The continuous rising of digital learning platforms have unarguably brought about a surge in the amount of data obtained from different learning environments, which presents a great opportunity for computer science teachers to gain an understanding of students’ coding processes. This is vital for enhancing student support as teachers can gain insights into students’ thought process, strategies and identify areas where students might be struggling while tacking programming tasks. Traditional assessment methods such as feedback after homework submissions or completed lab assignments often results in late and untimely intervention that would prevent early dropouts and massive failures as the students’ learning journey is neglected in the process.
We leverage students’ keystroke data obtained from Python and Java-based introductory programming courses delivered through CODIO learning platform, to design an interactive code visualization and error detection platform using Streamlit. The application features an interface that reproduces students’ code snippets with JavaScript enabled syntax highlighting. It includes a combination of dropdown menu and an adjustable slider which enables an instructor to navigate through the timestamps and have a detailed view of students’ coding processes. The application also includes a navigation to an environment that enables instructors to run the generated code snippets for error detection, giving the instructors clear idea of the difficult part(s) of the course for intervention purposes. The intervention ultimately fosters a more supportive learning environment and helps boost students’ confidence.
Authors
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Friday James is a PhD Candidate at Kansas State University. 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.
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Dr. Josh Weese is a Teaching Assistant 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.
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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.
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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.
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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.
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David is an Associate Professor in the Department of Curriculum and Instruction at Kansas State University and the Director of the Center for STEAM Education. His work involves professional development for K-12 schools in STEAM related areas, and he is currently focused on on-line programing development in mathematics and computer science education.
Note
The full paper will be available to logged in and registered conference attendees once the conference starts on
June 22, 2025, and to all visitors after the conference ends on June 25, 2025
