Our computer science education program prioritizes inclusivity and diversity aiming to provide equitable access to computer science learning for students from various backgrounds. It offers a comprehensive curriculum, teacher training, and resources that cater to diverse populations, emphasizing the importance of digital literacy and computational thinking. The program encourages underrepresented groups to engage in coding, problem-solving, and technology exploration, fostering a more inclusive and empowered generation of young learners in the field of computer science.
Academics and Coursework
Our Computer Science education research program has developed exceptional certificate programs and continuing education programs aimed at enhancing CS skills and literacy, preparing a diverse workforce for the evolving field of computing. In addition to broader requirements for a CS undergraduate or graduate degree, students may specialize their education around Computer Science Education research topics via these undergraduate and graduate level courses, minors, and certificate programs:
Undergraduate Courses
| CSC 699 | Learning by Teaching |
K-12 CS Teacher Supplementary Authorization Program
| CSC 699 | Computational Thinking |
| CSC 309 | Computer Programming |
| CSC 645 | Computer Networks |
| CSC 305 | Impacts of Computing |
| CSC 698 | Software Development and Pedagogy |
| CSC 209 | Introduction to Programming using Python |
| CSC 306 | An Interdisciplinary Approach to Computer Programming |
| CSC 219 | Data Structure for Data Science Application Development |
| CSC 308 | Introduction to Machine Learning for Data Scientists |
| CSC 508 | Machine Learning and Data Science for Personalized Medicine |
| CSC 509 | Data Science and Machine Learning for Medical Image Analysis |
Data Science and Machine Learning for Biotechnology Certificate
| CSC 308 | Introduction to Machine Learning for Data Scientists |
| CSC 508 | Machine Learning and Data Science for Personalized Medicine |
| CSC 509 | Data Science and Machine Learning for Medical Image Analysis |
| CSC 601 | Data Science and Machine Learning for Biotechnology Seminar Series |
| CSC 602 | Interview Preparation for Data Science and Machine Learning for Biotechnology |
Faculty and Focus Areas
Publications and Reports
Ihorn, S., Kulkarni, A., & Yoon, I. (2023). Sustaining and expanding student training and support efforts beyond the NSF support period. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.
Gautam, A., Ihorn, S., Yoon, I., & Kulkarni, A. Foundational strategies to support students with diverse backgrounds and interests in early programming. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.
Ihorn, S., Kulkarni, A., & Yoon, I. (2023). Sustaining and expanding student training and support efforts beyond the NSF support period. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.
Gautam, A., Kim, M., Ihorn, S., Yoon, I., & Kulkarni, A. (2023) Foundational strategies to support students with diverse backgrounds and interests in early programming. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.
Zimmerman, T., Esquerra, R., Chan, Y.H.M., Kulkarni, A., Adelstein, N., Albright, A., Luo, J., Dean, Z., Ahmed, S., Phillips, M. and Bianco, S., (2023). Teaching Image Processing and Optical Engineering to University Biology Students. The Biophysicist, 4(1), pp.38-57. https://doi.org/10.35459/tbp.2022.000240
Reyes, R.J., Hosmane, N., Ihorn, S., Johnson, M., Kulkarni, A., Nelson, J., Savvides, M., Ta, D., Yoon, I. and Pennings, P.S., (2022). Ten simple rules for designing and running a computing minor for bio/chem students. PLOS Computational Biology, 18(7), p.e1010202. https://doi.org/10.1371/journal.pcbi.1010202
Kulkarni, A., Ihorn, S., Tate, C., Nelson, J., Hosmane, N., Adelstein, N., Pennings, P., Jacques, T. and Yoon, I., (2021). January. Peer Mentoring in an Interdisciplinary Computer Science Training Program: Mentor & Student Perspectives and Lessons Learned. In Zone 1 Conference of the American Society for Engineering Education.
Ihorn, S., Yoon, I. and Kulkarni, A., 2020, February. Student psychological factors and diversity in computer science education. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1380-1380).
Grants and Awards
National Science Foundation. CS4NorthCal: Scaling an Evidence-based Model for Teacher Preparation and Support to Provide Equitable and Inclusive CS Education in California High Schools. 10/01/2022-09/31/2026. (PI: Yue)
National Science Foundation. BPC-A: Socially Responsible Computing: Promoting Latinx student retention via community engagement in early CS courses. 10/1/2022-9/30/2025. (PI: Yoon)
National Science Foundation. HSI Implementation and Evaluation Project: Self-sustaining Peer Mentor Support System for Computer Science Students. 08/01/2022-07/31/2025. (PI: Wang)
Center of Inclusive Computing, Northeastern (https://cic.northeastern.edu/). Redesign Introductory CS Courses. 6/1/2022-5/31/2024. (PI: Yoon)
California State University Chancellor’s Office. Math and Science Teacher Initiative (MSTI) Supplement Grant. 09/01/2020-08/31/2023. (PI: Yue)
Genentech Inc. Data Science, Artificial Intelligence and Biotechnology Certificate program. 8/1/2020-7/31/2023. (PI: Kulkarni)
Genentech Foundation. Gene-PINC Scholarship. 8/1/2020-7/31/2023. (PI: Kulkarni)
National Science Foundation. Collaborative Research: CS4SF: A Scalable Model for Preparing High School Teachers to Provide Rigorous, Inclusive Computer Science Instruction. 10/01/2018-09/31/2021. (PI: Yue)
National Science Foundation. Building the Diverse, Multidisciplinary Computer Science Workforce of the Future with Promoting Inclusivity in Computing (PINC) 2.0. 10/2018-9/2023 (PI: Yoon).
National Science Foundation. Scholarships To Improve Undergraduate Students' Academic Achievement, Retention, and Career Success in Computer Science and Artificial Intelligence. 8/7/2020-2/28/2025. (PI: Kulkarni)