Platforms and Publishers: delivering on adaptive learning

Get the complete book Thinking Strategically about MOOCs: The Role of Massive Open Online Courses in the College and University at Amazon in print or kindle version.

The report “Learning To Adapt: A Case for Accelerating Adaptive Learning in Higher Education” makes an important distinction between adaptive learning “platforms” and course content “publishers.” To date, campuses have generally had choices between vendors offering platforms with adaptive learning authoring tools and publishers providing course content with delivery models that try to incorporate adaptive learning.

Platform providers sell infrastructure and software for developing adaptive learning models. Examples include aNewSpring, Cerego, CogBooks, Knewton, LoudCloud, and Smart Sparrow. Publishers active in this market include traditional firms eager to capitalize on emerging commercial opportunities—such companies as Cengage, Jones & Bartlett Learning, Macmillan, McGraw-Hill, Pearson, and Wiley. Emerging digital-only publishing include Adapt Courseware and the Open Learning Initiative.

For now, the most successful providers working in the adaptive learning market may be those traditional publishers with the wherewithal to leverage existing content in new ways while negotiating productive partnerships with emerging platform providers. These partnerships are worth monitoring as they provide vendors a powerful vehicle to sell innovative educational resources to higher education and to insert themselves into the dialogue about the future of educational content.  As Mitchel Stevens of Stanford University has noted, who gets a seat at that table is still up for grabs. Campus leaders would do well to shoulder their way in and not wait for an invitation to help shape the future.[1]

Of new partnerships that have emerged, that between publisher Pearson and platform provider Knewton appears to have gained the most significant traction. Pearson is busily amassing a substantial portfolio of education companies through both purchases and partnerships. In addition to acquiring Learning Catalytics, for example, they have partnered with a rising startup called Knewton.

Founded by former Kaplan executive Jose Ferreira, Knewton is an adaptive learning infrastructure platform provider. Expanding on earlier successes of efforts like Carnegie Mellon’s Online Learning Initiative, the Knewton infrastructure “makes it possible for anyone to build the world’s most powerful adaptive learning applications. Knewton technology consolidates data science, statistics, psychometrics, content graphing, machine learning, tagging, and infrastructure in one place in order to enable personalization at massive scale” (Source:

Knewton builds on the past decade of work in big data and learning analytics. Ferreira is adamant about the power of mining big data and marrying analytics to digital course content in order to turn the traditional classroom on its head, thereby freeing instructors to manage their time in new ways.  Ferreira and his team are aggressively pursuing the potential of big data and analytics. In an interview with Marc Parry of the Chronicle of Higher Ed (“A Conversation With 2 Developers of Personalized-Learning Software,” The Chronicle of Higher Education, July 18, 2012), Ferreira noted the disparity in the scope of data available from services like Google and the data available from a student engaging with digital course materials: “You do a search for Google; Google gets about 10 data points. They get, by our standards, a very small amount of data compared to what we get per user per day. If they can produce that kind of personalization and that kind of business, based off the small amount of data they get, imagine what we can do in education,” he says. Ferreira and the Knewton team have developed a platform to extract a great deal of data from the user experience: “Knewton’s capturing in the hundreds of thousands of data per user per day. We’re capturing what you’re getting right, what you’re getting wrong, what answers you’re falling for if you get something wrong, what concepts are in that answer choice that you’re falling for. We’re also capturing when you log into the system; how much you do; what tasks you do; what you don’t do; what was recommended that you do that you didn’t do, and vice versa.”[2]

All of this data extraction results in predictions about learning outcomes followed by prescriptions for follow-up actions for each student. Applying the system’s learning analytics to the data generated by student interactions leads to “the perfect sentence, or perfect clip, or perfect problem for you at any one time, based on what you’re the weakest at, and what’s most important, and how you learn it best.”  As part of this effort, Knewton is launching what it calls “learning modality adaptivity,” a feature that will discern what and how much to show each student each day. The module is intended to understand how students learn best and when to present content appropriate to learning abilities and demonstrated progress. According to Ferreira, his system will “figure out things like, you learn math best with a video clip, or you learn science best with games instead of text, or in addition to text—and we can figure out what the optimal ratio is for you. We can figure out things like, you learn math best in the morning, and verbal concepts best in the evening, on average. Maybe you learn math best between 8:32 and 9:14. If so, we’ll know it. It means when you show up in the morning to do some practice, we’re going to try to feed you math, and if you show up in the evening, we’ll try to feed you more verbal, because that’s when you’re most receptive to those subject matters.”[3]

Adaptive learning systems do not stop with individual learners. The power of such programs lies in the interconnectivity of all data streams of all students in the course. Predictions and prescriptive actions are personalized for each student, with the learning trajectories of different students divergent by design. The system decides which course modules will be presented to each student, and when, based on data mining and analytics. In this regard, Knewton is representative of the marriage of big data, learning analytics, and personalized adaptive learning that fosters such potentially disruptive models as self-paced learning and flipped classrooms. By measuring productivity and progress, Knewton’s and other adaptive learning systems will recommend different times of the day for different students to “crack the book.” This blows up the standardized classroom model governed by the calendar and the clock, and gestures toward a hybridized, self-paced learning model.

Adaptive learning models may encourage consideration of alternatives to credentialing that are currently based on seat time and the credit hour. They may be attractive to institutions eager for solutions to problems of access to college, cost containment, and degree completion rates. Emerging partnerships and collaborations between corporations and startups that likely would have been competitors just a few years ago may fuel additional disruptive (or distracting) models.

Knewton’s partnership with Pearson allows it to leverage pre-existing contractual relationships with higher education institutions to deliver course content in a new way. The digital content of every Pearson textbook must now be “tagged” with metadata that powers the Knewton analytics system. Pearson can now use the Knewton model to re-power existing online reading and mathematics courses. With significant shares of the higher education textbook and digital book markets, the deal gives Knewton a boost as it markets to institutions.  Perhaps most significantly, Knewton now has access to pre-existing student data captured in the Pearson machine. The infusion of this comparative data will increase the accuracy of the Knewton system.

All of this is relevant to the consideration of MOOCs and online learning vendors. Daphne Koller, co-founder of Coursera, speaks of the analytical strengths of MOOCs, extolling their adaptive pedagogy. “We can now do the kind of rapid evolution in education that is common at companies like Google, which ‘A/B test’ their ad positions and user interface elements for effectiveness,” she has said. “These websites evolve in a matter of days or weeks rather than years”  (Booker, Ellis. “Can Big Data Analytics Boost Graduation Rates?” Information Week, February 5, 2013).

Many will object to the use of data mining, learning analytics, and other methodologies that inform website advertising in the development of academic learning environments. Even some MOOC proprietors are dubious. Mike Feerick, CEO of Advance Learning Interactive Systems Online (ALISON), which provides interactive multimedia courseware for certification and standards-based learning, acknowledges the importance of data analytics while also asserting that expecting such tools to solve education’s problems is simply wrong. Feerick is just as adamant that talented and dedicated teachers who make effective use of these emerging tools are the key to pedagogical success as Koller and Ferreira are about the promise of data mining and analytics.[4]

Media coverage of this issue highlights the technologies that these adaptive learning entrepreneurs promote, leading many to assume that they seek to supplant educators with software. Along with the persistent drumbeat coming out of these startups, the coverage inspires observations from scholars like Evgeny Morozov, who describes misplaced faith in technology as a “dangerous ideology.”  In his article “The Perils of Perfection” (New York Times, March 2, 2013), Morozov labels this ideology “solutionism: an intellectual pathology that recognizes problems as problems based on just one criterion: whether they are ‘solvable’ with a nice and clean technological solution at our disposal.”  He explains his ideas more fully in his book, To Save Everything, Click Here: The Folly of Technological Solutionism. Morozov raises important questions about the expectations our culture holds out for technological solutions to cultural and social problems. He specifically questions the impact of “nice and clean” big data solutions on our ability to negotiate the messy business of living and learning. Morozov argues that, in the effort to cleanse our social and cultural institutions of that messiness—“from education to publishing and from music to transportation” —in the name of mere efficiency is to lose the benefit of the struggle and decision-making that contribute to maturity.  Morozov cites Sartre, who “celebrated the anguish of decision as a hallmark of responsibility,” and notes that celebrating the value of such inefficiency and struggle “has no place in Silicon Valley.”

Indeed, there is undeniable value in inefficiency and imperfection. We learn from our mistakes and we do well to foster spaces where we can mess up and gain insights from the process. Proponents of adaptive learning are confident that the digital spaces they are creating are just that—spaces where we learn from our mistakes. At issue for higher education is the extent to which we identify and implement big data and adaptive learning solutions. Morozov’s concerns notwithstanding, these resources will be part of whatever MOOCs and learning management systems colleges and universities put in place. We must ensure that we understand how to make the best use of these tools as supplements to the established value and success of educators.  We need to listen to the cautionary tales of those like Morozov to understand the reasonable limits of such potentially invasive technologies. Campus leaders and stakeholders across all sectors of higher education and from all sorts of institutions need to fully understand the implications of these proposed solutions, insert appropriate checks and balances, and ensure continued appreciation for the necessarily awkward messiness of learning.


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