HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013
By Andrew D. Ho, Justin Reich, Sergiy O. Nesterko, Daniel T. Seaton, Tommy Mullaney, Jim Waldo, Isaac L. Chuang. 2014
359 citations recorded by the source corpus.
Subfields: Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/51c8c0278ce04c24641299825086e0183d065e11
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In Computer Science Applications
Citations in the corpus
- Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020, background): “Similarly, Ho et al [30] examined the completion rates of the 17 HarvardX and MITx MOOCs offered between Fall 2012 and Summer 2013, identifying that only 5% of the students received the course certificate.”
- Understanding the MOOCs continuance: The role of openness and reputation (2015, background): “Completion rate may not be the best measure to evaluate learning in MOOCs (Ho et al. 2014; Jordan, 2014), but these low rates do raise questions regarding their effectiveness (Chafkin, 2013; Marcus, 2013).”
- HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014 (2015, methodology): “In particular, we extend the argument from Ho et al. (2014) that the characterization of MOOC participants as a group of college-educated men in their late 20s misrepresents substantial variation, especially across different kinds of courses.”
- Democratizing Higher Education: Exploring MOOC Use among Those Who Cannot Afford a Formal Education. (2014, neutral): “According to an analysis of 17 online courses offered on the edX platform, Ho et al. (2014) found that of those reporting, the most typical edX MOOC learners were males with bachelor’s degrees who are 26 and older (31% of learners).”
- Massive Open Online Course Completion Rates Revisited: Assessment, Length and Attrition (2015, background): “The definition of completion rate as a percentage of enrolled students may be over-simplistic and subject to wide variations in enrolments (Ho et al., 2014).”
- UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies (2017, methodology): “Data, Algorithms, Software, and Hardware: We have a used a total of 11 publically available datasets including Twitter data, newsmedia data, seven activity recognition datasets, telecommunication networks and applications traffic data, and data from edX [7] (a collaborative effort of Harvard andMIT)”