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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
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
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
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
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
Hanzhu Yang; Linlin Hu; Hao Wang; Yunfei Xin – Journal of Computer Assisted Learning, 2025
Background: Computational thinking (CT) is a cognitive approach intricately linked with core competencies in Science, Technology, Engineering, and Mathematics (STEM). Numerous studies have explored strategies to effectively integrate CT into STEM education and systematically evaluated the multidimensional impact on student learning outcomes.…
Descriptors: Integrated Activities, Computation, Thinking Skills, STEM Education
Yin-Rong Zhang; Zhong-Mei Han; Tao He; Chang-Qin Huang; Fan Jiang; Gang Yang; Xue-Mei Wu – Journal of Computer Assisted Learning, 2025
Background: Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance. Objectives: This study…
Descriptors: Middle School Students, Grade 8, Scaffolding (Teaching Technique), Programming
Huiyan Ye; Biyao Liang; Oi-Lam Ng – Journal of Computer Assisted Learning, 2025
Background: Empirical studies have revealed students' development of computational thinking (CT) and mathematical thinking (MT) during programming-based mathematical problem-solving, highlighting specific CT concepts or practices that serve as learning goals or outcomes. However, implementing programming-based mathematics instruction requires…
Descriptors: Mathematics Instruction, Teaching Methods, Programming, Computation
Chiao Ling Huang; Lianzi Fu; Shih-Chieh Hung; Shu Ching Yang – Journal of Computer Assisted Learning, 2025
Background: Many studies have highlighted the positive effects of visual programming instruction (VPI) on students' learning experiences, programming self-efficacy and flow experience. However, there is a notable gap in the research on how these factors specifically impact programming achievement and learning intentions. Our study addresses this…
Descriptors: Attention, Self Efficacy, Visual Aids, Instructional Effectiveness
Meina Zhu – Journal of Computer Assisted Learning, 2025
Background: Computer programming learning and education play a critical role in preparing a workforce equipped with the necessary skills for diverse fields. ChatGPT and YouTube are technologies that support self-directed programming learning. Objectives: This study aims to examine the sentiments and primary topics discussed in YouTube comments…
Descriptors: Computer Science Education, Programming, Social Media, Video Technology
Niina Niinimäki; Kati Sormunen; Pirita Seitamaa-Hakkarainen; Sini Davies; Kaiju Kangas – Journal of Computer Assisted Learning, 2025
Background: Implementing maker education in schools is on the rise, fuelled by its potential to move formal education towards a creative, technology-driven 21st century learning culture. In maker education, collaborative learning takes place through and around various digital and traditional technologies, which provide the means for students'…
Descriptors: Cooperative Learning, Experiential Learning, Technological Literacy, Student Projects
Merve Aydin; Ünal Çakiroglu – Journal of Computer Assisted Learning, 2025
Background: Students experience higher-order thinking skills by finding ways to solve the problem, debugging errors while applying the solution, and testing the solution in programming. However, the inability to create schemas that will characterise programming structures is one of the difficulties during this process. Objectives: This study aimed…
Descriptors: Programming, Computer Science Education, Thinking Skills, Problem Solving

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