Supporting and Examining Student Computational Reasoning Through Realistic Cybersecurity Data
This study examines how realistic cybersecurity data, reflective activities, algorithm analysis, and programming support undergraduate students’ computational reasoning, interpretation of uncertainty, and evidence-based learning.
Presented by:
Li Xu, The University of Arizona
From the author, in their own words...
Li Xu, The University of Arizona
Transcript:
The study presented in this poster investigates how realistic cybersecurity data, computation, and structured reflection in a course design can support undergraduate students in developing computational reasoning beyond procedural programming.
The course redesign framework is presented at the top center of the poster. Students worked with cybersecurity data while conducting programming and algorithm analysis, followed by guided reflection.
Across seven assignments and two course cohorts, six reasoning patterns became visible, including four recurring reasoning practices and two broader patterns.
The visual on the left shows that students’ understanding and engagement ratings remained generally high across both cohorts, while weekly participation varied more noticeably.
The study used mixed methods to analyze student learning data. Coding on consented student reflection data was assisted by a locally installed large language model through Ollama, with human review of codes, evidence, and analytic decisions.
Finally, these reasoning patterns connect with capabilities emphasized in the NICE Workforce Framework, particularly problem solving, data interpretation, troubleshooting, evidence-based judgment, and adaptation across technical contexts.
Abstract:
This study examines how course redesign using realistic cybersecurity data supports student computational reasoning in an undergraduate course. Using a mixed-methods approach, the study analyzes participation records, assignment artifacts, and student reflections from two in-person cohorts (Fall 2025 and Spring 2026). It examines aggregated course-level learning data across cohorts while drawing individual quotations from consented participants. Findings reveal that realistic HHS OCR breach data and synthesized cyber operation data supported authentic learning experiences. The study also highlights how structured data activities, algorithm analysis, and reflective prompts support students’ interpretation of programming/analysis outputs, evaluation of uncertainty, and evidence-based computational reasoning.
Key Words:
Computational Reasoning, Reflective Learning, Cybersecurity Data.
Outcomes:
1. Analyze how authentic cybersecurity datasets and reflective learning activities can support student computational reasoning beyond procedural programming tasks.
2. Examine how students interpret computational outputs, evaluate uncertainty and limitations, and justify evidence-based conclusions in data-driven cybersecurity contexts.
3. Identify instructional strategies that integrate realistic data, algorithm analysis, and reflective prompts to support deeper computational reasoning in computing education.
References:
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National Academies of Sciences, Engineering, and Medicine. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press. https://doi.org/10.17226/24783
Tedre, M., & Denning, P. J. (2021). The long quest for computational thinking. Proceedings of the 16th Koli Calling International Conference on Computing Education Research, 120–129. https://doi.org/10.1145/2999541.2999542
Turns, J. A., Sattler, B., Yasuhara, K., Borgford-Parnell, J., & Atman, C. J. (2014). Integrating reflection into engineering education. Atlanta: American Society for Engineering Education-ASEE.
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