WIP: Boosting CS Freshmen: The Impact of Summer Bridge Programs on Academic Success and Attitudes

This Work-In-Progress Research paper details a quantitative analysis of the impact of a summer bridge program for incoming Freshmen majoring in Computer Science, mainly focusing on post-program academic outcomes. We designed and delivered a summer bridge program to serve students who are members of a historically underserved group in Computer Science. The program is designed to help participants build a foundation in mathematics and programming while fostering an environment for community and confidence building. In our study, we utilize data from a Likert-scale survey administered to students before and after the program to compare attitudes towards non-academic factors such as programming and mathematics self-concept, science motivation, and sense of belonging, alongside post-program academic performance indicators such as performance in the first engineering mathematics course, performance in the first programming course, academic standing, and major declaration and retention outcomes. This data, collected over four cohorts of participants from 2020 to 2023, includes two cohorts participating online due to the COVID-19 pandemic (2020 and 2021) and two in-person cohorts (2022 and 2023). We aim to address three primary research Questions: (RQ1) To what extent does the overall post-program academic performance vary between online (2020, 2021) and in-person (2022, 2023) modalities? (RQ2) Can a potential change in attitudes towards non-academic indicators influence or explain a student's post-program academic indicators? (RQ3) Can a student's non-academic indicators and math/programming self-concept influence or explain a student's persistence in STEM?

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Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/fie61694.2024.10893347
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00

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article

WIP: Boosting CS Freshmen: The Impact of Summer Bridge Programs on Academic Success and Attitudes

Shreya Gupta, Narges Norouzi
Teaching and Learning Programming
article

WIP: Boosting CS Freshmen: The Impact of Summer Bridge Programs on Academic Success and Attitudes

Shreya Gupta, Narges Norouzi
article en

Abstract

This Work-In-Progress Research paper details a quantitative analysis of the impact of a summer bridge program for incoming Freshmen majoring in Computer Science, mainly focusing on post-program academic outcomes. We designed and delivered a summer bridge program to serve students who are members of a historically underserved group in Computer Science. The program is designed to help participants build a foundation in mathematics and programming while fostering an environment for community and confidence building. In our study, we utilize data from a Likert-scale survey administered to students before and after the program to compare attitudes towards non-academic factors such as programming and mathematics self-concept, science motivation, and sense of belonging, alongside post-program academic performance indicators such as performance in the first engineering mathematics course, performance in the first programming course, academic standing, and major declaration and retention outcomes. This data, collected over four cohorts of participants from 2020 to 2023, includes two cohorts participating online due to the COVID-19 pandemic (2020 and 2021) and two in-person cohorts (2022 and 2023). We aim to address three primary research Questions: (RQ1) To what extent does the overall post-program academic performance vary between online (2020, 2021) and in-person (2022, 2023) modalities? (RQ2) Can a potential change in attitudes towards non-academic indicators influence or explain a student's post-program academic indicators? (RQ3) Can a student's non-academic indicators and math/programming self-concept influence or explain a student's persistence in STEM?

University of California, Berkeley (US)
National Science Foundation
Climate action
Openalex Percentile: Top 71%
Teaching and Learning Programming
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