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Xiao-Feng Kenan Kok – Metacognition and Learning, 2025
Effective collaboration depends on the mutual regulation of group members, positioning it as a fundamental aspect of socially shared regulation of learning (SSRL). In recent years, SSRL has garnered increasing research attention. However, there remains a scarcity of psychometrically sound measures for assessing this construct. Without reliable…
Descriptors: Cooperation, Learning Processes, Measurement Techniques, Surveys
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Behçet Oral; Nese Dokumaci-Sütçü – Journal of Theoretical Educational Science, 2025
The aim of this study is to develop a valid and reliable scale to measure the time traps teachers fall into during the teaching-learning process. The sample consists of 234 final-year students continuing their education at the Faculty of Education in the first implementation and 233 pedagogical formation students in the second implementation.…
Descriptors: Test Construction, Time Management, Test Validity, Test Reliability
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Anirudhan Badrinath; Zachary Pardos – Journal of Educational Data Mining, 2025
Bayesian Knowledge Tracing (BKT) is a well-established model for formative assessment, with optimization typically using expectation maximization, conjugate gradient descent, or brute force search. However, one of the flaws of existing optimization techniques for BKT models is convergence to undesirable local minima that negatively impact…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Problem Solving, Audience Response Systems
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Limin Wang; Qing Zhou; Le Ji; Huiying Zhang; Ye Li – Journal of Baltic Science Education, 2025
Evaluating students' cognitive understanding of core concepts and identifying learning path aligned with their cognitive developmental patterns are critical for enhancing conceptual mastery in chemistry. This study employed the Rule Space Model to investigate students' cognitive characteristics and learning path in comprehending the conceptual…
Descriptors: Secondary School Students, Foreign Countries, Chemistry, Scientific Concepts
Paul T. von Hippel; Brendan A. Schuetze – Annenberg Institute for School Reform at Brown University, 2025
Researchers across many fields have called for greater attention to heterogeneity of treatment effects--shifting focus from the average effect to variation in effects between different treatments, studies, or subgroups. True heterogeneity is important, but many reports of heterogeneity have proved to be false, non-replicable, or exaggerated. In…
Descriptors: Educational Research, Replication (Evaluation), Generalizability Theory, Inferences
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Mohammad Nayef Ayasrah; Mohamad Ahmad Saleem Khasawneh; Mazen Omar Almulla; Amoura Hassan Aboutaleb – Journal of Computer Assisted Learning, 2025
Background: One area that has been dramatically changed by artificial intelligence (AI) is educational environments. Chatbots, Recommender Systems, Adaptive Learning Systems and Large Language Models have been emerging as practical tools for facilitating learning. However, using such tools appropriately is challenging. In this regard, the…
Descriptors: Test Construction, Test Validity, Test Reliability, Rating Scales
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Sharon Wolf; Matthew C. H. Jukes; Hirokazu Yoshikawa; Jonathan Stern; Sharon Kim – Early Childhood Education Journal, 2025
High-quality early childhood education provides children with opportunities for engaged learning. Yet there are currently no classroom-level measures that focus specifically on how teachers support engagement in the classroom, a key underlying dimension of playful learning. We introduce the Playful Learning Across the Years (PLAY) observational…
Descriptors: Foreign Countries, Preschool Children, Preschool Curriculum, Preschool Education
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Dina Fitria Murad; Meta Amalya Dewi; Arbaiah Inn; Silvia Ayunda Murad; Noor Udin; Taufik Darwis – Journal of Educators Online, 2025
This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…
Descriptors: Electronic Learning, Data Collection, Context Effect, Learning Processes