La conception d'un programme motivationnel destiné au cycle supérieur en formation à distance
Bibliographic record
Abstract
Selon les ecrits scientifiques, certaines caracteristiques individuelles des comportements des etudiants, dans les cours a contenu chiffre offerts a distance, font obstacle a leur motivation. Pour diminuer ces difficultes eprouvees par les etudiants, un programme motivationnel a ete concu. Ce programme, developpe a partir du modele de Keller, dont les composantes sont l’Attention, la Pertinence, la Confiance et la Satisfaction, prend en compte l’ensemble des problemes dans les strategies selectionnees. C’est dans l’encadrement des etudiants que ce programme prend place, par l’envoi de messages motivationnels selon un contenu et une sequence bien definis. The purpose of this paper is to design a motivational program for students taking a distance learning course. According to scientific writings, there are particular behaviours that can hinder a student’s motivation. Focusing on the four basic tenets of the Keller Model—Attention, Relevance, Confidence, and Satisfaction—this motivational program addresses each one of these problematic behaviours in the strategies employed. This includes, namely, sending motivational messages with relevant content at strategic intervals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".