The MOOC as Distributed Intelligence: Dimensions of a Framework & Evaluation of MOOCs

Abstract

Massively Open Online Courses (MOOCs) have been at the center of media attention and hyperbole since 2012. As MOOCs proliferate and continue to influence higher education, it is increasingly pressing to define frameworks for their design and evaluation. This paper proposes such a framework, grounded in CSCL and learning sciences research along with a discussion of the unique aspects of MOOCs as learning environments. Through the lens of distributed intelligence the framework defines distinct, but interconnected, dimensions of MOOCs that must work synergistically to maximize individual as well as collective learning. Introduction and Motivation In all the hyperbole surrounding the rollout of Massively Online Open Courses (MOOCs) over the past year and a half, much has been said and written about the campus tsunami (Brooks, 2012) that is purportedly poised to change the face of higher education. Interestingly, while much of the positive feedback has focused on the noble sentiments behind making world-class courses (mostly from elite universities) freely available to anyone, anywhere in the world, a fair amount of the negative press aimed specifically at instructionist MOOCs or xMOOCs (as characterized by Daniel, 2012) has revolved around the quality of the courses themselves. Though this criticism covers the gamut of instructional design issues, (mostly misplaced views of) dropout and completion rates have garnered the most attention. We believe that often both the praise and criticism of MOOCs is founded on historical assumptions about learning environments and outcomes that do not necessarily apply (at least without some reconsideration and reframing) to this new phenomenon. MOOCs today are a moving target—their form and function is shifting weekly, as course designers and platform providers around the world dream up new approaches to open online learning. To remain grounded in this shifting landscape, we need a flexible and generalizable framework for understanding the effects of MOOC design decisions on learning. As a start, we reframe the question What makes a good MOOC? to How can we make a MOOC work for as many of its diverse participants as possible? MOOCs attract a global set of learners with an extensive range of goals and prior knowledge. These individuals vary in the approaches they take to learning, their responses to the social and pedagogical context for learning, and their intrapersonal strategies for dealing with challenges. Framing design and evaluation in this way emphasizes the potential for optimization for different participants or groups of participants—and the possibility of defining different learning outcomes for these different groups of learners. Learning outcomes should also be defined expansively, based on the goals that course designers have to influence cognitive and affective competencies of any subset of learners, or learning on the level of the collective. Furthermore, it helps to view a MOOC as a designed object (Simon, 1969) whose creation should ideally be influenced not only by faculty and instructional designers, but also by technologists, data scientists and learning researchers. These stakeholders influence different elements of the MOOC that interrelate to create learning opportunities for participants. A framework for the design and evaluation of MOOCs must reflect the complex nature of these interrelationships. It must also encapsulate principles from the learning sciences to guide the creation of a robust set of criteria for the design and evaluation of MOOC learning experiences. These criteria will not only help meaningfully frame the discourse on MOOC quality, but also serve prospective learners, course designers and faculty, researchers, as well as the technologists who are charged with developing and evolving the platforms on which MOOCs are deployed to meet needs and enable innovative experimentation.