REDESIGNING TELECOMMUNICATION ENGINEERING COURSES WITH CDIO GEARED FOR POLYTECHNIC EDUCATION
Bibliographic record
Abstract
Whether in chemical, civil, mechanical, electrical, or their related engineering subdisciplines, remaining up-to-date in the subject matter is crucial. However, due to the pace of technological evolution, information and communications technology (ICT) fields of study are impacted with much higher consequences. Meanwhile, the curricula of higher educational institutes are struggling to catch up to this reality. In order to remain competitive, engineering schools ought to offer ICT related courses that are at once modern, relevant and ultimately beneficial for the employability of their graduates. In this spirit, we were recently mandated by our engineering school to develop and design telecommunication courses with great emphasis on (i) technological modernity, and (ii) experiential learning. To accomplish these objectives, we utilized the conceive, design, implement and operate (CDIO) framework, a modern engineering education initiative of which Sheridan is a member. In this article, we chronicle the steps we took to streamline and modernize the curriculum by outlining an effective methodology for course design and development with CDIO. We then provide examples of course update and design using the proposed methodology and highlight the lessons learned from this systematic curriculum development endeavor.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".