External Factors Explaining Students’ Persistence in Online Courses in Higher Education: A Study Among Two French-Speaking Universities in Canada
Bibliographic record
Abstract
The aim of this study was to verify if external factors influence persistence in online courses in higher education. These external factors, borrowed from Kember’s (1995) model, included some students’ characteristics; cost benefits; social integration of adult students (enrolment encouragement, study encouragement, and family support); and external attribution (insufficient time, events hindering study, and distractions). Data were collected among a sample of 835 students from two Canadian French-Speaking Universities (n1 = 468 from University One and n2 = 367 from University Two) using an online questionnaire. The questionnaire included items borrowed from The Distance Education Student Progress (DESP) inventory (Kember et al., 1992). The multiple linear hierarchical regression analysis revealed that students’ characteristics and some of the external factors had an effect on students’ persistence in online courses and that the most important factor in predicting students’ persistence is cost benefits. These analyses were also conducted by university, gender, and age groups. Except for cost benefits, the results indicated different patterns of strength and significant relationships between groups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".