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Ting-Ting Wu; Hsin-Yu Lee; Pei-Hua Chen; Wei-Sheng Wang; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Conventional reflective learning methodologies in programming education often lack structured guidance and individualised feedback, limiting their pedagogical effectiveness. Whilst computational thinking (CT) offers a systematic problem-solving framework with decomposition, pattern recognition, abstraction, and algorithm design, its…
Descriptors: Computation, Thinking Skills, Educational Diagnosis, Diagnostic Tests
Kevin Sigayret; Nathalie Blanc; André Tricot – Journal of Computer Assisted Learning, 2025
Background: Teaching programming and computational thinking is becoming a major issue in many education systems. Numerous approaches are possible, but very few studies compare these different ways of implementing programming and computational thinking learning. Objectives: We compared three ways of teaching programming and computational thinking…
Descriptors: Educational Technology, Technology Uses in Education, Robotics, Computation
Dan Sun; Fan Ouyang; Yan Li; Chengcong Zhu; Yang Zhou – Journal of Computer Assisted Learning, 2024
Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major…
Descriptors: Computer Science Education, Programming, Computer Literacy, Comparative Analysis
Peng Chen; Dong Yang; Jia Zhao; Shu Yang; Jari Lavonen – Journal of Computer Assisted Learning, 2025
Background: Computational thinking (CT) refers to the ability to represent problems, design solutions and migrate solutions computationally. While previous studies have shown that self-explanation can enhance students' learning, few empirical studies have examined the effects of using different self-explanation prompts to cultivate students' CT…
Descriptors: Computation, Thinking Skills, Programming, Learning Processes
Shang Shanshan; Geng Sen – Journal of Computer Assisted Learning, 2024
Background: Artificial intelligence-generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC…
Descriptors: Artificial Intelligence, Educational Technology, Technology Integration, Programming
Yonggang Wei; Lu Wang; Yi Tang; Junyue Su; Yaxian Lei; Wanran Peng – Journal of Computer Assisted Learning, 2024
Background: The pedagogy of computational thinking has gained extensive traction across numerous nations globally, with a particular emphasis on nurturing computational thinking in the formative years of early childhood. Nevertheless, the efficacy of diverse instructional approaches in the domain of programming education, with regards to fostering…
Descriptors: Programming, Computation, Thinking Skills, Young Children
Xin Gong; Weiqi Xu; Ailing Qiao; Zhixia Li – Journal of Computer Assisted Learning, 2025
Background: Robot programming can simultaneously cultivate learners' computational thinking (CT) and spatial thinking (ST). However, there is a noticeable gap in research focusing on the micro-level development patterns of learners' CT and ST and their interconnections. Objectives: This study aims to uncover the intricate development patterns and…
Descriptors: Mental Computation, Thinking Skills, Skill Development, Robotics
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
Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
Temesgen Samuel; Hsiu-Ling Chen; Abebayehu Yohannes – Journal of Computer Assisted Learning, 2025
Background: As highly interactive hands-on learning tools, robots can inspire new generations of mathematics students. However, to date, no comprehensive systematic reviews have been conducted on robot-assisted mathematics education from K-12 through higher education. Hence, it is important to explore the research evidence of robot-assisted…
Descriptors: Mathematics Instruction, Robotics, Technology Uses in Education, Elementary Secondary Education
Fan Xu; Ana-Paula Correia – Journal of Computer Assisted Learning, 2025
Background: Computational thinking (CT) is an essential skill for preparing the younger generation to succeed in an AI-driven world, with pair programming emerging as a widely used approach to foster these skills. However, the role of individual factors and mutual engagement in shaping CT skills within pair programming remains underexplored,…
Descriptors: Computation, Thinking Skills, Learner Engagement, Middle School Students
Linlin Hu – Journal of Computer Assisted Learning, 2024
Background Study: As interest in programming education continues to grow, more educators are incorporating programming into K-12 classrooms. There is evidence that programming can help develop essential skills for future societal advancement, known as 21st century skills. However, there has been limited research into which sub-skills of 21st…
Descriptors: 21st Century Skills, Programming, Elementary Secondary Education, Futures (of Society)
Yanjia Liu; Chao Qin; Hao He – Journal of Computer Assisted Learning, 2024
Background: The world is moving towards digitalization and intelligence. Programming has become an essential development competency. Even though many countries are currently making great efforts to expand programming education, the programming education in these countries shows an imbalance in geographical and gender dimensions. We found that few…
Descriptors: Programming, Females, Womens Education, Rural Areas
Peidi Gu; Zui Cheng; Cheng Miaoting; John Poggio; Yan Dong – Journal of Computer Assisted Learning, 2025
Background: Today, the importance of STEM (Science, Technology, Engineering and Mathematics) education and training is widely recognised and accepted. Computer programming courses have become essential in higher education to nurture students' programming, analysis and computational skills, which are vital for success in all STEM fields and areas.…
Descriptors: Active Learning, Student Projects, Individualized Instruction, Student Motivation
Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
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