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Yanqing Wang; Shaoying Gong; Ning Jia; Ying Liu – Journal of Computer Assisted Learning, 2025
Background: Online learning is becoming increasingly popular among learners. To enhance the effectiveness of online learning, researchers have embedded an affective pedagogical agent (PA) on the computer screen to help regulate learners' emotions and support their learning. However, previous research has paid little attention to the effects of…
Descriptors: Metacognition, Prompting, Electronic Learning, Computer Uses in Education
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Zhe Wang; Sara Abercrombie; Rachel Wong; Yuxin Ren; Shiting Dai – Journal of Computer Assisted Learning, 2024
Background: There are two major types of pictures that have been the focus of multimedia learning research, namely, seductive and interpretational pictures. Despite an increasing body of literature documenting the effects of either seductive or interpretational pictures added to text-based materials, there is a paucity of research explicitly…
Descriptors: Electronic Learning, Computers, Computer Assisted Instruction, Visual Aids
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Yawen Yu; Yang Tao; Gaowei Chen; Can Sun – Journal of Computer Assisted Learning, 2024
Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning…
Descriptors: Learning Analytics, College Students, Epistemology, Computer Mediated Communication
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Yi-Fan Li; Jue-Qi Guan; Xiao-Feng Wang; Qu Chen; Gwo-Jen Hwang – Journal of Computer Assisted Learning, 2024
Background: Self-regulated learning (SRL) is a predictive variable in students' academic performance, especially in virtual reality (VR) environments, which lack monitoring and control. However, current research on VR encounters challenges in effective interventions of cognitive and affective regulation, and visualising the SRL processes using…
Descriptors: Electronic Learning, Individualized Instruction, Learning Processes, Performance
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Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models