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Emerging Student Patterns in MOOCs: A Graphical View

This article was originally posted at e-Literate

Thanks to feedback from my last post, I have modified the proposed description of patterns for students engaged in MOOCs. I also want to introduce a graphic to visually represent these patterns.

  • studentPatternsInMoocs2I have removed the language comparing passive participants to traditional students based on the idea that they expect others to define academic goals for them and ‘expect to be taught’ (thanks to Colin Milligan for description). While this distinction between passive and active participants is important, I have removed the direct reference to traditional students – the reader can apply their own comparisons.
  • I have removed the usage of the term archetype. As Satia Renee put it so well on Google+, archetype implies “more an internal personality type expressing itself in patterns of behavior” when I am trying to capture the patterns of behavior. Thus I’m sticking with the less loaded term of patterns.
  • I have added language, thanks to Kevin Kelly, that captures the growing case of Drop-Ins as students focused on a particular topic within a MOOC for usage outside of that MOOC.
  • Finally, I have moved Drop-Ins right after Lurkers based on Colin’s comments and to help with the graphical view below.

As a recap, I believe we are seeing the following four patterns of student behavior within MOOCs:

  • Lurkers – These students are the majority of xMOOC participants, where people enroll but just observe or sample a few items at the most. Many of these students do not even get beyond registering for the MOOC or maybe watching part of a video.
  • Drop-Ins – These are students who become partially or fully active participants for a select topic within the course, but do not attempt to complete the entire course. Some of these students are focused participants who use MOOCs informally to find content that help them meet course goals elsewhere.
  • Passive Participants – These are students who view a course as content to consume and expect to be taught. These students typically watch videos, perhaps take quizzes, but tend to not participate in activities or class discussions.
  • Active Participants – These are the students who fully intend to participate in the MOOC, including consuming content, taking quizzes and exams, taking part in activities such as writing assignments and peer grading, and actively participate in discussions via discussion forums, blogs, twitter, Google+, or other forms of social media.

An important point is that some students change between patterns – such as a passive participant deciding to fully jump in and become an active participant, or even an active participant becoming frustrated and becoming a lurker. From what I’ve seen, this type of change occurs once per course at the most for any individual student.

There are still some good comments coming into the original post, so I will probably refine the definitions over time.

Additional Notes on Behavior

I have already described the data captured by Katy Jordan on MOOC completion rates. Note that this data compares the ratio of students completing a course to total number of students registered.

There are several courses *, typically on Coursera, where we now have a deeper description of the student behavior based on information shared by the professors. I suspect this view will be different between xMOOCs and cMOOCs, and even between different MOOC providers. For now, treat these observations as primarily based on Coursera-style MOOCs.

The majority of students (60 – 80%) reported as registered in a course are lurkers who tend to leave the course completely by the second week and may not even engage with the material in any significant way.

  • IHTS: 46k registered, by week 2 there were 11.6k who “completed week 1″
  • EDC MOOC: 46k registered, by week 2 there were 7.4k logging in
  • Bioelectricity: 12k registered, 8k watched any videos, 3k watched week 2 intro
  • Microeconomics: 37k registered, 25% watching video during week 2

The courses seem to stabilize by week 2 or 3 in numbers of students still in course. While this observation is mostly based on anecdotes from blog posts, there are two charts capturing the data for Bioelectricity and EDC:

Activity per week


DC stats 3


Graphical View

How would these student patterns appear over time, at least for those courses similar to the Coursera MOOCs with intermediate data? I believe the following graphic captures the basic shape and topology of student patterns. Note the graphic is a generalization, and ideally we would have this type of diagram for each course.



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