MOOC Framework


The attention that Massive Open Online Courses (MOOCs) have garnered is unprecedented in higher education. Over the past two years in particular major newspapers, futurists, consultants, academics, startups, and technology journalists have driven a hype and angst-filled narrative around the changing nature of higher education. MOOCs have been held up as a model of learning at scale and new forms of teaching that are expected to transform the post secondary education system in the United States and globally.

Many questions remain and healthy skepticism is warranted regarding proclamations from pundits who move from one sector to another (first the music industry, then journalism, now healthcare and education) proclaiming “disruption” and “new economies”. The conversation regarding systemic transformation of higher education has been prominent for several decades. Whether or not MOOCs are capable of finally delivering long promised systemic change remains to be seen. Somewhere between “MOOCs will change everything” and “we’ve seen this all before” lies the reality that MOOCs are making an impact on the lives of learners and causing an evaluation of classrooms and pedagogies in many universities around the world.

The Context

The potential impact of MOOCs on traditional education is unclear. In spite of this uncertainty, many universities are investing substantial resources designing and delivering open online courses. Key decision-makers, including administrators, faculty, course developers and IT staff, do not have a framework for judging whether MOOCs are effective and whether or not to adopt them. When deciding to adopt MOOCs, numerous challenges remain regarding how to align models, design approaches, platforms, faculty role, and assessment with institutional and student needs and goals. Students similarly lack clear information about whether a given MOOC would be worth the investment of their time in order to address their learning needs and educational goals.

Decision-makers need to better understand what types of MOOCs exist now, and what new models are emerging. A top-tier research university will adopt different technological and pedagogical approaches to MOOCs than a community college. Questions remain for students regarding the value of taking an open online course, particularly in furthering personal learning goals. Varying student profiles also impact the design and development of a course. For example, a student taking a MOOC for personal interest will have different criteria for success than a student taking it for credit or as part of her formal education. State, provincial, and institutional leaders have distinct questions around assessment of MOOC initiatives, specifically how MOOCs might further strategic goals or solve specific problems. Course designers and faculty face concerns around quality of content and teaching for different course types, use of various media, mobile technologies, game-based MOOCs, micro-credentialing, learning analytics, and personal and adaptive learning.

The Need

The field needs a framework for describing and characterizing different types of online learning experiences in a way supports decision-making about which MOOCs and MOOC platforms are best suited for different types of learning, different learners, different contexts, and needs of various stakeholders.

Quality in the context of MOOCs is a complex concept. In essence, whether or not a MOOC is “good” depends on alignment between the purpose of the learning experience, the learning objectives, the instructional design, the goals and expectations of students and faculty, the way technology is used to deliver the content and facilitate learning interactions, and the overall programmatic or institutional context.

Connecting with existing research

Much of the discussion around MOOCs and their effectiveness (or lack thereof) has not been grounded in pre-existing body of research on online learning. Although not all of this prior research is relevant to these new models, many components of MOOCs are not new and replicate online pedagogies and instructional practices that have been extensively researched over the past several decades. In particular, the following areas may provide value to MOOCs:

  1. Distance education has provided important contributions around the design and operation of learning systems including enrolling learners, providing support technology support systems, tutoring, centralized and decentralized learning design models, faculty supervision, as well as alternative assessment methods such as eportfolios.
  2. Online learning research has provided key contributions regarding the role of technology in supporting the teaching and learning process. Researchers in online learning have extensively evaluated different learning formats (synchronous/asynchronous), the role of media, social networked learning, frameworks for interaction (equivalency theorem, communities of inquiry), experiences of learners in online vs. blended vs. classroom learning, as well as the cost structure of online learning programs. Much of the quality discussion that is now prominent in education originated in online learning.
  3. Grey literature, such as blogs and social media, has proven to be an important space for generating innovative and creative approaches to teaching and learning. Many of the concepts now gaining prominence in higher education, such as social networked learning, distributed pedagogies, and even MOOCs were prominent in grey literature well before broader acceptance in the traditional university sector.
  4. Learning sciences has provided important research contributions regarding cognition, human-computer interaction, constructivism, self-regulated learning, as well as cognitive and intelligent tutors.
  5. Learning analytics and educational data mining are more recent contributors to learning research. Researchers in these domains rely on data trails generated as learners use technology systems (learning management systems, social media) as well as learner profile data (student information systems) to identify granular and systems level relationships. In learning analytics, predictive models, personalized and adaptive learning, social network analysis, and discourse analysis are common. Educational data mining focuses on granular dimensions of learning and evaluates how learners interact with software and relationships generated through this interaction1.

The MOOCJam, to be held on November 20, is part of the MOOC Research Initiative (led by Athabasca University and funded by the Bill & Melinda Gates Foundation). The MOOCJam will introduce a conceptual framework for evaluating MOOCs and will invite dialogue and debate regarding the suitability of this framework. Following the online discussion, a literature-based narrative summary of the evolution and current state of MOOCs will be provided that addresses the descriptive framework. The whitepaper that results will be used as a launching point for conversations around assessing ‘quality’ in a meaningful way in the context of MOOCs. The revised framework will be introduced at the MOOC Research convening in December ( During this conference, it will be developed and refined through the combined input of the grantees and influenced by the work presented at this conference. The final framework and whitepaper will be published in early 2014.

MOOC Framework

The MOOC Framework consists of nine distinct components rooted in an underlying foundation of technology and systems support and evaluation. Each component presented below should be assessed as existing on a continuum.

  1. Design: The design decisions made by faculty and learning designers include structured or adaptive learning, sequential or socially mediated learning, types of media used, and how course content is presented through different media and instructional formats. Questions regarding quality of content, intended outcomes, goals, costs, audiences, as well as design processes are considered at this level.
  2. Learner profile: MOOC learners reflect a more diverse profile and interests than learners in traditional higher education. As such, decisions around pedagogy and design need to provide a larger range of options in the learning process. The MOOC support system (tutors, TAs, FAQs) should reflect the diverse learner profile and varying levels of familiarity with learning online as well as in distributed social systems. Other profile factors include technology access, experience in higher education, activity patterns based on motivations for taking a MOOC, etc.
  3. Pedagogy: At the core, pedagogical decisions exist on a continuum with two end points: instructivist (teacher led) and participatory (learner driven). Pedagogical decisions also influence the range of individual, networked, or community learning.
  4. Media: The use of media is evolving in MOOCs. The prominent offerings to date rely heavily on video and text. As MOOCs develop, greater use of game elements and user-generated content can be expected. Greater use of media, however, increases development and delivery costs.
  5. Time: Time decisions in MOOC offerings include the use of synchronous and asynchronous models, open anytime enrolment vs. courses run at set start and stop periods, as well as the availability of archives for future access.
  6. Support: Research into the MOOC experience is still limited. An area of particular weakness is the support systems that are required by learners. For example, what roles do TAs and tutors play in ensuring learner engagement and success? How effective and important are peer learning strategies and meetups? What additional support is required where MOOCs are offered as an additional “layer” in traditional classroom based learning?
  7. Assessment: Assessment of learner activity remains unsettled for many initiatives. Some systems have to date emphasized peer-based assessment. Others have run parallel classroom assessment for MOOCs. Some systems have adopted auto (or computer) grading. Questions of assessment are central to determining future acceptance of MOOCs by traditional universities. While micro credentialing (badges) and competency-based learning have gained recognition over the past several years, many universities continue to equate quality of assessment with quality of their institution name. Assessment remains a controversial and closed system.
  8. Space: Most MOOCs are offered by large providers such as edX and Coursera and represent top-tier universities. Emerging providers, such as Instructure Canvas, offer anyone the option of running a MOOC. Other MOOCs (see emphasize learning control and distributed interaction. Important questions remain regarding who owns the space of learning and the contributions of individual learners.
  9. Knowledge type/domain: Questions regarding content are important in determining design, pedagogy, and learner support. A course targeting basic skills in math, for example, will likely adopt a different design approach than one that focuses on critical thinking. Many entry-level courses require the learner to become familiar with language and terms, rather than on developing views and perspectives that question core assumptions or power relationships. Matching instructional forma and approach to the knowledge domain presents learning design challenges and shapes pedagogical approaches.

The nine components presented above are heavily influenced by foundational concerns regarding technology, system design, and system/course evaluation & improvement.

1 See (p 9-14) for differences between LA and EDM