LWMOOCS 2019: Improving MOOC quality using learning analytics tools

This is a research paper done by the Tokyo Institute of Technology's Online Education Development Office (OEDO). I personally wrote portions related to sentiment analysis. Professor Cross visited Milwaukee, WI, USA to present our work, which received a Best Paper Award (given to two papers).

Professor Cross receiving the Best Paper Award

For a copy of relevant materials (e.g., presentation, paper) or any questions you may have, please feel free to reach out to me through the Contact Me gadget on this blog's side bar.

Details

Title: Improving MOOC quality using learning analytics tools

Authors: Jeffrey S. Cross, Nopphon Keerativoranan, May Kristine Jonson Carlon, Yong Hong Tan, Zarina Rakhimberdina, Hideki Mori

Venue: IEEE Learning with MOOCs 2019

Date: October 23 to 25, 2019

DOI: 10.1109/LWMOOCS47620.2019.8939617 ($$)

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 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 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 surveys to identify areas in a course that need to be modified in 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.