Peripheral Vision: Engaging Multimodal Social Media Datasets to Differentiate MOOC Platforms by Course Offerings and User Bases

Peripheral Vision: Engaging Multimodal Social Media Datasets to Differentiate MOOC Platforms by Course Offerings and User Bases

Copyright: © 2019 |Pages: 43
DOI: 10.4018/978-1-5225-7528-3.ch008


In the dozen years since massive open online courses (MOOCs) have been a part of open-source online learning, the related platforms and technologies have settled out to some degree. This chapter indirectly explores 10 of the most well-known MOOC platforms based on social data from the following sources: large-scale web search data (via Google Correlate), academic research indexing (Google Scholar), social imagery and related image tagging (Google Image Search), crowd-sourced articles from a crowd-sourced encyclopedia (Wikipedia), microblogging data (Twitter), and posts and comments from social networking data (Facebook). This analysis is multimodal, to include text and imagery, and the analyses are enabled by various forms of “distant reading,” including topic modeling, sentiment analysis, and computational text analysis, and manual coding of social imagery. This chapter aims to define MOOC platforms indirectly by their course contents and the user bases (and their social media-based discourses) that have grown up around each.
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Taking a massive open online course (or “MOOC”) is a fairly simple endeavor. One registers with a validated email, selects from a range of courses, and chooses how much to participate. For a majority of learners, the cost is free because the learners are not taking a course for credit or digital badging or some other formal acknowledgment. In the MOOCs that this author has experienced, the co-learners tend to be friendly and mutually supportive. While the MOOC platforms include some friendly invitations to do homework assignments, learners who lurk are supported. The author has experienced several MOOC platforms over the years. The experiences have been edifying and positive, on the whole. For research, though, there are strengths and limits to sousveillance (or monitoring from within).

The first massive open online course (“In/Formation Year”) was offered in 2006 (Davidson, Sept. 27, 2013), even though it was not known by that name at that time and did not apparently use any unique platform. The term “massive open online course” or “MOOC” was coined in 2008 by Dave Cormier (of the University of Prince Edward Island) and Bryan Alexander (of the National Institute for Technology in Liberal Education), in reference to a course by George Siemens and Stephen Downes of the University of Manitoba (Connectivism and Connective Knowledge), which attracted approximately 2,300 learners (McAuley, Stewart, Siemens, & Cormier, 2010, as cited in Yang, 2014, p. 325).

MOOCs became a “thing” in 2012 (Pappano, 2012; Massive open online course, Jan. 12, 2018). An authoring team writes of the importance of Peter Norvig and Sebastian Thrun’s “Introduction to Artificial Intelligence” class, which brought out 160,000 enrollees, and sparked interest by investors to fund several MOOC platforms:

Following this massive success, several private initiatives started to establish online platforms to organize these courses. Sebastian Thrun went on to create Udacity, a website that could provide other courses than his own. Andrew Ng and Daphne Koller, two other Stanford professors, founded Coursera, while MIT and Harvard University jointly created edX. The success was almost immediate, and MOOCs quickly became a buzzword in the sector of online distance education, which was not used to be in the spotlights. For example, as of early 2015, Coursera partnered with 119 institutions from all over the world, the vast majority of which being traditional higher education institutions, to provide more than 1000 courses, which have attracted more than 13 million single users. In less than 3 years, Coursera also succeeded in attracting more than $85 million in venture capital investment. (Belleflamme & Jacqmin, Mar. 2016, pp. 148-149)

MOOCs are hosted mostly on private platforms, without or with minimal funding (Belleflamme & Jacqmin, Mar. 2016, pp. 149 - 150). This fact is heightened given the high costs to create a successful MOOC and the high risks to reputation (Dennen & Chauhan, 2013). A range of methods have been explored to make MOOCs financially viable and sustainable, and the most common model seems to be the so-called “freemium model” (a portmanteau word created from “free” and “premium,” with a majority of learners taking courses for free and some “premium” learners taking courses under a paid tuition model for credit or badging or other acknowledgment (Belleflamme & Jacqmin, Mar. 2016, p. 159).

Key Terms in this Chapter

Integration: The combining of software systems and mixed capabilities.

Affordance: A capability, an enablement.

Folk Tagging: Amateur labeling of digital contents like imagery.

Tagging: The applying of descriptive labels to digital contents.

Massive Open Online Course (MOOC): Online courses that enable large-scale enrollments of learners (tens of thousands) from around the world for tuition costs for some learners and free for others.

Social media: Web-based platforms that enable people to create persistent profiles, interact and communicate with others, and share informational contents.

Web Search Data: Data about topical, locational, time, and other related data about people’s uses of web search engines.

Related Tags Network: A network graph of tag labels on digital artifacts showing relatedness and co-occurrence.

Sousveillance: Recording of an event or activity by a participant usually through mobile technologies.

User Base: The typical population of people who have used and continue to use a particular software or platform.

Social Imagery: User-generated digital imagery shared on social media platforms.

Functionality: A range of capabilities for a tool.

Feature: An aspect, a dimension.

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