Research Overview
Through our research and collaborations, we seek to develop and assess technology and approaches that can enhance learning outcomes, empower individuals, and help prepare future generations in computing. We draw on methods and techniques from the fields of computing education (CEd), human-computer interaction (HCI), data science (DS), and machine learning (ML). Below, we provide information about some of the current projects and topics of focus in our laboratory. We also offer a selection of representative publications in these areas:
PROFESSIONAL AND TECHNICAL DEVELOPMENT
Examination of students’ pathways to job attainment (CEd approach with quantitative and qualitative methods); Creation and assessment of digital tools (using HCI methods) to cultivate professional and technical competencies and enhance technical interview preparation
- Brewster, V., & Lunn, S. J. (2023, October). Virtual Hiring Managers: Student Perceptions and Agent Preferences. In Proceedings of the 2023 IEEE ASEE Frontiers in Education (FIE), College Station, Texas, USA. https://doi.org/10.1109/FIE58773.2023.10343190
- Lunn, S. J., Zerbe, E., & Ross, M. (2024). You’re Hired! A Phenomenographic Study of Undergraduate Students’ Pathways to Job Attainment in Computing. ACM Transactions on Computing Education (TOCE), 24(1), 1-29. https://doi.org/10.1145/3636514
- Lunn, S. J., Dillon, E., & Sadid, Z. A. (2024, June). Educational Expertise: Faculty Insights on Preparing Computing Students to Navigate Technical Interviews. In Proceedings of the 2024 American Society of Engineering Education (ASEE) Conference & Exposition, Portland, Oregon, USA. https://doi.org/10.18260/1-2–47215
COMPUTING ACCESS POINTS, TOUCHPOINTS, AND EXPERIENCES
Exploration of the computing access points, touchpoints, and experiences that offer engagement with the discipline (e.g., participation in hackathons or bootcamps); Data-driven approach to extract insights using large educational datasets (using DS and ML methods)
- Zhu, J., Lunn, S. J., & Ross, M. (2023, March). Characterizing Women’s Alternative Pathways to a Computing Career Using Content Analysis. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE), Toronto, Canada, (pp. 158-164). https://dl.acm.org/doi/abs/10.1145/3545945.3569798
- Lunn*, S., Marques Samary*, M., & Peterfreund, A. (2023, July). Calling Upon the Community: Gathering Data on Programmatic and Academic Opportunities in Computing Education Research. In Proceedings of ACM’s 28th annual conference on Innovation and Technology in Computer Science Education (ITiCSE), Turku, Finland, (pp. 354-360). https://doi.org/10.1145/3587102.3588813
- Arunachalam, N., Lunn, S. J., Weiss, M. A., Liu, J., & Narasimhan, G. (2024, March). Foot in the Door: Developing Opportunities for Computing Undergraduates to Gain Industry Experience. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE), Portland, OR, USA, (pp. 74-80). https://dl.acm.org/doi/10.1145/3626252.3630857
ETHICAL CONSIDERATIONS IN COMPUTING
Raise awareness and promote consideration of ethical issues in data use and storage, system design, development, and deployment (e.g., algorithmic accountability); Establish and assess modules to integrate throughout computing curricula (using CEd approach)
- Hooper, K. & Lunn, S. J. (2024, May). A Scoping Review of Transparency and Explainability in AI Ethics Guidelines. In Proceedings of the 37th International Florida AI Research Society (FLAIRS) Conference, Miramar Beach, FL, USA. https://doi.org/10.32473/flairs.37.1.135594
- Hooper, K., & Lunn, S. J. (2024, October). Values in Education: Exploration of Artificial Intelligence Ethics Syllabi Using Natural Language Processing Analyses. Accepted to the IEEE Frontiers in Education, Washington DC, USA.
- Lunn, S. J., Hazewindus, I., Prasad, P., & Ramachandran, V. (2024). “Ethical Issues in Software Engineering.” Published in the International Handbook of Engineering Ethics Education, Routledge.