The Use of Learning Analytics to Improve Online Learning Outcomes: A Systematic Literature Review
ICTs are transforming the bricks and mortar institutional delivery methods with virtual and online learning converging to such an extent that the learner can experience anytime, anywhere learning, irrespective of the mode of education that he or she is engaged into. Such learning transactions occur in environments which are extensively data-driven. Researchers have argued that understanding patterns in such wealth of available data could be of value to address drop-out issues in online learning, improve student engagement and performances as well as the overall learning experiences leading to better student satisfaction. This concept is referred to as Learning Analytics. In this paper, we conduct a systematic literature review to look at how learning analytics have been used to model learner engagement in online courses and how their engagement has influenced their performances. We used Cooper’s taxonomy of literature reviews as research method. The method consists of five main steps: (1) identify the problem statement (2) search literature for data collection (3) evaluate the relevance of data (4) analyse and synthesize data (5) interpret and discuss the findings. In this study, the data collection process resulted in selecting 132 articles. This was followed by a first screening which identified only 77 articles for their pertinence in the field. The search was further refined to 40 articles for their appropriateness and relevance to address the aim of the study. We found that most learner engagement models in analytics studies used platform data and mainly access and activity logs of the system, and other data such as student participation in online discussion forums, and clicks on the platform to view pages, and do other types of tasks. We found that there is a gap in the research studies with respect to student engagement measurement, pertaining to courses designed as a set of activities which are outcomes and competency-based. // Paper ID 14
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