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Zhou, Ying; Chai, Ching Sing; Li, Xiuting; Ma, Chao; Li, Baoping; Yu, Ding; Liang, Jyh-Chong – Journal of Educational Computing Research, 2023
Computational thinking is a way of thinking that helps people "think like a computer scientist" to solve practical problems. However, practicing computational thinking through programming is dependent on the problem solvers' metacognition. This study investigated students' metacognitive planning and problem-solving performance in…
Descriptors: Metacognition, Computation, Thinking Skills, Problem Solving
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Shin, Yoonhee; Jung, Jaewon; Zumbach, Joerg; Yi, Eunseon – Journal of Educational Computing Research, 2023
This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners' knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: (1) task performance with…
Descriptors: Problem Solving, Metacognition, Scaffolding (Teaching Technique), Programming
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Chengliang Wang; Xiaojiao Chen; Yifei Li; Pengju Wang; Haoming Wang; Yuanyuan Li – Journal of Educational Computing Research, 2025
This study explored the impact of MetaClassroom, a virtual immersive programming learning environment designed based on the three-dimensional learning progression (3DLP) concept, on students' multidimensional development. Utilizing a quasi-experimental research design, this study compared students' programming learning achievements (PLA),…
Descriptors: Programming, Computer Science Education, Metacognition, Computer Simulation
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Lakshminarayanan, Srinivasan; Rao, N. J. – Cogent Education, 2021
CS1 courses are designed in Indian Institutions as a lecture course of three to four credits and one credit lab course. The issues related to curriculum design, instruction design, and students' learning manifest themselves as issues in the lab programs. This situation presents the lab instructor with an opportunity to understand and address the…
Descriptors: Computer Science Education, Teaching Methods, Programming, Programming Languages
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Shin, Yoonhee; Song, Donggil – Journal of Educational Computing Research, 2022
This study explores the effect of self-regulated learning support on learners' cognitive load and problem-solving performance, considering cases from well-structured to ill-structured tasks in a computer programing course. Sixty-seven undergraduate students in a computer programing fundamentals course were randomly assigned into one of two groups:…
Descriptors: Cognitive Ability, Computer Science Education, Programming, Problem Solving
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Christine Eith; Denise Zawada – Impacting Education: Journal on Transforming Professional Practice, 2025
This paper proposes a framework for integrating generative artificial intelligence (AI) tools into statistical training for Doctor of Education (EdD) students. The rigorous demands of doctoral education, coupled with the challenges of learning complex statistical software and coding language, often lead to anxiety and frustration among students,…
Descriptors: Doctoral Programs, Artificial Intelligence, Technology Integration, Statistics Education
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Tsai, Meng-Jung; Wang, Ching-Yeh; Hsu, Po-Fen – Journal of Educational Computing Research, 2019
Computer programming has been gradually emphasized in recent computer literacy education and regarded as a requirement for all middle school students in some countries. To understand young students' perceptions about their own learning in computer programming, this study aimed to develop an instrument, Computer Programming Self-Efficacy Scale…
Descriptors: Programming, Computer Literacy, Middle School Students, Student Attitudes
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Fung, Tze-ho; Li, Wing-yi – Practical Assessment, Research & Evaluation, 2022
Rough set theory (RST) was proposed by Zdzistaw Pawlak (Pawlak,1982) as a methodology for data analysis using the notion of discernibility of objects based on their attribute values. The main advantage of using RST approach is that it does not need additional assumptions--like data distribution in statistical analysis. Besides, it provides…
Descriptors: Gifted, Metacognition, Learning Strategies, Programming Languages
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Onah, Daniel F. O.; Pang, Elaine L. L.; Sinclair, Jane E.; Uhomoibhi, James – International Journal of Information and Learning Technology, 2021
Purpose: Massive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness in a blended-learning context. One of the major recurring issues raised…
Descriptors: MOOCs, Questionnaires, Learning Strategies, Blended Learning
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Gökçe, Semirhan; Yenmez, Arzu Aydogan; Özpinar, Ilknur – International Education Studies, 2017
Recent developments in technology have changed the learner's profile and the learning outcomes. Today, with the emergence of higher-order thinking skills and computer literacy skills, teaching through traditional methods is likely to fail to achieve the learning outcomes. That is why; teachers and teacher candidates are expected to have computer…
Descriptors: Foreign Countries, Undergraduate Students, Mathematics Education, Elementary School Mathematics
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Castledine, Alanah-Rei; Chalmers, Chris – Design and Technology Education, 2011
With the current curriculum focus on correlating classroom problem solving lessons to real-world contexts, are LEGO robotics an effective problem solving tool? This present study was designed to investigate this question and to ascertain what problem solving strategies primary students engaged with when working with LEGO robotics and whether the…
Descriptors: Foreign Countries, Problem Solving, Grade 6, Relevance (Education)
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Major, Nigel – Journal of Artificial Intelligence in Education, 1995
Describes a modelling language for representing teaching strategies, based in the context of the COCA intelligent tutoring system. Examines work on meta-reasoning in knowledge-based systems and describes COCA's architecture, giving details of the language used for representing teaching knowledge. Discusses implications for future work. (AEF)
Descriptors: Computer Assisted Instruction, Computer System Design, Computer Uses in Education, Educational Strategies