Explaining persistence in online courses in higher education: a difference-in-differences analysis
Bibliographic record
Abstract
Abstract The aim of this study was to verify if the UTAUT model, enriched with anxiety and factors relating to students characteristics and to the specificities of online courses, influences persistence in online courses. A theoretical model encompassing 13 variables was tested. Three moderating variables (gender, age and prior online course experience) were taken into account in the analyses. Data was collected among a sample of 759 students from Université Laval and Université de Sherbrooke using an online questionnaire. The results indicate that the main driver of persistence in online courses are: anxiety, satisfaction, effort expectancy, engagement, behavioral intention, employer encouragement, facilitating conditions and performance expectancy. The structural model was further examined according to gender, age and prior online course experience groups. Findings indicate that the model explains 21.4% to 37.1% of the variance in persistence in online courses. Moreover, as expected, the results indicated different patterns in the strength and significant relationships between groups and for the overall model, suggesting that gender, age and prior online course experience play a moderating role. The discussion links the results of this study to those of previous studies and suggests areas for improvement that could be implemented by academic administrators and instructors in order to enhance persistence in online courses.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".