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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
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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
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Changming Liang; Lei Du – Journal of Educational Computing Research, 2025
This study delved into the impact of educational robotics, virtual coding, and unplugged coding on the problem-solving, computational thinking (CT), and coding skills of English as a Foreign Language (EFL) learners. Employing a pretest-posttest experimental design, the study encompassed 351 EFL students distributed across four groups to compare…
Descriptors: Foreign Countries, College Students, English (Second Language), Second Language Learning
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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
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Sinan Onal; Derya Kulavuz-Onal; Marie Childers – Journal of Educational Technology Systems, 2025
This study investigates the integration and application of ChatGPT among U.S. higher education students across various academic disciplines. Given the recent introduction of ChatGPT in educational contexts, this research aims to understand the specific ways students utilize this tool for academic tasks and their perceived impact on their academic…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Benefits, Academic Achievement
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Chun-Ying Chen – ACM Transactions on Computing Education, 2025
This study examined the effects of worked examples with different explanation types and novices' motivation on cognitive load, and how this subsequently influenced their programming problem-solving performance. Given the study's emphasis on both instructional approaches and learner motivation, the Cognitive Theory of Multimedia Learning served as…
Descriptors: Models, Learning Motivation, Cognitive Processes, Difficulty Level
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Hüseyin Çakir – Journal of Learning and Teaching in Digital Age, 2025
This study aims to understand students' views on project development in coding and robotics courses. Focusing on student study groups in this field seeks to provide a broad view using qualitative and quantitative methods. The study group consists of students taking the coding and robotics course. A semi-structured interview form developed by the…
Descriptors: Student Attitudes, Program Development, Coding, Robotics
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Sokratis Tselegkaridis; Theodosios Sapounidis; Christos Tokatlidis; Dimitrios Papakostas – IEEE Transactions on Education, 2025
Contribution: This study focuses on microcontroller circuits and aims to: 1) investigate the impact of formal reasoning on students' post-knowledge using catastrophe theory; 2) compare the different combination sequences of tangible user interface (TUI) and graphical user interface (GUI); and 3) assess the usability of both interfaces and explore…
Descriptors: College Students, Electronics, Electronic Equipment, Engineering Education
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Zhizezhang Gao; Haochen Yan; Jiaqi Liu; Xiao Zhang; Yuxiang Lin; Yingzhi Zhang; Xia Sun; Jun Feng – International Journal of STEM Education, 2025
Background: With the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments,…
Descriptors: Computer Science Education, Mathematics Education, Novices, Programming