https://orcid.org/0000-0001-5024-9056
![Sample our Education journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5932/TOC-Subject-Sample-2021-Education.jpg"})
ABSTRACT
Massive open online courses (MOOCs) provide learners with high-quality learning resources, but learners drop out frequently. Learners’ concerns (e.g. the topics in course content or logistics) and cognitive engagement patterns (e.g. tentative or certain) are considered the essential factors affecting learners’ course completions. However, it is still unclear what different learning achievement groups focus on in each cognitive engagement pattern. In this study, we adopted an unsupervised computational model, the temporal cognitive topic model (TCTM), to automatically investigate learners’ cognitive engagement patterns in discussing different topics, as well as the changes under each cognitive engagement pattern over time. A data experiment of 4080 learners enrolled in a Modern Etiquette course revealed that the high-achievement group preferred to discuss on-task topics in an exclusive cognitive engagement pattern; the low-achievement group preferred to discuss off-task topics in a tentative pattern, including certificate acquisition and examination grades; the medium-achievement group showed less variation in different cognitive engagement patterns. Additionally, a moderation analysis showed that there was a significant moderating effect of discussion guidance, especially for instructor-led guidance, between the salient cognitive topics and learning achievements. The analytical results can help instructors to conduct (e.g. feedback and guidance) and timely intervention of cognitive knowledge construction in MOOCs.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Zhi Liu
Zhi Liu is an associate professor in National Engineering Laboratory for Educational Big Data at Central China Normal University. His focuses include learning analytics and educational data mining. He was a guest researcher in the Department of Computer Science at Humboldt University of Berlin from 2017 to 2018.
Rui Mu
Rui Mu is a master’s student in National Engineering Research Center for E-Learning at Central China Normal University. Her focuses include learning behavior analysis.
Zongkai Yang
Zongkai Yang is a professor in National Engineering Laboratory for Educational Big Data and National Engineering Research Center for E-Learning at Central China Normal University. He has been engaged in the research and development of education informatization, digital learning technology, and computer network communication for a long time.
Xian Peng
Xian Peng worked as a post-doctor in the education department at Zhejiang University, China, in 2018. And he is currently working as a research associate in the Department of Artificial Intelligence Education at Central China Normal University (CCNU). He has papers in Computers & Education and the International Journal of Educational Technology in Higher Education. His main research interest is to understand and uncover the online learning/teaching process using the method of learning analytics and educational data mining.
Sannyuya Liu
Sannyuya Liu is a professor in National Engineering Laboratory for Educational Big Data and National Engineering Research Center for E-Learning at Central China Normal University. His focuses include educational technology, educational big data and artificial intelligence.
Jia Chen
Jia Chen is a lecturer in the School of Educational Information Technology at Central China Normal University. His focuses include virtual reality and deep learning Technology.