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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
Ragonis, Noa; Shmallo, Ronit – Informatics in Education, 2022
Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of…
Descriptors: Programming, Computer Science Education, Concept Formation, Thinking Skills
Eunsung Park; Jongpil Cheon – Journal of Educational Computing Research, 2025
Debugging is essential for identifying and rectifying errors in programming, yet time constraints and students' trivialization of errors often hinder progress. This study examines differences in debugging challenges and strategies among students with varying computational thinking (CT) competencies using weekly coding journals from an online…
Descriptors: Undergraduate Students, Programming, Computer Software, Troubleshooting
Mahatanankoon, Pruthikrai; Wolf, James – Information Systems Education Journal, 2021
Learning a computer programming language is typically one of the basic requirements of being an information technology (IT) major. While other studies previously investigate computer programming self-efficacy and grit, their relationships between "shallow" and "deep" learning (Miller et al., 1996) have not been thoroughly…
Descriptors: Cognitive Processes, Learning Strategies, Introductory Courses, Computer Science Education
Yang Shi; Robin Schmucker; Keith Tran; John Bacher; Kenneth Koedinger; Thomas Price; Min Chi; Tiffany Barnes – Journal of Educational Data Mining, 2024
Understanding students' learning of knowledge components (KCs) is an important educational data mining task and enables many educational applications. However, in the domain of computing education, where program exercises require students to practice many KCs simultaneously, it is a challenge to attribute their errors to specific KCs and,…
Descriptors: Programming Languages, Undergraduate Students, Learning Processes, Teaching Models
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
Troussas, Christos; Krouska, Akrivi; Sgouropoulou, Cleo – IEEE Transactions on Education, 2021
Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students' cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students' knowledge level to fuzzy weights, and using rule-based decision making, delivers…
Descriptors: Undergraduate Students, Intelligent Tutoring Systems, Computer Science Education, Programming
Hao, Xiaoxin; Xu, Zhiyi; Guo, Mingyue; Hu, Yuzheng; Geng, Fengji – International Journal of STEM Education, 2023
Background: Coding has become an integral part of STEM education. However, novice learners face difficulties in processing codes within embedded structures (also termed nested structures). This study aimed to investigate the cognitive mechanism underlying the processing of embedded coding structures based on hierarchical complexity theory, which…
Descriptors: Cognitive Processes, Difficulty Level, Programming, Computer Science Education
Qin, Chao; Liu, Yanjia; Zhang, Hemei – Journal of Computer Assisted Learning, 2023
Background: Being easy to learn and fun, block-based programming tools are widely used to teach students introductory programming. Scratch and LEGO robots are two popular block-based programming tools. However, the objects they manipulate are completely different. Scratch manipulates graphical virtual sprites, whereas LEGO robots manipulate…
Descriptors: Foreign Countries, Undergraduate Students, Learner Engagement, Robotics
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
Sanna Forsström; Melissa Bond – International Journal of Computer Science Education in Schools, 2024
Despite the widespread adoption of computational thinking (CT) across educational levels, challenges persist in its assessment due to diverse definitions, frameworks, and practical applications in classroom settings. This meta-synthesis investigates the assessment of computational thinking (CT) in primary and secondary education, synthesising…
Descriptors: Computation, Thinking Skills, Elementary Secondary Education, Barriers
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
Hugo G. Lapierre; Patrick Charland; Pierre-Majorique Léger – Computer Science Education, 2024
Background and Context: Current programming learning research often compares novices and experienced programmers, leaving early learning stages and emotional and cognitive states under-explored. Objective: Our study investigates relationships between cognitive and emotional states and learning performance in early stage programming learners with…
Descriptors: Programming, Computer Science Education, Psychological Patterns, Cognitive Processes
Paula Chica, Maritza Gisella; Aguilar Morocho, Elva Katherine – Journal of Language and Linguistic Studies, 2022
The objective of this research has been the analysis of the effectiveness of sports performance through a program, based on the assimilation of contents from a previous diagnosis. The almost-experimental design was applied like a pre- during program. Eighty-two students from Basic Education Career of UPSE have participated in this study, developed…
Descriptors: Foreign Countries, Attention, Athletics, College Students
Minji Jeon; Kyungbin Kwon – TechTrends: Linking Research and Practice to Improve Learning, 2024
This study investigated the computational thinking (CT) practices of eight pre-service teachers through their Scratch and Python programs. Conducted within an undergraduate-level computer science education course, students learned CT concepts via parallel instruction in block-based programming (Scratch) and text-based programming (Python). The…
Descriptors: Preservice Teacher Education, Preservice Teachers, Computation, Cognitive Processes

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