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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
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
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Anna Eckerdal; Anders Berglund; Michael Thuné – European Journal of Engineering Education, 2024
Learning in the computer laboratory is an important component when students learn computer programming. In this article, we analyse empirical data on novice students working in pairs in the laboratory. Using an approach inspired by phenomenography and variation theory, we specifically focus on how students' learning of theory and their learning of…
Descriptors: Programming, Theory Practice Relationship, Higher Education, Computer Science Education
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Ismaila Temitayo Sanusi; Enoch Shadrack Cudjoe; Musa Adekunle Ayanwale; Bisola Adepoju – SAGE Open, 2025
The increased trend of incorporating computer programming in the basic education system across countries requires the training of new educators. However, the current effort to increase the number of teachers teaching programming is through professional development programs for computer science (CS) teachers and from other content areas. Meanwhile,…
Descriptors: Preservice Teachers, Student Attitudes, Programming, Computer Science Education
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Jeremy J. Blum – Discover Education, 2023
A wide range of benefits have been posited from participation in competitive programming contests. However, an analysis of participation in north American regional contests in the International Collegiate Programming Contest (ICPC) shows that participation in these contests is sharply declining, coinciding with the COVID-19 pandemic. Moreover,…
Descriptors: Programming, Higher Education, Competition, Trend Analysis
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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
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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
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Andrew Millam; Christine Bakke – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve…
Descriptors: Coding, Artificial Intelligence, Natural Language Processing, Computer Software
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Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
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Ivanilse Calderon; Williamson Silva; Eduardo Feitosa – Informatics in Education, 2024
Teaching programming is a complex process requiring learning to develop different skills. To minimize the challenges faced in the classroom, instructors have been adopting active methodologies in teaching computer programming. This article presents a Systematic Mapping Study (SMS) to identify and categorize the types of methodologies that…
Descriptors: Foreign Countries, Undergraduate Study, Programming, Computer Science Education
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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
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Cheers, Hayden; Lin, Yuqing – Computer Science Education, 2023
Background and Context: Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism…
Descriptors: Plagiarism, Programming, Computer Science Education, Identification
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Karnalim, Oscar; Simon; Chivers, William – IEEE Transactions on Learning Technologies, 2023
We have recently developed an automated approach to reduce students' rationalization of programming plagiarism and collusion by informing them about the matter and reporting uncommon similarities to them for each of their submissions. Although the approach has benefits, it does not greatly engage students, which might limit those benefits. To…
Descriptors: Gamification, Programming, Plagiarism, Cooperative Learning
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Shindler, Michael; Pinpin, Natalia; Markovic, Mia; Reiber, Frederick; Kim, Jee Hoon; Carlos, Giles Pierre Nunez; Dogucu, Mine; Hong, Mark; Luu, Michael; Anderson, Brian; Cote, Aaron; Ferland, Matthew; Jain, Palak; LaBonte, Tyler; Mathur, Leena; Moreno, Ryan; Sakuma, Ryan – Computer Science Education, 2022
Background and Context: We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in both large public and…
Descriptors: Misconceptions, Programming, Computer Science Education, Replication (Evaluation)
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Fegely, Alex; Tang, Hengtao – Educational Technology Research and Development, 2022
The purpose of this convergent mixed-methods study was to evaluate the effect of educational robotics on pre-service teachers' programming comprehension and motivation. Computer science is increasingly being integrated into K-8 curricula. However, a shortage of teachers trained to teach basic computer science concepts remains unresolved. This…
Descriptors: Programming, Robotics, Preservice Teachers, Comprehension
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