AN INTERACTIVE WEB-BASED PEDAGOGICAL TOOL FOR THE TRAINING OF ENGINEERS IN ELECTRICAL ENGINEERING
Bibliographic record
Abstract
The electrical engi neering curriculum at ÉTS includes industrial training which introduces interruption periods in the academic training. In addition, as the program contains a broad range of related courses, theoretical concepts need continuous revision and followups by students. Thus, considerable effort is required from the students and professors to review t hese concepts through various courses. As a solution to this concern, a motivating web-based pedagogical tool has been created. This tool has been used in a linear control course with traditional laboratories and lecturing. The r esults of student satisfaction obtained so far show that this tool is attractive for students and help them in their revision and learning. Moreover, it provides statistics to the professor helping the evaluation of the understanding level of the group through the entire semester. This paper presents the main features, challenges and results of the web-based pedagogical developed tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".