Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network
Bibliographic record
Abstract
While a lack of diversity is a longstanding problem in computer science and engineering, universities and organizations continue to look for solutions to this issue. Among the first of its kind, we launched INSPIRE: STEM for Social Impact, a program at the University of Victoria, Canada, aimed to motivate and empower students from underrepresented groups in computer science and engineering to develop digital solutions for society impactful projects by engaging in experiential learning projects with identified community-partners. The twenty-four students in the program came from diverse backgrounds in terms of academic areas of study, genders, ethnicities, and levels of technical and educational experience. Working with six community partners, these students spent four months learning and developing solutions for a societal and/or environmental problem with potential for local and global impacts. Our experiences indicate that working in a diverse team with real clients on solving pressing issues produces a sense of competence, relatedness, and autonomy which are the basis of self-determination theory. Due to the unique structure of this program, the three principles of self-determination theory emerged through different experiences, ultimately motivating the students to build a network of likeminded people. The importance of such a network is profound in empowering students to succeed and, in retrospect, remain in software engineering fields. We address the diversity problem by providing diverse, underrepresented students with a safe and like-minded environment where they can learn and realize their full potential. Hence, in this paper, we describe the program design, experiences, and lessons learned from this approach. We also provide recommendations for universities and organizations that may want to adapt our approach.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| 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".