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Dinsmore, Daniel L.; Fryer, Luke K.; Dumas, Denis G. – Educational Psychology Review, 2023
The literature on cognitive processing and strategic processing is murky with regard to how these types of processing influence learning. One reason for this is that the frameworks used to investigate these relations have separately focused on different aspects related to cognitive processing with little integration between them. To address these…
Descriptors: Cognitive Processes, Models, Barriers, Learning
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Julius Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Instructional Science: An International Journal of the Learning Sciences, 2024
Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with…
Descriptors: Problem Based Learning, Problem Solving, Prompting, Models
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Zheng Liu; Jiahui Wen; Yikang Liu; Chuan-Peng Hu – British Journal of Educational Psychology, 2024
Background: Self-related information is difficult to ignore and forget, which brings valuable implications for educational practice. Self-referential encoding techniques involve integrating self-referencing cues during the processing of learning material. However, the evidence base and effective implementation boundaries for these techniques in…
Descriptors: Self Concept, Meta Analysis, Student Attitudes, Models
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Julius Moritz Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can…
Descriptors: Video Technology, Models, Cues, Problem Solving
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Khong, Hou Keat; Kabilan, Muhammad Kamarul – Computer Assisted Language Learning, 2022
The notion of "Micro-Learning" (ML) has been repeatedly accented as a successful learning approach in different learning phenomena. Despite these optimistic emphases, several studies lack a theoretical grounding in adoption of ML, thus missing a shared perspective of the education community. The scarce theoretical justification for…
Descriptors: Second Language Instruction, Cognitive Processes, Difficulty Level, Self Determination
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Gyöngyvér Molnár; Ádám Kocsis – Studies in Higher Education, 2024
How important are learning strategies or personal attributes for learning outside of domain-specific knowledge or twenty-first-century transversal skills when predicting academic success in higher education? To address this question, we conducted a longitudinal study among 1,681 students at one of the leading universities in Hungary. Students took…
Descriptors: Academic Achievement, Predictor Variables, Higher Education, Learning Strategies
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Li, Hongli; Kim, Min Kyu; Xiong, Yao – American Journal of Distance Education, 2020
Researchers have been interested in classifying massive open online course (MOOC) students based on their learning behaviors. However, less attention has been paid to the cognitive attributes associated with various learning behaviors. In this study, we propose a conceptual model that links MOOC students' observable learning behaviors to their…
Descriptors: Learning Strategies, Cognitive Processes, Student Behavior, Online Courses
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Özkubat, Ufuk; Karabulut, Alpaslan; Özmen, Emine Rüya – International Electronic Journal of Elementary Education, 2020
Being a cognitive strategy instruction model called 'Solve It!' involves cognitive and metacognitive elements. The model was developed by Montague (1992) as one of the process-based teaching strategies. The purpose of 'Solve It!' strategy is to teach the following seven cognitive strategy steps: read, paraphrase, visualize, hypothesize, predict,…
Descriptors: Mathematics Instruction, Problem Solving, Cognitive Processes, Special Needs Students
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Fenanlampir, Albertus; Leasa, Marleny; Batlolona, John Rafafy – International Journal of Evaluation and Research in Education, 2021
Many future studies have been developed by scientists today in the form of methods, models, strategies, and techniques in improving student learning outcomes that are oriented to psychology and the development of students' intelligence. One of the latest innovations in learning offered in this study is the homogeneity psycho cognition (HPC)…
Descriptors: Foreign Countries, Cognitive Processes, Physical Education, Teaching Methods
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Zhang, Jiayi; Andres, Juliana Ma. Alexandra L.; Hutt, Stephen; Baker, Ryan S.; Ocumpaugh, Jaclyn; Nasiar, Nidhi; Mills, Caitlin; Brooks, Jamiella; Sethuaman, Sheela; Young, Tyron – Journal of Educational Data Mining, 2022
Self-regulated learning (SRL) is a critical component of mathematics problem-solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model (Winne, 2017),…
Descriptors: Problem Solving, Mathematics Instruction, Learning Management Systems, Learning Analytics
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Jannin, Leslie; Ganier, Franck; De Vries, Philine – Journal of Educational Psychology, 2019
When learning procedures in real life, learners generally use action atomization strategies (interleaving instructions consultation and execution) and need several repetitions to acquire the skill. However, in studies on procedural learning, delayed execution paradigms (2 separate steps consisting of instructions consultation, then execution)…
Descriptors: Learning Strategies, Undergraduate Students, Medical Students, Repetition
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Dierendonck, Christophe; Milmeister, Paul; Kerger, Sylvie; Poncelet, Débora – International Journal of Behavioral Development, 2020
Few studies have used exploratory factor analysis (EFA) and exploratory bifactor factor analysis (EBFA) to define a baseline factor structure model checking the construct-relevant psychometric multidimensionality of student engagement. This study was conducted on a sample of 3,374 students in France, Wallonia-Brussels Federation, and Luxembourg by…
Descriptors: Learner Engagement, Behavior Problems, Foreign Countries, Secondary School Students
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Sunawan; Xiong, Junmei – Turkish Online Journal of Educational Technology - TOJET, 2017
The present study tested the influence of control belief, learning disorientation, and academic emotions on cognitive load in two types of concept-map structures within hypermedia learning environment. Four hundred and eighty-five students were randomly assigned to two groups: 245 students in the hierarchical group and 240 students in the…
Descriptors: Cognitive Processes, Difficulty Level, Beliefs, Psychological Patterns
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Banire, Bilikis; Jomhari, Nazean; Ahmad, Rodina – Journal of Autism and Developmental Disorders, 2015
The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid…
Descriptors: Autism, Children, Visual Learning, Instructional Design
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Lee, Shinyoung; Kang, Eunhee; Kim, Heui-Baik – Journal of Science Education and Technology, 2015
This study aimed to explore the effect on group dynamics of statements associated with deep learning approaches (DLA) and their contribution to cognitive collaboration and model development during group modeling of blood circulation. A group was selected for an in-depth analysis of collaborative group modeling. This group constructed a model in a…
Descriptors: Group Dynamics, Cooperative Learning, Models, Group Activities
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