A large-scale study of Twitter Use in MOOCs

Researchers have proposed that social media might offer many benefits to Massive Open Online Courses. Yet such claims are supported by little empirical evidence. The existing research exploring the use of social media in MOOCs has been conducted with individual courses and convenience samples, making it difficult to know to what extent research results are generalizable. In this mixed methods research, I used data mining techniques to retrieve a large-scale Twitter data set from 116 MOOCs with course-dedicated hashtags. Using quantitative and qualitative methods, I then examined users’ participation patterns, the types of users posting to those hashtags, the types of tweets that were posted, and the variation in types of posted tweets across users. I found little evidence to support the claims that Twitter as an adjunct to MOOCs is used much/effectively. Results show that learners make up only about 45% of users and contribute only about 35% of tweets. The majority of users contribute minimally, and an active minority of users contributes the preponderance of messages.

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Brand new tennis ball among eight used ones – Image by Horia Varlan CC-BY

These findings do not reveal substantive evidence of learners contributing to multiple hashtags, which may suggest that learners did not find Twitter to be a useful space that provided added value or responded to their needs. Ultimately, these results demonstrate the need for greater intentionality in integrating social media into MOOCs.

I am linking to the pdf pre-print of this article below.

Veletsianos, G. (in press). Toward a Generalizable Understanding of Twitter and Social Media Use Across MOOCs: Who Participates on MOOC Hashtags and In What Ways? Journal of Computing in Higher Education.



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  1. Will the mooc provider and courses continue to be anonymized in the actual publication? I have no way to verify that these results are in any way meaningful.

    • Hi Stephen,
      I was not given permission to identify the provider, and therefore the final paper won’t include identifying information.
      I hope my description of context, methods, and limitations are helpful in judging the quality of the results. If not, I would be happy to hear from you on what additional information I could include to help with that.

  2. Well there’s no way to validate any of the information. We have no idea how many people enrolled, we have no way of knowing the instructional strategy, and we don’t have an example of an actual course tweet (even though, note, that all tweets are publicly available).

    • Table 1 has examples of course tweets.

      Does this study have limitations? Yes. Further information into the context would have been helpful.

      If I had enrolment numbers (i don’t, and that’s acknowledged in the paper), I could have compared the two values. More could also be done with completion numbers, forum participation numbers, number of high-activity users in each course, etc. Unfortunately, I don’t have access to any of that information.

      Like you, I also don’t know the instructional strategy of each course. Again, in the paper, I note that future studies should look at how the design of the course impacts participation/use (p. 20).

      One could address these issues by asking different types of research questions, using additional data sources, and examining relationships, for example, between instructional strategies and variables of interest, such as participation rates, meaningful conversations, etc.

      Do we need to look further and deeper? Yes, of course we do.

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