MOOCs plus big data

In The Coming Big Data Education Revolution, Doug Guthrie argues that “big data”, rather than MOOCs, represent the true revolution in education:

MOOCs are not a transformative innovation that will forever remake academia. That honor belongs to a more disruptive and far-reaching innovation – “big data.” A catchall phrase that refers to the vast numbers of data sets that are collected daily, big data promises to revolutionize online learning and, in doing so, higher education.

I agree that there are exciting new discoveries and innovations still yet to be made through the advent of big data in education, and I also agree that MOOCs’ current reliance on scaling up delivery of existing content isn’t particularly revolutionary. Yet I see the two movements as overlapping and complementary, rather than as competing forces.

While MOOCs may not (yet) have revolutionized instruction, they have revolutionized access for many learners. Part of their appeal for those interested in their growth is their potential for enabling large-scale analysis due to the high enrollments as well as the availability of online data. The opportunity to study such large numbers of students across such disparate contexts is rare in traditional academic settings, and it permits discoveries of learning trajectories and error patterns that might otherwise get missed as noise amidst smaller samples.

Another potential innovation which traditional MOOCs (xMOOCs) have not yet explored is new models of building cohorts and communities from amidst a large pool of learners, a goal at the heart of “connectivist MOOCs” (cMOOCs) that highlights peer-learning pedagogy. Combine xMOOCs and cMOOCs, and you can improve educational access even further by enabling courses to spring up whenever and wherever enough people, interest, and resources converge. Add in the analytical power of big data, and then you have the capacity to truly personalize learning, by providing both the experiences that best support students’ learning and the human interactions that will enrich those experiences.

Distinguishing MOOCs from OER

Stanford mathematics professor Keith Devlin suggests that we should drop MOOCs and focus on MOORs (massively open online resources) or OERs (open educational resources):

no single MOOC should see itself as the primary educational resource for a particular learning topic. Rather, those of us currently engaged in developing and offering MOOCs are, surely, creating resources that will be part of a vast smorgasbord from which people will pick and choose what they want or need at any particular time.

Yet even if current MOOCs follow a mediocre model for structuring learning experiences, they do still attempt to meet a need for learners who seek guidance, structure, and social cohorting for the way they access educational resources. I would be interested in decoupling OERs from MOOCs and similar pathways, in order to broaden the scope of available OERs from which anyone can choose. That opens up possibilities for more innovative approaches to enabling diverse learning paths and cohorting models.

Why personalized learning and assessment?

Much of the recent buzz in educational technology and higher education has focused on issues of access, whether through online classes, open educational resources, or both (e.g., massive open online courses, or MOOCs). Yet access is only the beginning; other questions remain about outcomes (what to assess and how) and process (how to provide instruction that enables effective learning). Some anticipate that innovations in personalized learning and assessment will revolutionize both, while others question their effectiveness given broader constraints. The goal of this blog is to explore both the potential promises and pitfalls of personalized and adaptive learning and assessment, to better understand not just what they can do, but what they should do.