MOOC performance prediction and analysis via Bayesian network and Maslow’s hierarchical needs theory

ABSTRACTNowadays, Massive Open Online Courses (MOOC) has been gradually accepted by the public as a new type of education and teaching method. However, due to the lack of timely intervention and guidance from educators, learners' performance is not as effective as it could be. To address this problem, predicting MOOC learners' performance and providing them with timely interventions have become an indispensable part for the MOOC learning. However, current MOOC performance prediction methods cannot provide us with interpretable prediction results and cannot further help us to provide learners with targeted intervention strategies. To this end, we adopt the framework of Bayesian Network (BN) and then constructed an MOOC Performance Prediction BN (MPBN), which provides us with a graphical explanation of how learners' demographical and learning behavior characteristics affect their performance. Besides, since the productive MOOC learners tend to be driven by their inner goals, we further use Maslow's hierarchical needs theory to construct several indicators, by which to analyze the prediction of MPBN and then propose the appropriate intervention strategies.KEYWORDS: MOOCperformance predictionBayesian networkMaslow's hierarchical needs theoryintervention strategies Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets generated during the current study are available in the online repository the website: https://analyse.kmi.open.ac.uk/open_dataset.Notes1 https://analyse.kmi.open.ac.uk/open_dataset.Additional informationFundingThis work was supported by National Natural Science Foundation of China [grant number 61862068]; Youth Project of Applied Basic Research Program of Yunnan Province [grant number 202201AU070050]; Key Project of Applied Basic Research Program of Yunnan Province [grant number 202201AS070021].Notes on contributorsLuyu ZhuLuyu Zhu received the B.S. degree in educational technology in Qufu Normal University. She is currently a Master degree candidate in the School of Information at Yunnan Normal University. Her research interests include massive data analysis and students' achievement prediction and analysis.Jia HaoJia Hao received the MS.D. and Ph.D. in computer science from Wuhan University of Technology and Yunnan University in 2015 and 2020 respectively. She is currently a lecturer and a postdoctoral research fellow in the Minister of Education at Yunnan Normal University, Kunming, China. Her research interests include massive data analysis, uncertainty in artificial intelligence, educational technology.Jianhou GanJianhou Gan received the Ph.D. in computer science from Kunming University of Technology in 2016. He is currently a professor and Ph.D. supervisor at Yunnan Normal University, Kunming, China. His research interests include massive data analysis, database, educational informatization and intelligent education. He has published more than 80 papers in the journals as Applied Soft Computing, Neurocomputing and conferences as DASFAA, CIKM, etc.