Understanding students' approaches to learning in university traditional and distance education courses
Bibliographic record
Abstract
This paper presents a descriptive study that compared students' approaches to learning in two settings: one group of students was in a face-to-face course, and the other was in a distance education course. The methods used emphasize the value and difficulty of using qualitative techniques in researching students' learning. The results revealed evidence of distinct approaches to learning in the two groups. However, there were no significant differences in the level of comprehension of content material. Resume Cet article presente une etude descriptive, comparant la facon d'aborder l'apprentissage des etudiants qui apprennent dans deux cadres differents : un groupe d'etudiants dans un cours en classe, et l'autre dans un cours de l'education a distance. Les methodes utilisees ont mis l'accent sur la valeur et la difficulte d'utiliser des techniques qualitatives dans la recherche sur l'apprentissage des etudiants. Les resultats ont mis en evidence des facons bien distinctes d'aborder l'apprentissage dans les deux groupes. Cependant, il n'y avait pas de differences notables dans le niveau de la comprehension du contenu du materiel enseigne.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".