From Complaining to the Associate Dean to Leading Innovation and Entrepreneurship in the Engineering Classroom
Bibliographic record
Abstract
In this paper I provide a personal narrative,early exploration of, and directions for work being carriedout to reframe and more deeply understand the learningtaking place in my engineering entrepreneurshipclassroom. The first contribution is a new pedagogical lensbeing used to frame the learning students need to doexplicitly in terms of the key competencies, barrierconcepts, and learning thresholds my students need tomaster (as opposed to the time-based tables of content andsyllabi we normally use). The second contribution is adescription of the game-like dynamics that have resultedfrom this competency-threshold lens, and which seem tohave, in turn, encouraged a switch in focus from ‘gettinggrades’ to ‘leveling up’ by mastering the competenciesbetween and at each barrier or threshold. I also describehow this new lens seems to have focused me and mystudents more on their personal processes of buildingknowledge than just the products of the learning normallyassessed. The third contribution is a proposed researchmethodology and novel approach to data collection thattogether promise to provide a rich narrative andsignificantly deeper understanding of my students’learning experiences. I share insights gained to date on thisresearch journey, along with steps for further research.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".