2026 ASEE Annual Conference & Exposition

AnonTool: An Interactive Rule-Based Tool for Detecting Struggling Programming Students

Presented at Computers in Education (CoED): Computing Pedagogy & Methods (4 of 8) -- T308C

Identifying students who struggle during programming tasks remains a persistent challenge in computer science education, often resulting in delayed interventions and untimely support. Keystroke data analysis has emerged as an approach for capturing students' coding behaviors and identifying those who might have difficulties. This is made possible due to the granular nature of the data. By examining the keystroke dynamics such as typing speed, frequency of deletions, pauses and code reconstruction, it becomes possible to make an inference of the level of confidence, frustration and hesitations students experience during programming tasks. Nevertheless, while keystroke data can reveal insightful patterns, the interpretation of the results is complex and requires complementary data sources to validate inferences about students’ struggles or confidence. This study introduces AnonTool, a rule-based system designed to detect struggling programming students by analyzing keystroke data in conjunction with self-reported survey responses. By applying a series of predefined thresholds and scoring rules, AnonTool computes a struggling score that categorizes students into five struggle levels. The system was validated in an introductory Python course involving 40 students with data collected from 547 labs and 130 homework keystroke logs, alongside structured post-assignment surveys. Results revealed that AnonTool effectively identified key struggle behaviors such as long pauses, frequent deletions, low insert-delete ratios and copy-paste events. These indicators closely aligned with students’ self-reports, particularly for challenging assignments.

Authors

Friday Emmanuel James Kansas State University [biography]

Friday James is a Teaching Assistant Professor at Kansas State University, from where he bagged a PhD in Computer Science in 2025. He has a double-majored Bachelor's degree in Statistics/Computer Science from University of Agriculture, Makurdi - Nigeria. He got a Master's degree in Statistics and a Master's degree in Computer Science from University of Ilorin - Nigeria and Kansas State University - Kansas USA in 2015 and 2021 respectively. His research interest cuts across the use of machine learning and data science in Computing Science Education to improve teaching and learning.
Joshua Levi Weese Kansas State University [biography]

Dr. Josh Weese is a Teaching Associate Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students.
Nathan H Bean http://orcid.org/https://0000-0002-1970-1427 Kansas State University [biography]

Dr. Nathan Bean is a Teaching Associate Professor at Kansas State University Department of Computer Science and Co-Director of the Advancing Learning and Teaching in Computer Science (ALT+CS) Lab. His research is focused on the need to grow the body of students skilled in computing – both within the field of Computer Science, and within other disciplines that increasingly rely on the tools computer science makes available to advance their own work. Thus, his research involves investigations into how to effectively reach a broader and more diverse audience of students, and developing pedagogical techniques and technologies that allow it to be done at scale.
Russell Feldhausen http://orcid.org/0000-0001-7236-3933 Kansas State University [biography]

Russell Feldhausen received a bachelor’s degree in computer science in 2008, and a master’s degree in computer science in 2018, both from Kansas State University. He is currently pursuing a doctorate in computer science with a focus on computer science education, also at K-State. Feldhausen’s research interest is computer science education, targeting rural populations and exploring ways to integrate mastery learning into CS curricula. He is also actively involved in many K-12 outreach programs providing curricula and teacher training throughout Kansas.
Michelle Friend [biography]

Dr. Michelle Friend is an Associate Professor in the Teacher Education Department at the University of Nebraska at Omaha. She teaches CS teaching methods and research methods. Her research focuses on equity in computer science and interdisciplinary connections between computer science and other subjects. She received her Ph.D. from Stanford University in Learning Science and Technology Design, and previously taught middle school computer science.
Mrs. Safia Malallah Kansas State University [biography]

Dr. Safia Malallah is a teaching assistant professor at Kansas State University, where she completed her Ph.D. in Computer Science and early childhood. Her research is dedicated to advancing computer science and data science education across the PreK-12 and undergraduate levels. Dr. Malallah is particularly passionate about designing innovative and accessible learning experiences that cultivate essential computational skills in students

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