REDESIGNING TELECOMMUNICATION ENGINEERING COURSES WITH CDIO GEARED FOR POLYTECHNIC EDUCATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it