Iterative Design for Adapting Engineering Learning Systems to Tunisian Education
Bibliographic record
Abstract
E-learning studies have identified challenges related to the viability of e-learning systems (ELS) and the relevance of instructional design models and methods. The authors implemented an iterative design experiment of ELS prototypes, using the engineering method of learning systems (EMILSO), for the learning of chronobiology in an agri-food master's program via the Virtual University of Tunisia's platform. An iterative, didactic, pedagogical, and technological analysis of the prototypes allowed the authors to revise and adapt them to the Tunisian educational context, validating and developing EMILSO. The analysis maintained EMILSO's four specifications and renamed certain phases, such as the “project definition” phase as the “preliminary analysis of the project.” New validation links were created between EMILSO's knowledge, pedagogical, and media models, and new tasks were inserted in the project identification phase. The characterization of representations was also considered in the preliminary analysis phases of the knowledge and pedagogical estimates.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".