MOOCsperiments: How should we assign credit for their success and failure?

That San Jose State University’s Udacity project is on “pause” due to comparatively low completion rates is understandably big news for a big venture.

We ourselves should take pause to ponder what this means, not just regarding MOOCs in particular, but regarding how to enable effective learning more broadly. The key questions we need to consider are whether the low completion rates come from the massive scale, the online-only modality, the open enrollment, some combination thereof, or extraneous factors in how the courses were implemented. That is, are MOOCs fundamentally problematic? How can we apply these lessons to future educational innovation?

Both SJSU and Udacity have pointed to the difficulties of hasty deployment and starting with at-risk students. In an interview with MIT Review, Thrun credits certificates and student services with helping to boost completion rates in recent pilots, while the inflexible course length can impede some students’ completion. None of these are inherent to the MOOC model, however; face-to-face and hybrid settings experience the same challenges.

As Thrun also points out, online courses offer some access advantages for students who face geographic hurdles in attending traditional institutions. Yet in their present form, they only partly take advantage of the temporal freedom they can potentially provide. While deadlines and time limits may help to forestall indefinite procrastination and to maintain a sense of shared experience, they also interfere with realizing the “anytime, anywhere” vision of education that is so often promoted.

But the second half of “easy come, easy go” online access makes persistence harder. Especially in combination with massive-scale participation that exacerbates student anonymity, no one notices if you’re absent or falling behind. While improved student services may help, there remain undeveloped opportunities for changing the model of student interaction to ramp up the role of the person, requiring more meaningful contributions and individual feedback. In drawing from a larger pool of students who can interact across space and time, massive online education has great untapped potential for pioneering novel models for cohorting and socially-situated learning.

Online learning also can harness the benefits of AI in rapidly aggregating and analyzing student data, where such data are digitally available, and adapting instruction accordingly. This comes at the cost of either providing learning experiences in digital format, or converting the data to digital format. This is a fundamental tension which all computer-delivered education must continually revisit, as technologies and analytical methods change, as access to equipment and network infrastructure changes, and as interaction patterns change.

The challenges of open enrollment, particularly at massive scale, replay the recurring debates about homogeneous tracking and ability-grouping. This is another area ripe for development, since students’ different prior knowledge, backgrounds, preferences, abilities, and goals all influence their learning, yet they benefit from some heterogeneity. Here, the great variability in what can happen exaggerates its importance: compare the consequences of throwing together random collections of people without much support, vs. constraining group formation by certain limits on homogeneity and heterogeneity and instituting productive interaction norms.

As we all continue to explore better methods for facilitating learning, we should be alert to the distinction between integral and incidental factors that hinder progress.

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.