Adaptive Learning

Read the complete book (MOOCs: Opportunities, Impacts, and Challenges Massive Open Online Courses in Colleges and Universities), available from

In his article “A History of Teaching Machines” (American Psychologist, September, 1988), Ludy T. Benjamin traced the pedigree and legacy of teaching machines in the U.S: “By the early 1960s, teaching machines were much in the news. National and international conferences were held to discuss the new technology, and popular magazines and scientific journals published news of the emerging research and applications.”[1]

Online learning and MOOCs, big data, and analytics, have re-energized a long-standing educational initiative—technology-mediated teaching. Adaptive learning, building on big data and learning analytics, is the latest iteration. Modern adaptive learning includes the implementation of data-driven analytics to help faculty shape the delivery of course materials to adapt to individual abilities.  These tools offer personalized learning, mediated by technology.  In his essay “Adaptive Learning Could Reshape Higher Ed Instruction” (April 4, 2013, Inside Higher Ed), Peter Stokes, executive director of postsecondary innovation in the College of Professional Studies at Northeastern University, describes adaptive learning as “an environment where technology and brain science collaborate with big data to carve out customized pathways through curriculums for individual learners and free up teachers to devote their energies in more productive and scalable ways.”[2]  (Stokes is also a contributor to the report “LEARNING TO ADAPT: A Case for Accelerating Adaptive Learning in Higher Education,” funded by the Bill and Melinda Gates Foundation.)

There is a long history of teaching machines—mechanical, multimedia, and computers—extending back to an 1809 patent for an educational appliance for the teaching of reading. By 1936, there were nearly seven hundred patents for teaching devices. The history of these devices can be traced from the original patented machines of the nineteenth century through the teaching/testing devices of Sidney Pressey in the 1920s to the more sophisticated teaching machines of Harvard psychologist B.F. Skinner in the 1950s.

Where initial devices were more about testing, late-twentieth-century efforts focused on teaching that enables students to adapt to machine-provided feedback. B.F. Skinner created a mechanical “teaching machine” in the mid-1950s that broke learning into sequenced steps and allowed students to pace themselves as they worked through a series of questions. The steps resembled processes that tutors use to engage students and guide them, via feedback, toward increasingly accurate responses and new knowledge. The machine posed questions and offered new questions only when the student answered correctly; an incorrect answer caused the machine to repeat the question. Skinner’s efforts eventually fell out of favor in part because few companies were willing to invest in designing and developing materials for a product with an indeterminate future, but interest in adaptive learning persisted through the latter half of the century with the emergence of affordable personal computers.

Contemporary instructional designers adhere to Skinner’s basic tenets, offering adaptive learning tools that present course materials to students who do not move on to subsequent questions until their performance, based on data generated in the adaptive learning process, indicates competency and knowledge. Adaptive learning combines individualized instruction (or rather, something that feels like it to the student), peer interaction, effective and engaging simulations, and applications that dynamically adapt to the learner’s abilities.

Because MOOCs and related online and software-mediated learning environments leverage earlier adaptive-learning techniques, campus leadership should consider the value in making historic overview part of their consideration of MOOCs. They might also also look for ways to engage private-sector partners in their strategic thinking, particularly since corporate startups are eager to partner with colleges and universities in developing adaptive learning products. In April 2013, Rice University held the first annual Workshop On Personalized Learning, bringing together leaders from higher education and adaptive-learning startup companies to “plot a course to the future of personalized learning” ( Participants were invited to explore the potential of big data to ensure time and cost efficiencies in the delivery of learning outcomes. Presenters included researchers from Knewton, Carnegie Learning, and Khan Academy as well as faculty and researchers from MIT, Arizona State University, and Duke University.

Meanwhile, the Bill and Melinda Gates Foundation is investing heavily in adaptive learning.  The foundation has solicited proposals from colleges and universities for ten $100,000 grants to help develop new partnerships implementing adaptive learning courses. To encourage participation, the foundation hosted a March 2013 webinar outlining the details of their Adaptive Learning Market Acceleration Program. The session showcased new research indicating that “intelligent” (meaning “digital”) tutors are nearly as effective as humans, citing related research by educational psychologist Benjamin Bloom. In 1984, in the journal Educational Researcher, Bloom reported that students tutored one-to-one performed two standard deviations better than students taught in conventional lecture courses. Commercial MOOC providers have also touted Bloom’s research:


Figure 9-1

The foundation’s strategy is to invest in “market change drivers” that include exemplary implementations of adaptive learning courses combined with research and analysis of learning outcomes in order to accelerate the adoption of adaptive learning in higher education. The Foundation also has formed a loose coalition of leaders from a dozen colleges and two associations to share information about developing and implementing adaptive learning. These schools and this coalition could become a rich laboratory for partnerships between technology vendors and campuses. Participants in the Gates Foundation group include:

  • American Association of State Colleges and Universities
  • American Public University System
  • Arizona State University
  • Association of Public and Land-Grant Universities
  • Capella University
  • Excelsior College
  • Kaplan University
  • Kentucky Community and Technical College System
  • Rio Salado College
  • Southern New Hampshire University
  • SUNY Empire State College
  • University of California at Berkeley
  • University of Texas at Austin
  • Western Governors University

The Gates Foundation also commissioned a report from Education Growth Advisors, entitled “Learning To Adapt: A Case for Accelerating Adaptive Learning in Higher Education.” (The group also issued a more comprehensive report entitled “Learning to Adapt: Understanding the Adaptive Learning Supplier Landscape.”) The report outlines the potential of adaptive learning and how it might help address the “Iron Triangle” of cost, access and quality, and describes potential adoption paths, opportunities and barriers, solutions, and case studies. It attempts to detail the capabilities of emerging adaptive learning products in an effort to help college leadership make decisions.

The report simplifies campus review of adaptive learning options through analysis of potential benefits of current and emerging providers and products, and includes very brief case studies from a few universities. The document is not unbiased; it reflects the enthusiasm and vision of the Gates Foundation and those who contributed to the narrative. (As its website states, “Education Growth Advisors is affiliated with Education Growth Partners, a Stamford Connecticut-based private equity firm focused exclusively on growth equity investments in education companies in the preK-12, higher education, corporate training, and lifelong learning sectors. Education Growth Partners invests in profitable, innovative, high-potential companies that are seeking capital and expertise to reach scale.”) The report opens with three congratulatory scenarios about successful experiences in personalized education, each extolling the potential of adaptive learning systems. The first paragraph of the report is unambiguous: “Welcome to the world of adaptive learning—a more personalized, technology-enabled, and data-driven approach to learning that has the potential to deepen student engagement with learning materials, customize students’ pathways through curriculum, and permit instructors to use class time in more focused and productive ways. In this fashion, adaptive learning promises to make a significant contribution to improving retention, measuring student learning, aiding the achievement of better outcomes, and improving pedagogy.”[3]

One hears echoes of B.F. Skinner and other, earlier, proponents of now-antiquated “teaching machines” in the report’s descriptions of students interacting with course materials in a digital environment. Similar to those earlier claims, we are promised that when “students answer particular questions incorrectly, they may be directed back to appropriate points in the materials to better acquaint themselves with the relevant concepts or facts.” It would be difficult to fault the reader for concluding that this is simply the latest iteration of the same old story. It might be tempting to dismiss such reports (and many do) as vehicles for corporate expansion into new and profitable markets. We have survived several generations of enthusiasts and profiteers working to develop technology-mediated education products; it is tempting to say that this one will soon burn itself out as well.

But this time may very well be different.  Unlike the earlier mechanical contraptions, which were isolated (and isolating), everyone now has a “teaching machine” in his or her shirt pocket. We are interconnected and interactive in ways not possible before.  We have become predisposed to the use of ubiquitous technologies that mediate our information and communication exchanges. On the policy side, there is focused corporate and governmental pressure to innovate in the interest of increasing access to education and improving graduation rates. Foundations are investing in collaborative programs and families are ready for educational alternatives that do not saddle them with life-long debt.

[1] Ludy T. Benjamin, “A History of Teaching Machines,” American Psychologist 43, no. 9 (September 1988): 703–712.

[2] Peter Stokes, “Adaptive Learning Could Reshape Higher Ed Instruction (essay),” Inside Higher Ed, April 4, 2013,

[3] ADAM NEWMAN, PETER STOKES, and GATES BRYANT, “LEARNING TO ADAPT: A Case for Accelerating Adaptive Learning in Higher Education” (EDUCATION GROWTH ADVISORS, n.d.).


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