The Cognitive Style Revolution – Excerpts from MOOC Research

Note:  I will use this space over the next month to share excerpts from my dissertation The Evolution & Impact of the Massive Open Online Course.  The research was a Delphi study bringing together 20 MOOC experts to discuss the MOOC in educational, political, and sociocultural terms (slides from the oral presentation can be seen here). Upon library clearance, the entire document will be available through a Creative Commons license.  The following is from Chapter 5, the conclusions of the research study.  This excerpt tackles one of the educational implications of the study — the re-emergence of cognitive learning theory in the educational milieu.

1. Computer science replaces education research & theory.  In the time since the Delphi research study (note:  the expert study ran in October and November of 2013), prominent MOOC voices involved in development and surrounding political affairs have continued to advocate for educational solutions engaged within a cognitive worldview.  Coursera co-founder Andrew Ng recently promoted the book “Why Students Don’t Like School:  A Cognitive Scientists Answers Questions About How the Mind Works and What It Means for the Classroom,” in doing so advocating for the cognitive approach, saying, “[This is a] great book on applying cogsci principles to teach better. Loved this!” (Ng, 2014). This exchange, passed along the social media platform Twitter to over 14,000 followers, marked some of the first recognized link to educationally rigorous learning theory, a change in the histories MOOC developers have heretofore shared with the world. Since 2011, those at the forefront of developing MOOCs have either linked their structures with very recent technological phenomenon such as Khan Academy (Vanderbilt, 2012), or avoided making a link to the history of education at all (Koller, 2013). The link between the artificial intelligence and machine learning backgrounds of the primary MOOC developers and the cognitive principles at the foundation of their academic disciplines now has been linked to existing learning theory literature. This link suggests MOOC developers believe the principles they employ for teaching machines are ideal principles for teaching humans.

Such developments might be ideal if, as Marvin Minsky put it, the brain is a computer made of meat (Minsky, 1982).  Such a comparison may be provocative but does not withstand psychological scrutiny. The evolution of educational psychology, generations removed from the dawn of cognition in the 60s and 70s, has rendered cognitive learning theory archaic (Siemens, 2013a).  While cognitive theory remains popular in computer science and among some educators, the work of educational psychologists and social scientists such as Jean Piaget, Etienne Wenger, and Bonnie Nardi have identified the limits of cognitive learning theory while using its strengths to create new theories of learning such as constructivism, communities of practice, and activity theory, theories accepted within education as more robust than cognitive theory (Wenger, 2013). A theoretical return to cognition thus creates a rift in the field of educational research, where a focus on the MOOC phenomenon as a learning model substitutes the field of computer science for educational psychology theory.  Moreover, the ahistorical attitude of the MOOC movement (Khan, 2012) implicitly invalidates prior education research.  The end result is a whitewash of the field of education, where prior initiatives and research are discarded without consideration, and where the MOOC model and similar education initiatives can grow and thrive despite sizable concerns existing within prior and contemporary education research.

The dismissal of education as a field of study and subsequent re-adoption of cognitive learning theory has already seen prominence in public policy debates.  California Governor Jerry Brown, who as Governor is an Ex Officio Regent for the University of California system, recently pushed for the adoption of college courses designed to run without a professor or teaching staff:

If this university can probe into “black holes,” he said, “can’t somebody create a course — Spanish, calculus, whatever — totally online? That seems to me less complicated than that telescope you were talking about,” referring to an earlier agenda item.  After receiving pushback from UC provost Aimée Dorr, who delivered the presentation, that students are “less happy and less engaged” without human interaction, Brown said those measurements were too soft and he wanted empirical results (Koseff, 2014).

This development is not novel; the State of California has engaged in a number of cognitive-heavy policy initiatives over the past year, most notably the partnership of San Jose State University with MOOC providers Coursera and Udacity, as well as the drafting of SB520, state legislation designed to promote and encourage the development and implementation of scalable online lower-level undergraduate courses. What is unique to this Board of Regents discussion is Governor Brown’s desire to remove the human element from courses entirely, shown through a belief such an endeavor would be easier to implement than hard science initiatives such as an astronomy telescope.  Unique also is a desire to measure efficacy through back-end learning analytics rather than what Brown alludes to as soft educational measurements.  The Delphi results of #data, in conjunction with recent public policy discussion, shows a societal shift towards learning analytics as preferential data, data derived from cognitive models of learning.

Despite the rich history of education as an academic discipline and field of research, education discussion and political movement throughout the MOOC phenomenon has largely been driven by outside voices.  The rise in online learning notoriety over the last several years has largely come on the backs of what media outlets call celebrity educators (Vanderbilt, 2012; Friedman, 2012; Weber, 2011), individuals who have celebrated their lack of theoretical and pedagogical expertise within the education discipline (Thrun, 2012; Khan, 2012).   In this world, the lack of immediate consensus on a MOOC topic such as #expertise makes sense, as the social space where education is debated has erased expertise and replaced it with education newcomers with a cognitive worldview and dependent on a specific brand of qualitative data to solidify their theoretical lenses.  As these MOOC luminaries have been allowed to define the parameters of education’s history and purpose, the results of their analytic evidence will likely be viewed and advertised as all-knowing, rather than inclusive of and understanding education in the environmental and contextual terms of the rigorous education theory research of the past 25 years.

Non-Web Citations:

Khan, S. (2012).  The one-world schoolhouse:  Education reimagined.  New York: Twelve.
Minsky, M. (1982).  Why people think computers can’t.  AI Magazine, 3(4).
Wenger, E. (2013).  Learning in landscapes of practice:  Recent developments in social learning theory.  Presentation at Pepperdine University’s Distinguished Speaker Series.  Los Angeles, CA:  October 5, 2013.

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