Board 266: Enhancing Transfer Pathways in Computing: An NSF Project Progress Report
Bibliographic record
Abstract
Abstract Our project, known as "University of California's Servingness," is dedicated to establishing a robust transfer pathway in Computing between California Community Colleges and the University of California system. The primary focus of our endeavor is to advance the transition from merely enrolling racially diverse students to genuinely serving them in ways that foster greater persistence, graduation rates, and career placement. We posit that universities can better exemplify the concept of "serving" Hispanic and Latinx, Black, Indigenous, and People of Color (BIPOC) students who attend predominantly white institutions by investing in effective transfer pathways. Eligibility for our program extends to students who meet two or more of the following criteria: being the first in their family to attend college, experiencing socio-economic challenges, and hailing from historically underrepresented groups in terms of both gender and race/ethnicity. Through this NSF-funded project, we have been actively working to dismantle institutional barriers, adapt computing curricula at our partner institutions to local contexts, and, most importantly, elevate degree attainment and career placement by providing students with invaluable research experiences. A pivotal component of our project is the implementation of a summer program tailored to transfer students from our collaborating community colleges. This program aims to equip these students with crucial summer research experiences that deepen their understanding of computing research areas and smooth their transition into upper-division courses, all while stimulating their interest in pursuing advanced studies at the graduate level. Given the growing availability of summer bridge programs for students in STEM fields at four-year institutions, it has become essential to assess the impact of such programs on a wide range of academic and non-academic indicators [1]. In this poster presentation, we will share our project's progress, experiences, and valuable lessons learned. Our objective is to illustrate the tangible impacts of our program on academic success metrics, psychosocial well-being, and department-level goals. Moreover, we are keen on delving into the transformation in participants' perspectives concerning non-academic indicators, and we aim to determine whether this transformation varies across the two program modalities: online and in-person. To achieve this, we will employ A/B testing and a thorough evaluation of pre- and post-program score distributions [2, 3]. This research forms an essential part of our ongoing work as we strive to enhance the educational experience and future prospects of our diverse student body. References: [1] Ashley, M., Cooper, K. M., Cala, J. M., & Brownell, S. E. (2017). Building better bridges into STEM: A synthesis of 25 years of literature on STEM summer bridge programs. CBE—Life Sciences Education, 16(4), es3. [2] Norouzi, N., Habibi, H., Robinson, C., & Sher, A. (2023, June). An Equity-minded Multi-dimensional Framework for Exploring the Dynamics of Sense of Belonging in an Introductory CS Course. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 131-137). [3] Norouzi, N., & Robinson, C. (2022, March). Evaluation of the Impact of Modality for Equity Program. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1335-1335).
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".