Examining the key drivers of student acceptance of online labs
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. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".