L@S2020: Content Type Distribution and Readability of MOOCs

I am standing on the shoulder of giants here! This work builds upon the works done by OEDO TAs even before I joined OEDO. Below is the video prepared in advance and added as supplementary material to this work.

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Details

Title: Content Type Distribution and Readability of MOOCs

Authors: May Kristine Jonson Carlon, Nopphon Keerativoranan, Jeffrey S. Cross

Venue: Seventh ACM Conference on Learning @ Scale

Date: August 12 to 14, 2020

DOI: 10.1145/3386527.3405950 (open access)

Abstract

Massive open online courses (MOOCs) provide a great opportunity to use multiple means of information representation through a mixture of various media such as text, graphics, and video, among others. However, most research on MOOCs focused on learning analytics and not much attention is given to content analysis. We gathered all text corpora and video transcripts of selected MOOCs using a web crawler and looked at word counts, clustered by distribution, and measured the readability of the crawled data. Analyzing content distribution allows for a comparison of MOOCs regardless of topics, thus giving us an idea of what most course developers might think is ideal in terms of content distribution. This comparison along with readability analysis can be useful for course pre-run quality assessment and gauging content sufficiency.