Lecturer e-Training Program to Support University Teaching | Programme d’e-formation pour les chargés de cours pour appuyer l’enseignement universitaire
Bibliographic record
Abstract
This article attempts to explore the extent to which Lecturer e-Training Program (LeP) supports lecturers in their preparation for student-centred teaching. LeP was conducted in a blended mode, that is, it involved an online self-paced learning module followed by an interactive online discussion and ended with a face-to-face action learning. It was compulsory for all lecturers in this university to enrol in LeP. The data collection involved distributing questionnaires to all 36 lecturers. After that, 16 lecturers were selected at random for one-to-one structured interviews. In this study, it was found that LeP contributed significantly to the lecturer preparation for student-centred teaching, in particular, Stage 2 (Online Discussion) and Stage 3 (Face-to-Face Action Learning). Lecturers in this university were mostly homogeneous with regard to culture. It would be interesting to test LeP across cultural diversity as it was believed Asians and Westerners think differently.Le présent article vise à explorer la mesure dans laquelle le Programme d’e-formation (Lecturer e-Training Program) appuie les chargés de cours dans leur préparation pour un enseignement centré sur l’étudiant. Le Programme a été réalisé en mode hybride, c’est-à-dire avec un module d’apprentissage en ligne à rythme libre, qui a été suivi d’une discussion interactive en ligne et finalement d’un apprentissage par l’action réalisé en personne. Tous les chargés de cours de cette université devaient s’inscrire au Programme. La collecte de données a consisté à distribuer des questionnaires aux 36 chargés de cours. Ensuite, 16 d’entre eux ont été choisis au hasard pour des entrevues structurées en tête-à-tête. Dans cette étude, on a conclu que le Programme contribuait de façon importante à la préparation du chargé de cours pour un enseignement centré sur l’étudiant, particulièrement le deuxième stade (discussion en ligne) et le troisième (apprentissage par l’action réalisé en personne). La culture des chargés de cours de cette université était surtout homogène. Il serait intéressant de tester le Programme à travers une diversité culturelle, car on croit que les personnes asiatiques et occidentales pensent différemment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".