An Equity-Minded Assessment of Belonging Among Computing Students at Cal Poly
Bibliographic record
Abstract
Creating a Computer Science and Software Engineering Department that supports students with diverse identities and backgrounds is essential to creating a computing workforce that reflects the world at large. Inspired by the work of Metcalf et al.'s survey conducted at the University of Illinois, we use the same methods to examine the state of our computing department with respect to issues of inclusive climate and student sense of belonging, which have been shown to be important for retention in STEM fields. We use the four areas that contribute to belonging based on the work of Rainey et al. along with a fifth category of learning environment in order to assess our students' sense of belonging. This paper's main focus is based on results from two surveys of Cal Poly Students conducted exactly one year apart (2019: n=154, 2020: n=122). Both surveys were sent to all computing majors in Spring quarter, the last quarter of the regular academic year. We found that 58-68\% of students felt they were not typical computer scientists, which mirrors the results of the survey conducted at the University of Illinois, indicating that the lack of belonging is perhaps a ubiquitous problem within the field of computing. Other salient results include identifying the presence of statistically significant differences for some groups based on gender and race & ethnicity. These differences were found when looking at students' senses of their science identity and learning environment. We also found that women had a significantly greater chance of having strong interpersonal relationships within computing. The survey results are augmented by a survey of first-quarter freshmen in Fall 2019 (n=44) and student interviews conducted in Spring 2021 (n=15). We hope that the addition of these results explain and expand upon our main results and add insight as to how the student experience can evolve from a student's first quarter onward. These differences shine an important light on some positive trends as well as several concerning differences to be examined in our quest to create a diverse and equitable department.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".