Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design
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Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design Hongxin Yan 1 & Fuhua Lin 2 & Kinshuk 3 Accepted: 1 October 2020/ # The Author(s) 2020
Abstract Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer’s work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined. Keywords Self-paced online learning . Learning analytics . Student modelling . AIED .
Learning data . Course learning design . Intervention
* Hongxin Yan [email protected] Fuhua Lin [email protected] Kinshuk [email protected] Extended author information available on the last page of the article
International Journal of Artificial Intelligence in Education
Introduction Online education is growing and creating enormous learning opportunities for learners. The vast amounts of learning-related data in online environments are significantly empowering intelligent computing technologies to enhance learning. One such technology is learning analytics (LA). Siemens and Long (2011) defines Learning Analytics as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” For online education, LA brings massive potential in various dimensions — monitoring, prediction, tutoring, feedback, adaptation, personalization, recommendation, and reflection (Wong and Li 2020; Chatti et al. 2012). Since the early stages, LA has been used to predict learning success and to identify at-risk students at the course or program level. Mor
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