Jul 2026· Annual Conference on Innovation and Technology in Computer Science Education· pp. 2-8· 0 citations· 37 references
Computer Science
TL;DR
The most common mistakes that students made are identified, as well as mistakes that were particularly likely to persist even in the face of targeted feedback, and a conceptual pretest-posttest designed to help contextualize these mistakes.
Abstract
Novice programmers often have flawed mental models of program execution, and knowing what misconceptions they are likely to hold can help instructors to prevent and correct these misconceptions. Recently, tools have emerged which task students with simulating a program's execution and constructing the run-time memory layout. Such tools create new opportunities to systematically log student errors and identify flaws in their mental models --- including low-level misconceptions that may not be visible when looking at other artifacts such as student-written programs. This paper analyzes log data that was collected by instrumenting one such tool. We identify the most common mistakes that students made, as well as mistakes that were particularly likely to persist even in the face of targeted feedback. We also present the results of a conceptual pretest-posttest designed to help contextualize these mistakes.
This study considers if statements as they comprise some of the semantically simplest compound statements in Python, to provide an overview that goes deeper and beyond mere frequency statistics, together with noteworthy examples of student errors that may further guide the development of automated systems.
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While LLM-rewritten messages significantly improved subjective evaluations, with pragmatic messages rated as clearer and less cognitively demanding, these perceived gains did not translate into statistically significant improvements in objective debugging performance.
Alexandru-Radu Moraru, Shreyan Biswas, U. Gadiraju· 0 citations
Debugging is an immense challenge for novices when learning programming, often causing frustration. Applying a systematic debugging process has a considerable impact on debugging success. As a result, several interventions have been designed to teach this process to students. However, understanding how students actuall...
Elena Spörer, Tilman Michaeli· Proceedings of the 2026 Unit...· 1 citation
The rapid proliferation of large language models (LLMs) in the context of education has introduced significant challenges in enforcement of academic integrity, especially in programming courses. We present Argus, an automated detection system for LLM-assisted student work in undergraduate C programming assignments. Arg...
David Racovan, Ajay Rawat, Christopher K. May et al.· 0 citations
Findings indicate that behavioral signals present at the very start of an exercise contain useful clues about whether a student will ultimately solve the problem, and that keystroke-level editing logs provide additional value for early prioritization beyond execution logs alone.
A scaffolded programming exercise designed to support student differentiation between good and bad GenAI code suggestions based on negative expertise–that identifying why an answer is wrong is part of developing conceptual knowledge.
J. Prather, Stephen MacNeil, Andrew Luxton-Reilly et al.· International Computing Educ...· 0 citations
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