Board 266: Enhancing Transfer Pathways in Computing: An NSF Project Progress Report
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».