Examining the key drivers of student acceptance of online labs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As an important tool for STEM education, online labs have gained significant research attention. However, our understanding of online labs is limited by the inattention to the factors that contribute to the acceptance of online labs. This study adopts the UTAUT model to investigate the salient determinants of use of online labs. We test the proposed research model with data from N = 194 students. We find that performance expectancy, effort expectancy, and social influence are positively related to behavioral intention. Behavioral Intention, in turn, is positively related to use. In contrast, the association between facilitating conditions and use is not significant. In terms of the moderating links in the research model, age did not moderate any of the four links (performance expectancy and behavioral intention; effort expectancy and behavioral intention; social influence and behavioral intention; facilitating conditions and use) and gender did not moderate any of the three links (performance expectancy and behavioral intention; effort expectancy and behavioral intention; social influence and behavioral intention). The three variables (performance expectancy, effort expectancy, and social influence) explain 61.4% of variance in behavioral intention. In contrast, the two variables (behavioral intention and facilitating conditions) explain only 15.7% of variance in use. .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it