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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
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
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
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
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
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)
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