Improving MOOC quality using learning analytics and tools

Abstract

Assessing the quality of MOOCs is an important issue for learners since learners are paying fees for accessing the content (e.g. graded assignments), certificates of completion and for course credit. One of the unique advantages of online courses is that all the content can be assessed and analyzed even before the courses are released using various learning analytical and natural language processing tools. However, to date there are few studies in the literature published on the analysis of MOOC content. Furthermore, MOOC providers expect the course developers to periodically revise their MOOCs. Various types of analysis that can be done on the course text, video transcripts and assessments such as readability, listenability, videolytics, and text analysis. By analyzing the course content before its release, the content can be adjusted to target various learners. Subsequently, the same techniques can be used to analyze the discussion board posts and post-course survey to identify areas in a course that need to be modified in to order to improve the course quality for subsequent release. In this paper natural language processing and MOOC analytics were applied to several MOOCs to identify areas for revision to enhance their quality.