Posted by: Michael Atkisson | August 28, 2011

Comparing MOOCs, MIT’s OpenCourseWare, and Stanford’s Massive AI Course

What is a MOOC?

Massive Open Online Courses (MOOCs) are large-scale online courses (in the thousands of participants) where an expert or group of experts from a particular field both 1. create the large draw to the course, and 2. facilitate a multi-week series of interactive lectures and discussion forms on critical issues from that field. Participants are expected to self-organize, to share and discuss the course material, and to create and publish new artifacts that represent their learning. Additionally, MOOC participation is recorded and published openly so that those who come upon it later may follow peripherally.

Where did MOOCs Come From?

This is best answered in the words of David Cormier and George Siemens,

“The term was coined in response to Siemens and Downes’s 2008 “Connectivism and Connective Knowledge” course. An initial group of twenty-five participants registered and paid to take the course for credit. The course was then opened up for other learners to participate: course lectures, discussion forums, and weekly online sessions were made available to nonregistered learners. This second group of learners–those in The Open Course who wanted to participate but weren’t interested in course credit–numbered over 2,300. The addition of these learners significantly enhanced the course experience, since additional conversations and readings extended the contributions of the instructors.” (2010, p. 32).

Since 2008, several other MOOCs have developed. These include:

What is a MOOC Experience?

The scale of interaction among MOOC participants is like that of massively multiplayer online games, such as World of Warcraft, but where as in the gaming environment large numbers of people come together online to play, self-organize, develop skill, strategize as a group, and execute strategies, MOOCs, on the other hand, facilitate learning about or the development of a particular knowledge domain at a participation scale ripe for diversity.

As Mackness, Mak, and Williams described, “The experience was, in part, positive and stimulating, and in part frustrating and negative…For participants not only was the course design unique, but so too was the learning experience. Easy access to advancing technologies means that learners can now take control of where, when, how, what and with whom they learn. There has been a massive growth in online social networking in recent years. The use of online and other web 2.0 technologies is becoming common. Increasingly some learners can, and do, choose not to use the learning environment provided by a course or institution, but to meet instead in locations of their choice, such as Facebook, Twitter, wikis or blogs (Beetham, 2008; Guldberg & Mackness, 2009)” (2010, p. 267). This great flexibility can also detract from the learning for many participants. It has been difficult for some to find the right group to join, consequently parts of the MOOC experience have not been well received (2010).

Other ways to experience a MOOC are to lurk or to follow the course after-the-fact. For example, unlike the live MOOC participant, I have only accessed posted materials and recorded MOOC sessions, which I have found to be engaging and full of value. I also noticed that my trajectory of feelings followed what many in the live MOOC also experienced. For example, in the LAK11 MOOC, a significant drop-off occurred and some disillusionment was expressed when data mining and data science were the focus. For someone not from those fields, it was overwhelming to see all the skills that one did not possess. It made me think about how relevant my contributions to the field of Learning Analytics could be if I were not also a data miner. But as I continued through the sessions I regained confidence that there were lots of ways to participate in the filed of learning analytics. I thought it was remarkable how much I felt that I was there in the class, this feeling of presence was much more so than if I would have been just watching a webinar after the fact. I felt immersed through my after-the-fact peripheral participation.

Is MIT’s OpenCourseWare a MOOC?

The short answer is no. I again point to Cormier and Siemens:

“In an open course, participants engage at different levels of the educator’s practice, whether that be helping to develop a course or participating in the live action of the course itself. This is distinctly different from the idea of open in the open content movement, where open is used in the sense of being free from the intellectual property stipulations that restrict the use and reuse of content” (2010, p. 32).

Though MIT’s OpenCourseWare is revolutionary, making content publicly available is not enough because it only focuses on the content. The proposed benefit of MOOCs, on the other hand, is “the interaction, the access to the debate, to the negotiation of knowledge–not to the stale cataloging of content” (2010, p. 32). Essentially, MOOCs and other open courses are “open” (i.e., transparent) in the practice of knowledge negotiation and developing the field of study (2010, p. 32), opposed to just allowing open content consumers to be aware of latest developments.

Are Stanford’s Massive Online Courses MOOCs?

Stanford has opened three courses to the public for the fall of 2011: AI, Databases, and Machine Learning. The number of participants in these courses will be unprecedented: 135,455, 38,499, and 38,779 respectively as of the middle of the day on Aug 27, 2011. The number will continue to increase until registration closes and the courses begin in October. According to the course pages, participants “receive a statement of accomplishment from the instructor,” including a normative performance ranking against other online students, but only enrolled Stanford students receive credit and grades. Online students can submit questions to the instructor and staff, but these questions will go through an aggregation and rating process where only “top-rated” questions will be answered (“Introduction to Artificial Intelligence – Fall 2011,” n.d., “Introduction to Databases – Stanford University,” n.d., “Machine Learning – Stanford University,” n.d.).

MOOCs seem to differ from Stanford’s classes in these principle ways:

  1. Direct access to course facilitators: MOOC (yes), Stanford (no)
  2. Inclusion of all participation: MOOC (yes), Stanford (no)
  3. Ranking of performance: MOOC (no), Stanford (yes)
  4. Degree of separation between accredited and online participants: MOOC (lesser), Stanford (greater)
  5. Flexible, personalized curriculum: MOOC (yes), Stanford (no)
  6. Define or develop the field: MOOC (yes), Stanford (no)
  7. Other differences may emerge as the Stanford courses proceed.

Stanford’s large-scale courses do not appear to be MOOCs, but they are massive, are online, have celebrity draw (Peter Norvig), appear to invite both real-time and asynchronous participation and self organization, and make the sessions and forms publicly available like MOOCs do. The Stanford courses seem to have a technological innovation over the MOOC model, however, in the ability to rank individuals’ course performance, which should be interesting to see what metrics and technologies are used to achieve such measures at scale.


MOOCs and the similar variations I have discussed appear to be carving out a substantial niche in the array of online learning experiences. They are a significant and unique addition to how people may engage virtually at scale for both learning and exploration.


Cormier, D., & Siemens, G. (2010). Through the Open Door: Open Courses as Research, Learning, and Engagement. Educause Review, 45(4), 30-39.

Introduction to Artificial Intelligence – Fall 2011. (n.d.). . Retrieved August 27, 2011, from

Introduction to Databases – Stanford University. (n.d.). . Retrieved August 27, 2011, from

Machine Learning – Stanford University. (n.d.). . Retrieved August 27, 2011, from

Mackness, J., Mak, S. F. J., & Williams, R. (2010). The Ideals and Reality of Participating in a MOOC. Proceedings of the 7th International Conference on Networked Learning 2010.

Mcauley, A. A., Stewart, B., Cormier, D., & Siemens, G. (2010). MOOC Model for Digital Practice. Practice. Retrieved from


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  6. I think the innovation MOOCs have brought has definitely changed our perception of online teaching. Compared to the old ways whereby an academic would give a video recording of what may be regarded as a ‘boring’ lecture, todays online education asks for student involvement and participation. The short lecture formats are definitely are bonus, as well as the intermittent pause in the middle of videos whereby students are required to answer a question before moving on to watch the rest of the video. Furthermore, I must say before I heard about the ‘peer review’ format in assessing a lot of the students work, as well as the introduction of ‘self-review’ work, I was definitely very dubious. However, after seeing the data results of the positive correlation between peer-review and expert marks, and the even MORE positive correlation between self-review and expert marks, I think there is definitely potential here. Of course, the next step and perhaps the most fundamental issue in online education is the assessment of arts and humanities subjects, which are difficult to grade via MCQs. However, I think despite the doubts regarding this form of online learning, people need to realise that it is an experiment, and a way of enhancing the way we learn. Also, it is perhaps the most cleaver way of obtaining data regarding student age, demographic, geographic locations, culture, and all various other important factors. These data are important as they are a guide to how students learn, and how students can benefit from the various formats of education, whether it be online or offline.

  7. […] ist ein MOOC ein MOOC”.  Hier findet man auch einen sehr interessanten Link auf einen Artikel von Michael Atkinson von 2011, der die verschiedenen MOOC-Formate untereinander vergleicht und weitere Hintergrundinformationen […]

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