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Disaggregating the Aggregators: MOOCs as Course Supplements

Disaggregating the Aggregators: MOOCS as Course Supplements
After the initial critiques of MOOCs in regards to course completion rates, components of these courses are now being adopted by institutions as supplements to their traditional programming.

The success of San Jose State University’s (SJSU) incorporation of a Massive Open Online Course (MOOC) into their curriculum is indisputable: in side-by-side comparisons of two traditionally-taught sections of an introductory electrical engineering course with an edX-provided MOOC variant, the pass rates went from 55-59 percent to 91 percent.[1] This mirrors results that the Open Learning Initiative (OLI) at Carnegie Mellon University has been achieving for years. However, interestingly, SJSU incorporated MOOCs as a course supplement in a flipped classroom. If you think about that, it is the beginning of disaggregation of MOOCs into technological (big data), content and pedagogical (peer learning) components.

MOOCs will inevitably pass through a period in which their incorporation is resisted, whether because they are being imposed on universities by legislators (see: California Senate Bill 520) or because every new kind of course supplement, from textbooks to computers to online materials, always faces an initially slow uptake. However, if publishers can make fancy PowerPoints to help sell their texts, it does not take a leap of imagination to think that, in the near future, disaggregated MOOC technology and content will be bundled with the best-selling texts. In fact, the large publishers are already transitioning to software services models and MOOCs fit neatly into this transition.

One of my criticisms of MOOCs has been that they supply no search engine for users to find, inside of the individual courses, the specific nugget of instructional value they may be seeking. To that extent, MOOCs are a step behind OpenCourseWare and other open educational resources. However, once the MOOC becomes a resource for professors, it is inevitable that they will want to cherry pick which components of the course to use, regardless of the elite provenance of the material. And why not? As Ronald Legon points out in “MOOCs and the Quality Question,” the design of material for the “bright, self-starter” may land wide of the mark for the mainstream student, not to mention for those with inadequate secondary preparation. [2]

A second area that requires examination is the area of peer assessment. One small study indicated that MOOC courses using peer assessments principally, or in combination with automated assessment, had lower completion rates. [3] But in the flipped classroom, the peer group exists and has the motivation — be it high tuition costs or the love of learning — to march together through the course. In the area of pedagogy, the flipped classroom can provide better opportunities for learning from groups of students working together. There is a large literature of peer groups in science, technology, engineering and mathematics education which show that, with appropriately-trained peer leaders and careful integration, peer learning can be more successful than a lecture class.

The final area for reflection is the use of MOOC or OLI-style big data to provide a continuous feedback loop on the effectiveness of content and pedagogy. While higher education has begun to wholeheartedly embrace big data to identify students at risk of dropping out, it can actually go much further to help students navigate their college years. If they could see their courses before taking them, they would presumably make better decisions on majors. Once enrolled in a course, they could be given just-in-time learning support if we had proper analytics. This is a bit of the idea in embryonic form of the University of California Irvine’s OpenChem project, which seeks to give learners concurrent support in their current courses.

In the long run, we can foresee institutions adopting MOOC content as supplements, not as replacements, and incorporating big data as another IT function, integrated with registration functions. And that wouldn’t be a bad result either.

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[1] Steve Kolowich, “California State U. System Will Expand MOOC Experiment,” The Chronicle of Higher Education, April 10, 2013. Accessed at

[2] Ronald Legon, “MOOCs and the Quality Question,” Inside Higher Ed, April 25, 2013. Accessed at

[3] Katy Jordan, “MOOC completion rates,” February 13, 2013, available from

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