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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
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Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michail – Journal of Computer Assisted Learning, 2022
Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data).…
Descriptors: Cognitive Processes, Psychological Patterns, Problem Solving, Programming
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Aris, Baharuddin; Gharbaghi, Alireza; Ahmad, Maizah Hura; Rosli, Mohd Shafie – International Education Studies, 2013
The main purpose of this study is to introduce a check list for evaluating persuasive features of mathematics courseware. Since mathematics is a source of anxiety among students (Zeidner & Matthews, 2010), this research is an attempt to employ persuasive features that can be used in mathematics courseware. Specifically, we sought to determine…
Descriptors: Check Lists, Courseware, Mathematics Instruction, Teaching Methods
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Schroeder, Noah L.; Adesope, Olusola O.; Gilbert, Rachel Barouch – Journal of Educational Computing Research, 2013
Research on the use of software programs and tools such as pedagogical agents has peaked over the last decade. Pedagogical agents are on-screen characters that facilitate instruction. This meta-analysis examined the effect of using pedagogical agents on learning by reviewing 43 studies involving 3,088 participants. Analysis of the results…
Descriptors: Meta Analysis, Cybernetics, Artificial Intelligence, Technology Uses in Education
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Lin, Lijia; Atkinson, Robert K. – Journal of Educational Computing Research, 2013
The purpose of the two experiments was to investigate the potential effects of different types of visualizations and self-explanation prompts on learning human cardiovascular system in a multimedia environment. In Experiments 1 and 2, 70 and 44 college students were randomly assigned to one of the four conditions in a 2 × 2 factorial design with…
Descriptors: Educational Experiments, Visualization, Prompting, Human Body
Nguyen, Hien Thu – ProQuest LLC, 2009
Research shows both benefits and challenges of online discussion as a collaborative learning activity. Online discussion is especially challenging for novice college students who have limited metacognitive skills as well as limited knowledge of the subject domain. With limited metacognitive skills, it can be challenging for novice students to…
Descriptors: Computer Mediated Communication, Discussion (Teaching Technique), Metacognition, Cognitive Processes
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Haenggi, Dieter; Perfetti, Charles A. – Journal of Educational Psychology, 1992
Decoding, working memory, and domain-specific prior knowledge were studied as predictors of comprehension for 48 university undergraduate students after rewriting notes, rereading notes, or rereading a text. Working memory was most important for comprehension of text-implicit information, whereas knowledge was relatively more important for…
Descriptors: Coding, Cognitive Processes, Comparative Analysis, Higher Education
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection