e-Literate

Present is Prologue

Tag: graphical view

  • The Battle for Open and MOOC Completion Rates

    Yesterday I wrote a post on the 20 Million Minds blog about Martin Weller’s new book The Battle for Open: How openness won and why it doesn’t feel like victory. Exploring different aspects of open in higher education – open access, MOOCs, open education resources and open scholarship – Weller shows how far the concept of openness has come, to the point where “openness is now such a part of everyday life that it seems unworthy of comment”. If you’re interested in OER, open courses, open journals, or open research in higher education – get the book (it’s free and available in a variety of formats).

    Building on the 20MM post about the ability to reuse or repurpose the book itself, I would like to expand on a story from early 2013 where I happen to play a role. I’ll mix in Weller’s description (MW) from the book with Katy Jordan’s data (KJ) and my own description (PH) from this blog post.

    (MW) I will end with one small example, which pulls together many of the strands of openness. Katy Jordan is a PhD student at the OU focusing on academic networks on sites such as Academia. edu. She has studied a number of MOOCs on her own initiative to supplement the formal research training offered at the University. One of these was an infographics MOOC offered by the University of Texas. For her final visualisation project on this open course she decided to plot MOOC completion rates on an interactive graph, and blogged her results (Jordan 2013).

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  • Combining MOOC Student Patterns Graphic with Stanford Analysis

    In part 1, part 2, and part 3 of this series of posts on MOOC student patterns, I shared a description of five student patterns emerging from open-enrollment MOOCs (excluding those with an associated student fee) based on anecdotal data.  In part 4 I compared the overall course completion pattern against an MIT study of the first edX MOOC, leading to some validation of the general shape of when participants drop out.

    Combined MOOC patterns

    The model and typology has gained traction recently, being referenced in both the Government of Ontario’s  environmental scan and literature review [pp 32-33, 40] as well as the British government’s The Maturing of the MOOC literature review [pp 26-28]. So that means it’s time to update and keep people on their toes.

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  • Some validation of MOOC student patterns graphic

    File this under “you read it first on e-Literate”.

    In previous posts from spring 2013 I provided a graphical view on MOOC student patterns based on observed retention over time as well as differing student types. This graphic was based on anecdotal observations of multiple MOOCs, mostly through Coursera.

    studentPatternsInMoocs3-2

     

    Based on a recent study of the edX Circuits and Electronics MOOC, there is this interesting chart of student patterns based on actual data analysis of the 155k students from the spring 2012 offering of this course. The full report is worth reading, by the way, with some real student pattern insights.

    edX Retention

     

    I took this chart and overlaid it on the MOOC student patterns graphic, scaling for 0% / 100% of enrollment vertically and start / stop of course horizontally.

    Combined MOOC patterns

     

    It’s good to see this validation of the overall retention pattern based on real data analysis to augment the original graphic’s model.

  • Emerging Student Patterns in MOOCs: A (Revised) Graphical View

    In part 1 of this series of posts on MOOC student patterns, I shared an initial description of four student patterns emerging from Coursera-style MOOCs based on new data from professors. In part 2, I revised the description based on some feedback and added a graphical view. The excellent feedback has continued, primarily through comments to both posts mentioned above as well as a separate Google+ discussion. This process has helped identify a fifth pattern, clarify the pattern description, and improve the associated graphic. In particular, I want to thank Debbie Morrison, Colin Milligan, John Whitmer, Charles Severance and Kevin Kelly – as well as other commenters for the great discussion.

    The primary changes involve clarifying the previously-described Lurker category. I have separated out a new No-Show category and renamed the Lurkers as Observers. There are also tighter descriptions of each pattern that help define potential data collection that would identify these groupings. Here are the new descriptions and updated graphic.

    No-Shows – These students appear to be the largest group of those registering for an Coursera-style MOOC, where people register but never login to the course while it is active.

    Observers – These students login and may read content or browse discussions, but do not take any form of assessment beyond pop-up quizzes embedded in videos.

    Drop-Ins – These are students who perform some activity (watch videos, browse or participate in discussion forum) 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. They may watch videos, take quizzes, read discuss forums, but generally do not engage with the assignments.

    Active Participants – These are the students who fully intend to participate in the MOOC and take part in discussion forums, the majority of assignments and all quizzes & assessments.

    studentPatternsInMoocs3-2

     

  • Emerging Student Patterns in MOOCs: A Graphical View

    Update (3/10): Patterns and descriptions have been updated based on feedback in a new post. Added links to Astronomy, AI Planning courses.

    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.

    studentPatternsInMoocs2

    • I 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.

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