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Title
Japanese: 
English:Content Type Distribution and Readability of MOOCs 
Author
Japanese: CARLON May Kristine Jonson, Keerativoranan Nopphon, クロス ジェフリー スコット.  
English: May Kristine Jonson Carlon, Nopphon Keerativoranan, Jeffrey Scott Cross.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date Aug. 2020 
Publisher
Japanese: 
English:Association for Computing Machinery 
Conference name
Japanese: 
English:Seventh ACM Conference on Learning @ Scale 
Conference site
Japanese: 
English: 
Official URL https://dl.acm.org/doi/abs/10.1145/3386527.3405950
 
DOI https://doi.org/10.1145/3386527.3405950
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 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.

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