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Dan Sun; Fan Xu – Journal of Educational Computing Research, 2025
Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when…
Descriptors: Cooperative Learning, Programming, Computer Science Education, Undergraduate Students
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Anna Y. Q. Huang; Cheng-Yan Lin; Sheng-Yi Su; Stephen J. H. Yang – British Journal of Educational Technology, 2025
Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Coding, Programming
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Rajagopal Sankaranarayanan; Mohan Yang; Kyungbin Kwon – Journal of Computing in Higher Education, 2025
The purpose of this study is to explore the influence of the microlearning instructional approach in an online introductory database programming classroom. The ultimate goal of this study is to inform educators and instructional designers on the design and development of microlearning content that maximizes student learning. Grounded within the…
Descriptors: Teaching Methods, Introductory Courses, Databases, Programming
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Margulieux, Lauren; Parker, Miranda C.; Cetin Uzun, Gozde; Cohen, Jonathan D. – Journal of Technology and Teacher Education, 2023
Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which--and to what extent--these concepts are included in these integrated experiences, especially when compared to…
Descriptors: Programming, Programming Languages, Computer Science Education, Age Differences
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Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
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Chen Sun; Stephanie Yang; Betsy Becker – Journal of Educational Computing Research, 2024
Computational thinking (CT), an essential 21st century skill, incorporates key computer science concepts such as abstraction, algorithms, and debugging. Debugging is particularly underrepresented in the CT training literature. This multi-level meta-analysis focused on debugging as a core CT skill, and investigated the effects of various debugging…
Descriptors: Troubleshooting, Computation, Thinking Skills, Intervention
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Erkan Er; Gökhan Akçapinar; Alper Bayazit; Omid Noroozi; Seyyed Kazem Banihashem – British Journal of Educational Technology, 2025
Despite the growing research interest in the use of large language models for feedback provision, it still remains unknown how students perceive and use AI-generated feedback compared to instructor feedback in authentic settings. To address this gap, this study compared instructor and AI-generated feedback in a Java programming course through an…
Descriptors: Student Evaluation, Student Attitudes, Feedback (Response), Artificial Intelligence
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Xing, Wanli – Interactive Learning Environments, 2021
Previous research has invested much effort in understanding how programming can contribute to the development of young learners' computational thinking (CT) in traditional K-12 classroom settings. Relatively few studies have examined programming for CT in informal online communities, especially for large scale quantitative research. With the…
Descriptors: Programming, Thinking Skills, Computation, Programming Languages
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Cheers, Hayden; Lin, Yuqing; Yan, Weigen – Informatics in Education, 2023
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, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work…
Descriptors: Plagiarism, Assignments, Computer Software, Computer Science Education
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Wen-shuang Fu; Jia-hua Zhang; Di Zhang; Tian-tian Li; Min Lan; Na-na Liu – Journal of Educational Computing Research, 2025
Cognitive ability is closely associated with the acquisition of programming skills, and enhancing learners' cognitive ability is a crucial factor in improving the efficacy of programming education. Adaptive feedback strategies can provide learners with personalized support based on their learning context, which helps to stimulate their interest…
Descriptors: Feedback (Response), Cognitive Ability, Programming, Computer Science Education
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Ma, Yingbo; Katuka, Gloria Ashiya; Celepkolu, Mehmet; Boyer, Kristy Elizabeth – International Educational Data Mining Society, 2022
Collaborative learning is a complex process during which two or more learners exchange opinions, construct shared knowledge, and solve problems together. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. If intelligent systems could predict…
Descriptors: Middle School Students, Cooperative Learning, Prediction, Peer Relationship
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Garcia, Manuel B. – Journal of Educational Computing Research, 2023
Computer programming is a difficult course for many students. Prior works advocated for group learning pedagogies in pursuit of higher-level reasoning and conceptual understanding. However, the methodological gaps in existing implementations warrant further research. This study conducted a three-armed cluster-randomized controlled trial to…
Descriptors: Computer Science Education, Programming, Cooperative Learning, Apprenticeships
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Robbert Smit; Rahel Schmid; Nicolas Robin – British Journal of Educational Technology, 2025
Secondary school students (N = 269) participated in a daylong visual programming course held in a stimulating environment for start-up enterprises. The tasks were application-oriented and partly creative. For example, a wearable device with light-emitting diodes, (ie, LEDs) could be applied to a T-shirt and used for optical messages. Our research…
Descriptors: Self Efficacy, Gender Differences, Prediction, Student Attitudes
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Aykut Durak; Vahide Bulut – Technology, Knowledge and Learning, 2025
The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the…
Descriptors: Prediction, Low Achievement, High Achievement, Academic Achievement
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Cárdenas-Cobo, Jesennia; Puris, Amilkar; Novoa-Hernández, Pavel; Galindo, José Angel; Benavides, David – IEEE Transactions on Learning Technologies, 2020
Learning computer programming is a challenging process. Among the current approaches for overcoming this challenge, visual programming languages (VPLs), such as Scratch, have shown very promising results for beginners. Interestingly, some higher education institutions have started to use VPLs to introduce basic programming concepts, mainly in CS1…
Descriptors: Computer Science Education, Programming, Programming Languages, Teaching Methods
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