a sequential SE→MOV pathway — related
- Self-efficacy has a significant positive effect on motivation among undergraduates using generative AI — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
model-explains-75-percent-sdl-variance |
title |
The TUGA, motivation, and self-efficacy jointly explain 75.3% of the variance in undergraduates’ self-directed learning |
description |
The TUGA, motivation, and self-efficacy jointly explain 75.3% of the variance in undergraduates’ self-directed learning |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — hui-zhao-huijuan-gu-2026 |
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