Applying the Scholarship of Teaching and Learning to Antennas and Propagation Education
Bibliographic record
Abstract
For many years, engineering education practice was driven almost exclusively by a combination of institutional edict and instructor experience and intuition. During the 1990's, two fundamental shifts in thinking occurred. First, engineering accreditation boards began to take a more aggressive approach in challenging engineering schools to improving student outcomes. Second, the notion that teaching and learning could be subjected to scholarly research and inquiry took root and gave rise to the Scholarship of Teaching and Learning (SoTL) movement. Although SoTL has gathered a large following within the academic community in recent years, integration of SoTL into the engineering disciplines is still in its early stages. While many works have been devoted to SoTL as a scholarly process, relatively few have considered the institutional or discipline-specific context within which SoTL is practiced. Here, we consider how the principles of SoTL can be usefully applied to our own efforts to fundamental improve the senior-level antennas and propagation course at UBC, and hopefully entice the best of them to consider pursuing graduate studies in this area.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".