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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
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
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)
Jana Gonnermann-Müller; Jule M. Krüger – Journal of Computer Assisted Learning, 2025
Background: Despite the numerous positive effects of augmented reality (AR) on learning, previous research has shown ambiguous results regarding the cognitive demand on the learner arising from, for example, the overlay of virtual elements or novel interaction techniques. At the same time, the number of evidence-based guidelines on designing AR is…
Descriptors: Computer Simulation, Computer Assisted Design, Difficulty Level, Cognitive Processes
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
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
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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