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Icy Zhang; Yunqi Jia; Xiaoxuan Cheng; Ji Y. Son; James W. Stigler – Journal of Educational Computing Research, 2025
Although programming is often learned through formal instruction or self-paced tutorials, informal learning, for example, through publicly available online documentation, is also a significant resource for skill development among novices. However, many novices struggle to extract useful information from documentation. This work aims to answer two…
Descriptors: Programming, Novices, Informal Education, Documentation
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Jaewon Jung; Yoonhee Shin; HaeJin Chung; Mik Fanguy – Journal of Computing in Higher Education, 2025
This study investigated the effects of pre-training types on cognitive load, self-efficacy, and problem-solving in computer programming. Pre-training was provided to help learners acquire schemas related to problem-solving strategies. 84 undergraduate students were randomly assigned to one of three groups and each group received three different…
Descriptors: Training, Cognitive Processes, Difficulty Level, Self Efficacy
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Yoonhee Shin; Jaewon Jung; Seohyun Choi; Bokmoon Jung – Education and Information Technologies, 2025
This study investigates the effects of metacognitive and cognitive strategies for computational thinking (CT) on managing cognitive load and enhancing problem-solving skills in collaborative programming. Four different scaffolding conditions were provided to help learners optimize cognitive load and improve their problem-solving abilities. A total…
Descriptors: Scaffolding (Teaching Technique), Mental Computation, Cognitive Processes, Difficulty Level
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Christina Glasauer; Martin K. Yeh; Lois Anne DeLong; Yu Yan; Yanyan Zhuang – Computer Science Education, 2025
Background and Context: Feedback on one's progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective: We investigate the potential use of a…
Descriptors: Novices, Computer Science Education, Programming, Accuracy
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Saso Koceski; Natasa Koceska; Limonka Koceva Lazarova; Marija Miteva; Biljana Zlatanovska – Journal of Technology and Science Education, 2025
This study aims to evaluate ChatGPT's capabilities in certain numerical analysis problem: solving ordinary differential equations. The methodology which is developed in order to conduct this research takes into account the following mathematical abilities (defined according to National Centre for Education Statistics): Conceptual Understanding,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Number Concepts, Problem Solving
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Busra Ozmen Yagiz; Ecenaz Alemdag – Education and Information Technologies, 2025
Resilience is a critical personality trait that allows one to deal with difficulties, learn from failures, and maintain a positive attitude during task performance. However, it has not been understudied in a complex and challenging educational domain. The current research intends to address this gap by analyzing the specific characteristics of…
Descriptors: Foreign Countries, Undergraduate Students, Resilience (Psychology), Programming
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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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Haoming Wang; Chengliang Wang; Zhan Chen; Fa Liu; Chunjia Bao; Xianlong Xu – Education and Information Technologies, 2025
With the rapid development of artificial intelligence technology in the field of education, AI-Agents have shown tremendous potential in collaborative learning. However, traditional Computer-Supported Collaborative Learning (CSCL) methods still have limitations in addressing the unique demands of programming education. This study proposes an…
Descriptors: Artificial Intelligence, Cooperative Learning, Programming, Computer Science Education
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Xuanyan Zhong; Zehui Zhan – Interactive Technology and Smart Education, 2025
Purpose: The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners' computational thinking. Design/methodology/approach: By…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming, Independent Study
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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