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.
Highlights
Online education is growing and creating enormous learning opportunities for learners
This deficiency leads to the current issue of misalignment between the information generated by learning analytics and the needs, problems, and concerns that educators have
Based on the learning analytics (LA) applications, the Learning Design-Analytic (LDA) model and the data sources determined for self-paced online learning (SPOL), the following course design strategies are recommended for including LA in the loop of learning design
Summary
Online education is growing and creating enormous learning opportunities for learners. LA has started to include more sophisticated analysis of learning processes, aiming for a more in-depth understanding of students’ learning experiences at the topic or concept level (Mangaroska and Giannakos 2018) Such deeper level analysis can provide timely, actionable, and personalized insights to students and instructors, scale up personalized and adaptive learning, and examine the strengths and weaknesses of online courses (Gašević et al 2015; Jovanović et al 2017; Mangaroska and Giannakos 2018). Many online courses experience high academic failure rates, usually higher than traditional face-to-face education (Park and Choi 2009; Stone 2017) It is critical for educators and researchers to find solutions that can mitigate these learning barriers. The rest of this paper is organized as follows: second section discusses what LA applications can help to overcome these learning barriers; third section explains the limitation of current learning design practices for LA implementation; fourth section describes a new model and a set design strategies that aim to include the LA in the course design loop; fifth section uses a case study to illustrate how to follow the design strategies to revise a SPOL course and implement LA applications in it
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