Removing Learning Barriers in Self-paced Online STEM Education




learning barrier, adaptive practicing, knowledge tracing, exercise sequencing, reinforcement learning, self-paced online learning


Self-paced online learning provides great flexibility for learning, yet it brings some inherent learning barriers because of the nature of this educational paradigm. This review paper suggests some corresponding strategies to address these barriers in order to create a more supportive self-paced online learning environment. These strategies include a) increasing students’ self-awareness of learning, b) identifying struggling students, and c) facilitating mastery learning.Focusing on Science, Technology, Engineering, and Mathematics (STEM) disciplines’ delivery of self-paced online learning, this paper reviewed the role of formative assessment for learning. It is proposed that systematically designing and embedding adaptive practicing in STEM courses would be an effective learning design solution to implement these strategies. By examining the goals and context of adaptive practicing requested in this study, the feature requirements are depicted for such an adaptive practicing model. The models and techniques that can be used for adaptive assessment were then reviewed. Based on the review results, this paper argues that a reinforcement learning-based adaptive practicing model would be the best option to meet those feature requirements. Finally, we point out a research gap in this field and suggest a future research direction for ourselves and other researchers.

Author Biographies

Hongxin Yan, University of Eastern Finland

Hongxin Yan is a doctoral student at the School of Computing, University of Eastern Finland, Finland and works as a Learning Designer in the Faculty of Science and Technology, Athabasca University, Canada. His research interests include personalized and adaptive learning in online education.

Fuhua Lin, Athabasca University

Dr. Fuhua Lin is Professor and former Chair for the School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Canada. He is the Coordinator of Intelligent Systems and Machine Learning research cluster of Athabasca University and a senior member of IEEE and ACM. 

Kinshuk, University of North Texas

Dr. Kinshuk is the Dean of the College of Information at the University of North Texas. Prior to that, he held the NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics, funded by the Federal government of Canada, Provincial government of Alberta, and by national and international industries.

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