2026 ASEE Annual Conference & Exposition

Introducing Community College Students to Data Science through a Scaffolded Summer Experience

Presented at DSAI-Session 7: Adaptive Learning, Personalized Feedback, and Global Teamwork

Community college students in the USA represent an important but often overlooked talent pool for computing fields. Yet many face barriers such as math anxiety and low self-efficacy, limiting their interest in CS and STEM transfer pathways. To address this, we developed a 10-day online summer program aimed at shifting pretransfer STEM-focused community college students’ perceptions of data science, an interdisciplinary, CS-centric field blending mathematics, programming, and real-world applications. Rather than a mastery-focused bootcamp, the program served as an exposure-based intervention: a scaffolded, supportive experience designed to give students a taste of what to expect in a formal data science education pathway while fostering confidence, curiosity, and conceptual familiarity. The program featured peer mentorship, scaffolded programming exercises, and guided instruction in key topics such as probability, statistics, and machine learning. Most participants had no prior experience with coding or advanced math but reported increased confidence in applying mathematical concepts in a computing context. Peer mentors played a vital role in creating a non-intimidating environment that encouraged engagement. Despite overall success, students still found some topics, particularly probability and regression, challenging. Pre and post-program surveys analyzed using the Wilcoxon signed rank test showed statistically significant improvements in self efficacy and reductions in math anxiety. Future iterations will incorporate more interactive problem-solving and expanded career guidance to better support student transitions to four-year computing programs. This paper offers lessons from the pilot, including design strategies and student outcomes, to inform scalable computing education interventions for underrepresented learners.

Authors

Prof. SAGNIK NATH http://orcid.org/0000-0002-5225-952X University of California, Santa Cruz [biography]

Sagnik Nath is an Assistant Teaching Professor (tenure track) in the Department of Computer Science and Engineering at the University of California, Santa Cruz. He earned his Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute in 2020 and his B.E. in Electronics and Communication Engineering from the Indian Institute of Engineering Science and Technology, Shibpur, in 2015. His teaching and research interests span computer architecture, VLSI design, AI literacy, and optimization techniques for small language models, with a particular focus on integrating generative AI into engineering education. He leads projects exploring how embodied AI, data science instruction, and large language models can enhance student engagement and broaden participation in computing.

Dr. Nath is actively involved in the computer science education community, having served multiple times as an Associate Program Chair for the SIGCSE Technical Symposium and as a panel reviewer for the National Science Foundation. He also reviews regularly for leading computer science and engineering education venues, including ITiCSE, CompEd, and ASEE.
Marcella Gomez University of California, Santa Cruz
Dr. So Yoon Yoon http://orcid.org/0000-0003-1868-1054 University of Cincinnati [biography]

Dr. So Yoon Yoon is an assistant professor in the Department of Engineering and Computing Education in the College of Engineering and Applied Science at the University of Cincinnati, OH, USA. Dr. Yoon received her Ph.D. in Gifted Education and an M.S.Ed. in Research Methods and Measurement with a specialization in Educational Psychology, both from Purdue University, IN, USA. She also holds an M.S. in Astronomy and Astrophysics and a B.S. in Astronomy and Meteorology from Kyungpook National University, South Korea. Her work centers on elementary, secondary, and postsecondary engineering education research as a psychometrician, data analyst, and program evaluator with research interests in spatial ability, STEAM education, workplace climate, and research synthesis with a particular focus on meta-analysis. She has developed, validated, revised, and copyrighted several instruments beneficial for STEM education research and practice. Dr. Yoon has authored more than 100 peer-reviewed journal articles and conference proceedings and served as a journal reviewer in engineering education, STEM education, and educational psychology. She has also served as a PI, co-PI, advisory board member, or external evaluator on several NSF- and NASA-funded projects.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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1st Generation
2 Year Institution
Broadening Participation in Engineering and Engineering Technology
computer science
engineering
race/ethnicity
transfer
undergraduate