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Showing 1 to 15 of 18 results Save | Export
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Zheng, Lanqin; Long, Miaolang; Niu, Jiayu; Zhong, Lu – International Journal of Computer-Supported Collaborative Learning, 2023
Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Learner Engagement, Automation
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Zheng, Lanqin; Zhong, Lu; Fan, Yunchao – Education and Information Technologies, 2023
Online collaborative learning (OCL) has been a mainstream pedagogy in the field of higher education. However, learners often produce off-topic information and engage less during online collaborative learning compared to other approaches. In addition, learners often cannot converge in knowledge, and they often do not know how to coregulate with…
Descriptors: Electronic Learning, Cooperative Learning, Undergraduate Students, Learning Analytics
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Zheng, Lanqin; Niu, Jiayu; Long, Miaolang; Fan, Yunchao – British Journal of Educational Technology, 2023
Computer-supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Graphs, College Students
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Zheng, Lanqin; Niu, Jiayu; Zhong, Lu – British Journal of Educational Technology, 2022
Learning analytics (LA) has been widely adopted in research on education. However, most studies in the area have conducted LA after computer-supported collaborative learning (CSCL) activities rather than during CSCL. To address this problem, this study proposed a LA-based real-time feedback approach based on a deep neural network model to improve…
Descriptors: Learning Analytics, Feedback (Response), Outcomes of Education, Cooperative Learning
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Zheng, Lanqin; Kinshuk; Fan, Yunchao; Long, Miaolang – Education and Information Technologies, 2023
Online collaborative learning has been an effective pedagogy in the field of education. However, productive collaborative learning cannot occur spontaneously. Learners often have difficulties in collaborative knowledge building, group performance, coregulated behaviors, learning engagement, and social interaction. To promote productive…
Descriptors: Learning Analytics, Performance, Electronic Learning, Cooperative Learning
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Zheng, Lanqin; Zhen, Yuanyi; Niu, Jiayu; Zhong, Lu – Journal of Computing in Higher Education, 2022
Programming skills have gained increasing attention in recent years because digital technologies have become an indispensable part of life. However, little is known about the roles of fade-in and fade-out scaffolding in online collaborative programming settings. To close this research gap, the present study aims to examine the roles of fade-in and…
Descriptors: Programming, Scaffolding (Teaching Technique), Skill Development, Undergraduate Students
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Zheng, Lanqin; Long, Miaolang; Chen, Bodong; Fan, Yunchao – International Journal of Educational Technology in Higher Education, 2023
Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated…
Descriptors: Learning Analytics, Computer Assisted Testing, Cooperative Learning, Graphs
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Zheng, Lanqin; Niu, Jiayu; Zhong, Lu; Gyasi, Juliana Fosua – Innovations in Education and Teaching International, 2023
Computer-supported collaborative learning (CSCL) has been widely adopted in the field of education. However, most studies focus on collaborative learning outcomes rather than collaborative learning processes. It is still unclear why some groups fail in CSCL. Therefore, this study extracted four process variables, namely, knowledge-building,…
Descriptors: Metacognition, High Achievement, Low Achievement, Electronic Learning
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Zheng, Lanqin; Cui, Panpan; Zhang, Xuan – International Journal of Computer-Supported Collaborative Learning, 2020
This study reports on a novel design methodology, namely, design-centered research (DCR), developed to analyze and evaluate the alignment between an online collaborative learning design and its enactment. The approach is illustrated in a study involving 40 groups in total. Twenty different online collaborative learning activities were designed and…
Descriptors: Cooperative Learning, Instructional Design, Alignment (Education), Computer Assisted Instruction
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Zheng, Lanqin; Zhong, Lu; Niu, Jiayu – Assessment & Evaluation in Higher Education, 2022
Learning analytics has been widely used in the field of education. Most studies have adopted a learning analytics dashboard to present data on learning processes or learning outcomes. However, only presenting learning analytics results was not sufficient and lacked personalised feedback. In response to these gaps, this study proposed a learning…
Descriptors: Electronic Learning, Cooperative Learning, Undergraduate Students, Feedback (Response)
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Fosua Gyasi, Juliana; Zheng, Lanqin – SAGE Open, 2023
Cross-cultural collaborative learning has been paid more and more attention in recent years. To promote productive cross-cultural collaborative learning, idea generation and improvement, and socially shared regulation is crucial. The study aimed to identify the differences in idea generation and improvement as well as socially shared regulation…
Descriptors: Graduate Students, Foreign Countries, Foreign Students, Electronic Learning
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Zheng, Lanqin – Lecture Notes in Educational Technology, 2021
This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design…
Descriptors: Data Use, Instructional Design, Computer Assisted Instruction, Cooperative Learning
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Zheng, Lanqin; Chen, Nian-Shing; Cui, Panpan; Zhang, Xuan – International Review of Research in Open and Distributed Learning, 2019
With the advancement of information and communication technologies, technology-supported peer assessment has been increasingly adopted in education recently. This study systematically reviewed 134 technology-supported peer assessment studies published between 2006 and 2017 using a developed analysis framework based on activity theory. The results…
Descriptors: Peer Evaluation, Educational Technology, Technology Uses in Education, Evaluation Methods
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Zheng, Lanqin; Zhong, Lu; Niu, Jiayu; Long, Miaolang; Zhao, Jiayi – Educational Technology & Society, 2021
In recent years, the rapid development of artificial intelligence has increased the power of personalized learning. This study aimed to provide personalized intervention for each group participating in computer-supported collaborative learning. The personalized intervention adopted a deep neural network model, Bidirectional Encoder Representations…
Descriptors: Instructional Effectiveness, Individualized Instruction, Computer Assisted Instruction, Cooperative Learning
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Zheng, Lanqin; Li, Xin; Huang, Ronghuai – Educational Technology & Society, 2017
Students' abilities to socially shared regulation of their learning are crucial to productive and successful collaborative learning. However, how group members sustain and regulate collaborative processes is a neglected area in the field of collaborative learning. Furthermore, how group members engage in socially shared regulation still remains to…
Descriptors: Cooperative Learning, Undergraduate Students, Experimental Groups, Control Groups
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