Éducation et technologie: Analyse des perceptions d’intervenants sénégalais à l’aube de la mise en place de projets éducatifs en technologie de l’information et de la communication
Bibliographic record
Abstract
Before proposing a major social change in a developing country, it is important to understand the prevailing perceptions and to identify the underlying values of the affected persons. Senegal has opted for the use of technologies for educational purposes. To insure the success of such a project, it was decided to evaluate, as thoroughly as possible, the perceptions of the key intervening variables and the underlying values of the principal actors of the educational system. Senegal has many different kinds of schools, and there is a risk that the advent of technology may be variously interpreted. To understand the implication of this situation, an evaluation model based on soft system methodology was developed and used to gather opinions and values of an important group of actors in the educational system. The study has revealed not only the limited knowledge of the general population about the educational possibilities and of this new media but also the possibilities offered by emerging écoles communautaires de base. Finally, the study permitted the formulation of suggestions to promote the collective and harmonious growth of the Senegalese educational system.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".