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Jansen, Renée S.; Leeuwen, Anouschka; Janssen, Jeroen; Kester, Liesbeth – Journal of Computer Assisted Learning, 2022
Background: Learners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self-regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching…
Descriptors: Student Behavior, Learning Processes, Online Courses, Personal Autonomy
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Okan Bulut; Guher Gorgun; Seyma Nur Yildirim-Erbasli – Journal of Computer Assisted Learning, 2025
Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress.…
Descriptors: Formative Evaluation, Academic Achievement, Student Participation, Learning Processes
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Li, Wenjing; Wang, Fuxing; Mayer, Richard E.; Liu, Tao – Journal of Computer Assisted Learning, 2022
Background: With the rapid popularization of e-learning, how to improve online learning has aroused widespread concern. A human-like pedagogical agent (PA) that displays eye gaze and gestures, is often added to online multimedia lessons to increase social connection and improve learning in e-learning environments. However, there has been a debate…
Descriptors: Online Courses, Animation, Educational Technology, Technology Uses in Education
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Filius, Renée M.; de Kleijn, Renske A. M.; Uijl, Sabine G.; Prins, Frans J.; van Rijen, Harold V. M.; Grobbee, Diederick E. – Journal of Computer Assisted Learning, 2019
We investigated the relation between providing and receiving audio peer feedback with a deep approach to learning within online education. Online students were asked to complete peer feedback assignments. Data through a questionnaire with 108 respondents and 14 interviews were used to measure to what extent deep learning was perceived and why.…
Descriptors: Feedback (Response), Peer Influence, Online Courses, Educational Technology
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Shao, Kaiqi; Kutuk, Gulsah; Fryer, Luke K.; Nicholson, Laura J.; Guo, Jidong – Journal of Computer Assisted Learning, 2023
Background: Considerable evidence suggests that students' achievement emotions are important contributors to their learning and success online. It is, therefore, essential to understand and support students' emotional experiences to enhance online education, especially under the COVID-19 context. However, to date, very few studies have…
Descriptors: Student Attitudes, Undergraduate Students, English (Second Language), Second Language Learning
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Garcia, Consuelo; Badia, Antoni – Journal of Computer Assisted Learning, 2017
This study investigated the frequency of use of information problem-solving (IPS) skills and its relationship with learning outcomes. During the course of the study, 40 teachers carried out a collaborative IPS task in small virtual groups in a 4-week online training course. The status of IPS skills was collected through self-reports handed in over…
Descriptors: Problem Solving, Incidence, Outcomes of Education, Small Group Instruction
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Thomas, Matthew J. W. – Journal of Computer Assisted Learning, 2002
Presents results from a study of undergraduate students' learning outcomes and patterns of interaction within an online discussion forum. Topics include social dynamics of computer-mediated communication versus face-to-face communication; cognitive engagement; critical and reflective thinking; and student interaction. (Author/LRW)
Descriptors: Computer Mediated Communication, Critical Thinking, Discussion (Teaching Technique), Group Dynamics
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Barrett, E.; Lally, V. – Journal of Computer Assisted Learning, 1999
Explores gender differences in the use of computer-mediated communication in a specific learning context by a small group of postgraduate distance learners and their tutors at the University of Sheffield. Uses content analysis of online dialog to investigate learning and socioemotional behavior. (Author/LRW)
Descriptors: Affective Behavior, Computer Assisted Instruction, Computer Mediated Communication, Content Analysis