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Wang, Yanqing; Wang, Fuxing; Mayer, Richard E.; Hu, Xiangen; Gong, Shaoying – Journal of Computer Assisted Learning, 2023
Background: How to improve learning with online multimedia lessons has attracted widespread concern. Prior studies have attempted to help students learn by breaking a video lesson into several segments. However, there has been a debate about whether learners can use pause time effectively and whether prompting them to engage in different types of…
Descriptors: Multimedia Instruction, Multimedia Materials, Prompting, Documentation
Yun Tang; Zhengfan Li; Guoyi Wang; Xiangen Hu – Interactive Learning Environments, 2023
To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with…
Descriptors: Electronic Learning, Learning Activities, Self Management, Intelligent Tutoring Systems
Qiuchen Yu; Caixia Liu; Zhongling Pi; Jiumin Yang – Educational Technology Research and Development, 2025
Writing explanations is widely recognized as an effective strategy to promote meaningful learning outcomes. However, most research focused on writing explanations for fictitious peers, with limited investigation into the benefits of writing for actual peers, particularly considering the influence of peer relationship closeness. To address this…
Descriptors: Multimedia Instruction, Peer Relationship, Social Distance, Friendship
Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
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
Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
Xu Du; Lizhao Zhang; Jui-Long Hung; Hao Li; Hengtao Tang; Miao Dai – Journal of Computing in Higher Education, 2024
This study aims to track college students' on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students'…
Descriptors: Teaching Methods, Attention Control, Brain Hemisphere Functions, Diagnostic Tests
Xia, Xiaona – Interactive Learning Environments, 2023
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential…
Descriptors: Behavior Patterns, Learning Analytics, Decision Making, Correlation
Jeff Ford; Rachel Erickson; Ha Le; Kaylee Vick; Jillian Downey – PRIMUS, 2024
In this study, we analyzed student participation and success in a college-level Calculus I course that utilized standards-based grading. By measuring the level to which students participate in this class structure, we were able to use a clustering algorithm that revealed multiple groupings of students that were distinct based on activity…
Descriptors: Calculus, Mathematics Instruction, Mathematics Achievement, Grades (Scholastic)
Wen-Lung Huang; Liang-Yi Li; Jyh-Chong Liang – Educational Technology & Society, 2024
The purposes of this study were to explore students' learning performance, knowledge construction, and behavioral patterns in computer-supported collaborative learning (CSCL) online discussions with/without using Form+Theme+Context (FTC) model guidance scaffolding in visual imagery education. In the online learning activities, the control group…
Descriptors: Asynchronous Communication, Online Courses, Behavior Patterns, Discussion (Teaching Technique)
Lyn Lim; Maria Bannert; Joep van der Graaf; Yizhou Fan; Mladen Rakovic; Shaveen Singh; Inge Molenaar; Dragan Gaševic – British Journal of Educational Technology, 2024
Scaffolds that support self-regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to…
Descriptors: Learning Processes, Scaffolding (Teaching Technique), Comparative Analysis, Undergraduate Students
Li, Yuhao; Chang, Mengyi; Zhao, Hanxuan; Jiang, Caihong; Xu, Sihua – Journal of Computer Assisted Learning, 2023
Background: Mobile devices facilitate learning activities in a self-paced way. However, the current understanding of learning participation and its consequence are minimal when learners take advantage of opportunities provided by mobile technologies worldwide. Aims: The primary purpose of this study is to examine the effectiveness of environmental…
Descriptors: Anxiety, Computer Software, Computer Assisted Instruction, Learning Processes

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