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Malmberg, Jonna; Saqr, Mohammed; Järvenoja, Hanna; Järvelä, Sanna – Journal of Learning Analytics, 2022
The current study uses a within-person temporal and sequential analysis to understand individual learning processes as part of collaborative learning. Contemporary perspectives of self-regulated learning acknowledge monitoring as a crucial mechanism for each phase of the regulated learning cycle, but little is known about the function of the…
Descriptors: High School Students, Group Activities, Learning Processes, Student Motivation
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Lars de Vreugd; Anouschka van Leeuwen; Renée Jansen; Marieke van der Schaaf – Journal of Learning Analytics, 2024
For university students, self-regulation of study behaviour is important. However, students are not always capable of effective self-regulation. Providing study behaviour information via a learning analytics dashboard (LAD) may support phases within self-regulated learning (SRL). However, it is unclear what information a LAD should provide, how to…
Descriptors: Learning Management Systems, Learning Analytics, Student Behavior, Behavior Patterns
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Joel Weijia Lai; Wei Qiu; Maung Thway; Lei Zhang; Nurabidah Binti Jamil; Chit Lin Su; Samuel S. H. Ng; Fun Siong Lim – Journal of Learning Analytics, 2025
The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot,…
Descriptors: Artificial Intelligence, Computer Software, Learning Analytics, Introductory Courses
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Zhang Yingbin; Paquette, Luc; Baker, Ryan S.; Ocumpaugh, Jaclyn; Bosch, Nigel; Biswas, Gautam; Munshi, Anabil – Journal of Learning Analytics, 2021
Confusion may benefit learning when it is resolved or partially resolved. Metacognitive strategies (MS) may help learners to resolve confusion when it occurs during learning and problem solving. This study examined the relationship between confusion and MS that students evoked in Betty's Brain, a computer-based learning-by-modelling environment…
Descriptors: Metacognition, Brain, Grade 6, Emotional Response
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Zhongzhou Chen; Tom Zhang; Michelle Taub – Journal of Learning Analytics, 2024
The current study measures the extent to which students' self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access…
Descriptors: Learning Analytics, Algorithms, Instructional Materials, Course Content
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Rau, Martina A. – Journal of Learning Analytics, 2017
STEM instruction often uses visual representations. To benefit from these, students need to understand how representations show domain-relevant concepts. Yet, this is difficult for students. Prior research shows that physical representations (objects that students manipulate by hand) and virtual representations (objects on a computer screen that…
Descriptors: High School Students, Charter Schools, STEM Education, Knowledge Representation
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Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – Journal of Learning Analytics, 2015
Researchers have long recognized the potential benefits of open-ended computer- based learning environments (OELEs) to help students develop self-regulated learning (SRL) behaviours. However, measuring self-regulation in these environments is a difficult task. In this paper, we present our work in developing and evaluating "coherence…
Descriptors: Educational Environment, Computer Assisted Instruction, Independent Study, Learning Strategies