DIVERSIFYING THE ENGINEERING VOICES: BRINGING DIVERSE PROFESSIONALS TO THE CLASSROOM THROUGH VIDEO INTERVIEWS
Bibliographic record
Abstract
Abstract – This paper will discuss the importance of using videos in the classroom and the significance of adding diversity to teaching and learning, particularly in engineering and technology. Increasing diversity is a common goal seen in many institutions, including the Faculty of Engineering at McMaster University. Beginning in Fall 2017, instructors from the Bachelor of Technology program, in the School of Engineering Practice and Technology (SEPT) will integrate recorded video interviews of diverse professionals into the classroom, hopefully putting the goal more within reach. These videos will depict professionals in engineering fields who identify as young, female or visible minorities. In these interviews, the participants will discuss a variety of topics including: the importance of communication skills in a professional environment; the advantages of participating in co-op programs; life after graduation; as well as other topical issues. In addition to the proven benefits of learning through videos, students will hear from various professionals of different backgrounds thereby adding diversity to the engineering classroom.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".