How Diversifying / Updating the Teaching Team Has Positively Affected Teaching
Bibliographic record
Abstract
Abstract Diversity is a key concern for universities, especially in the engineering field. North American universities have been recently pushing for diversity, equity, and inclusion in their education system. University X started by diversifying the teaching team in one of its engineering programs to check the effectiveness of diversifying the teaching team on the education system. It replaced the teaching team for two courses from white males of European descent to racialized females with no other instructions on how to deliver their courses. Both teams were given the same teaching notes and instructions to teach from, however it was fascinating to see what levels these courses were taken to by just diversifying the teaching team. In one of the studio courses, the principals of universal design have never been included explicitly in years past. The previous lecture material, which served to introduce a similar project that focused on furniture design in past years, included slides that referenced "man as the universal standard" and contained many slides of architectural graphic standards based on the average European man's body proportions. In 2022, the teaching team is entirely female, bringing more diversity to the presentation of materials than ever before. The instructors are focusing on human-centered design and universal design methods in multiple lectures over the course, encouraging students to design equitable user experiences and empathize with the diversity of the greater public. In another mechanics course, the female instructor transformed the activities delivered in the class to include the ethical and social impact effects. She did so by transforming the delivery of the same hands-on activity from a simple report to a presentation to middle school kids in the community around them. The aim was to present the mechanics concepts learned in class through hands-on activities to middle-school kids while focusing on how such structures affect society. They need to do so through well guided ethical behavior guidelines taking into consideration their audience and engaging all students with their different capabilities. The aim was to increase the engagement of women and marginalized students and kids in such activities as they get to clearly relate to the benefits to the society for these structures and the engineering disciplines in general. This paper discusses in detail the transformation done through these two courses by the simple fact of diversifying the teaching team as well as the effects noticed on the education system
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".