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Showing 1 to 15 of 97 results Save | Export
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Yuqin Yang; Xueqi Feng; Gaoxia Zhu; Kui Xie – Journal of Computer Assisted Learning, 2024
Background: Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer-supported collaborative inquiry approaches, such as KB--the focus of this…
Descriptors: Undergraduate Students, Cooperative Learning, Epistemology, Inquiry
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Shadi Esnaashari; Lesley Gardner; Michael Rehm; Tiru Arthanari; Olga Filippova – Journal of Computer Assisted Learning, 2025
Background: Self-Regulated Learning (SRL) plays a crucial role in student success, particularly in blended learning (BL) environments where learners must take greater ownership of their educational journey. Whilst prior research has extensively examined SRL, there remains a gap in understanding how students' SRL profiles evolve over time and how…
Descriptors: Blended Learning, Learning Strategies, College Freshmen, Undergraduate Students
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Hüseyin Ates; Mustafa Köroglu – Journal of Computer Assisted Learning, 2024
Background: Online collaboration tools have been identified as potentially effective means for enhancing student learning, motivation, and engagement in science education. However, their effectiveness in improving science education outcomes among middle school students remains uncertain. Objectives: The study aimed to investigate the impact of…
Descriptors: Cooperative Learning, Comparative Analysis, Academic Achievement, Learner Engagement
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Geng, Xuewang; Yamada, Masanori – Journal of Computer Assisted Learning, 2023
Background: Augmented reality has been widely applied in various fields, and its benefits in language learning have been increasingly recognized. However, the investigation of effective learning behaviours and processes in augmented reality learning environments, taking into account temporality and analysis of differences in learning behaviours…
Descriptors: Learning Analytics, Second Language Learning, Second Language Instruction, Learning Processes
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Carl Boel; Tijs Rotsaert; Martin Valcke; Tammy Schellens – Journal of Computer Assisted Learning, 2025
Background: As immersive virtual reality (IVR) is increasingly being used by teachers worldwide, it becomes pressing to investigate how this technology can foster learning processes. Several authors have pointed to this need, as results on the effectiveness of IVR for learning are still inconclusive. Objectives: To address this gap, we first…
Descriptors: Artificial Intelligence, Computer Simulation, Learning Strategies, Middle School Students
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Lahza, Hatim; Khosravi, Hassan; Demartini, Gianluca – Journal of Computer Assisted Learning, 2023
Background: The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.…
Descriptors: Learning Analytics, Learning Strategies, Electronic Learning, Independent Study
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Fan, Yizhou; Tan, Yuanru; Rakovic, Mladen; Wang, Yeyu; Cai, Zhiqiang; Shaffer, David Williamson; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and…
Descriptors: MOOCs, Students, Learning Processes, Learning Strategies
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Lanqin Zheng; Yunchao Fan; Zichen Huang; Lei Gao – Journal of Computer Assisted Learning, 2024
Background: Online collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning. Objectives: The study compared the impacts of the three learning approaches on collaborative knowledge building,…
Descriptors: Cooperative Learning, Electronic Learning, College Students, Learning Strategies
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Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
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Tornike Giorgashvili; Ioana Jivet; Cordula Artelt; Daniel Biedermann; Daniel Bengs; Frank Goldhammer; Carolin Hahnel; Julia Mendzheritskaya; Julia Mordel; Monica Onofrei; Marc Winter; Ilka Wolter; Holger Horz; Hendrik Drachsler – Journal of Computer Assisted Learning, 2025
Background: Learning analytics dashboards (LAD) have been developed as feedback tools to help students self-regulate their learning (SRL) by using the large amounts of data generated by online learning platforms. Despite extensive research on LAD design, there remains a gap in understanding how learners make sense of information visualised on LADs…
Descriptors: Field Studies, Student Reaction, Feedback (Response), Learning Analytics
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Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
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Li, Shuang; Wang, Shuang; Du, Junlei; Pei, Yu; Shen, Xinyi – Journal of Computer Assisted Learning, 2022
Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course…
Descriptors: Online Courses, Time Management, Time Factors (Learning), Learning Strategies
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Yu Gao; Linjing Wu; Xiaotong Lv; Xinqian Ma; Qingtang Liu – Journal of Computer Assisted Learning, 2024
Background: Both socially regulated learning and cognitive quality are important factors affecting collaborative knowledge building, but the current research lacks a joint quantified evaluation method that combines these two aspects. Objectives: Based on the existing framework, we proposed a joint evaluation method for regulated learning and…
Descriptors: Self Management, Cooperative Learning, Learning Strategies, Evaluation Methods
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Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
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Kun Huang; Ching-Huei Chen – Journal of Computer Assisted Learning, 2025
Background: Digital game-based learning (DGBL) has shown promise in enhancing learning and motivation, with appropriate scaffolding playing a crucial role in facilitating student inquiries and knowledge acquisition through science games. While scaffolding is generally effective in promoting learning in DGBL, there is variability among different…
Descriptors: Video Technology, Educational Technology, Artificial Intelligence, Computer Mediated Communication
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