Impact of Undergraduate Research Workshops on Sense of Belonging and Self-Efficacy based on Gender and Race
Bibliographic record
Abstract
Motivation: There is a significant need for diverse voices in research, yet we observed that our CS department’s upper-year research programs were rarely pursued by students from diverse backgrounds. Thus, we developed the PRISM (Preparation for Research through Immersion, Skills, and Mentorship) program at our institution for second- and third-year undergraduate students. Objectives: As this is a new program (offered fully in-person for the first time in 2023), we wanted to measure how well the program fostered feelings of belongingness and self-efficacy in students, and collect general feedback about the program. Methods: Data was collected through online surveys completed at the beginning and end of the program consisting of: (1) scales measuring belongingness and self-efficacy in CS research, (2) open-ended questions about aspects of the program, and (3) a closed-ended question asking for an overall rating. Results: We found that while sense of belonging was not affected by the program, self-efficacy improved statistically significantly from the beginning to the end of the program, and the majority of students enjoyed the program based on their overall rating and positive feedback on the open-ended questions. Implications: The current structure of the program is generally liked by students and had a positive impact on student self-efficacy in CS research. Further research needs to be done on ways to re-structure the program to reach our goal of improving a feeling of belongingness in students.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".