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Leah Bidlake; Eric Aubanel; Daniel Voyer – ACM Transactions on Computing Education, 2025
Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model-based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn…
Descriptors: Schemata (Cognition), Programming, Computer Science Education, Coding
Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
Manuel B. Garcia – Education and Information Technologies, 2025
The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the…
Descriptors: Programming, Introductory Courses, Computer Science Education, Mastery Learning
Molly Domino; Bob Edmison; Stephen H. Edwards; Rifat Sabbir Mansur; Alexandra Thompson; Clifford A. Shaffer – Computer Science Education, 2025
Background and Context: Self-regulated learning (SRL) skills are critical aspect of learning to program and are predictive of academic success. Early college students often struggle to use these skills, but can improve when given targeted instruction. However, it is not yet clear what skills are best to prioritize. Objective: We seek to create a…
Descriptors: Metacognition, Programming, Computer Science Education, College Students
Anna Rechtácková; Radek Pelánek; Tomáš Effenberger – ACM Transactions on Computing Education, 2025
Code quality is a critical aspect of programming, as high-quality code is easier to maintain, and code maintenance constitutes the majority of software costs. Consequently, code quality should be emphasized in programming education. While previous research has identified numerous code quality defects commonly made by students, the current state…
Descriptors: Programming, Computer Science Education, Error Patterns, Automation
Cheryl Resch – ProQuest LLC, 2024
Software vulnerabilities in commercial products are an issue of national importance. The most prevalent breaches are input validation vulnerabilities, and these are easily avoidable. This dissertation contributes to cybersecurity education with a set of hands-on interventions tailored for three CS courses, a set of reflection prompts to encourage…
Descriptors: College Students, Computer Science Education, Computer Security, Curriculum Development
Ibrahim Albluwi; Raghda Hriez; Raymond Lister – ACM Transactions on Computing Education, 2025
Explain-in-Plain-English (EiPE) questions are used by some researchers and educators to assess code reading skills. EiPE questions require students to briefly explain (in plain English) the purpose of a given piece of code, without restating what the code does line-by-line. The premise is that novices who can explain the purpose of a piece of code…
Descriptors: Questioning Techniques, Programming, Computer Science Education, Student Evaluation
Nilüfer Atman Uslu; Aytug Onan – Education and Information Technologies, 2025
Understanding the emotions experienced by programming students, particularly concerning gender and education level, is increasingly critical. However, only limited research has used text data to examine these differences within the context of programming education and emotions. This study aims to determine programming students' emotions and any…
Descriptors: Programming, Psychological Patterns, Student Attitudes, Secondary School Students
Kevin Slonka; Matthew North; Neelima Bhatnagar; Anthony Serapiglia – Information Systems Education Journal, 2025
Continuing to fill the literature gap, this research replicated and expands a prior study of student performance in database normalization in an introductory database course. The data was collected from four different universities, each having different prerequisite courses for their database course. Student performance on a database normalization…
Descriptors: Required Courses, Academic Achievement, Information Systems, Databases
Shao-Heng Ko; Kristin Stephens-Martinez – ACM Transactions on Computing Education, 2025
Background: Academic help-seeking benefits students' achievement, but existing literature either studies important factors in students' selection of all help resources via self-reported surveys or studies their help-seeking behavior in one or two separate help resources via actual help-seeking records. Little is known about whether computing…
Descriptors: Computer Science Education, College Students, Help Seeking, Student Behavior
Maciej Pankiewicz; Yang Shi; Ryan S. Baker – International Educational Data Mining Society, 2025
Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…
Descriptors: Algorithms, Artificial Intelligence, Models, Intelligent Tutoring Systems
Mehmet Arif Demirta¸; Max Fowler; Kathryn Cunningham – International Educational Data Mining Society, 2024
Analyzing which skills students develop in introductory programming education is an important question for the computer science education community. These key skills and concepts have been formalized as knowledge components, which are units of knowledge that can be measured by performance on a set of tasks. While knowledge components in other…
Descriptors: Programming, Computer Science Education, Skill Development, Knowledge Level
Hawlitschek, Anja; Berndt, Sarah; Schulz, Sandra – Computer Science Education, 2023
Background and Context: Pair programming is an important approach to fostering students' programming and collaborative learning skills. However, the empirical findings on pair programming are mixed, especially concerning effective instructional design. Objective: The objective of this literature review is to provide lecturers with systematic…
Descriptors: Cooperative Learning, Programming, Computer Science Education, College Students
Mark Frydenberg; Anqi Xu; Jennifer Xu – Information Systems Education Journal, 2025
This study explores student perceptions of learning to code by evaluating AI-generated Python code. In an experimental exercise given to students in an introductory Python course at a business university, students wrote their own solutions to a Python program and then compared their solutions with AI-generated code. They evaluated both solutions…
Descriptors: Student Attitudes, Programming, Computer Software, Quality Assurance
Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2022
In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…
Descriptors: Computer Science Education, College Students, Programming, Coding

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