Increasing Value Without Increasing Effort? The Use of WebCT in Accompanying Face-to-Face Lectures Under the Constraint of Low Budget
Bibliographic record
Abstract
After a period of unbridled enthusiasm about economical prospects, e-learning today is linked with the expectation of increasing expenditures. We implemented an e-learning offering at low cost for the benefit of students and teachers in a course on general pedagogy. This article describes the core ideas in our planning and the empirical findings on the students’ use of the course. Apres une periode d’enthousiasme debride sur les prospectives economiques du e-learning, son developpement aujourd’hui est lie a des attentes d’augmentation des depenses. Dans cette etude, une offre de e-learning a ete realisee a un cout moindre, pour le benefice des etudiants et des enseignants dans un cours de pedagogie generale. Cet article decrit les idees principales utilisees dans notre planification et les resultats empiriques sur l’utilisation faite par les etudiants de ce cours.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".