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Busting the Top 5 Myths about Adaptive Learning

Approx 5 minute read

Jeremy Anderson, Deputy Chief of Academic Technology, Bay Path University

Jeremy Anderson, Deputy Chief of Academic Technology, Bay Path University

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Busting the Top 5 Myths about Adaptive Learning

Adaptive learning is growing up. Step back and you can see the steps along the yardstick of maturity: 10 years ago, adaptive learning was just getting onto the radar in higher education; five years ago it was a buzzword; and now a number of prominent institutions are promoting best practices for applying it in the classroom.

Just like an ungainly teenager who’s popped up six inches in a summer, adaptive learning’s growth has come with some awkwardness that is evident in some competing beliefs about how this technology can (or, alternately, does not) play an effective role in the future of education. Grab your goggles and lab coats; we’re going to inspect five of these perceptions!

Myth 1: Adaptive learning replaces faculty

I’ve helped launch adaptive initiatives with faculty at two organizations and spoken with colleagues at other institutions with adaptive programming, and one of the first concerns that arises in conversation is that the system is going to replace faculty members. The line of reasoning tends to be some variation of, “if students can interact with an adaptive system on their own time and work towards mastery with interventions from the system, where does the instructor fit into the process?”

There’s no denying students interact with learning experiences in the adaptive system to acquire and demonstrate baseline skills and knowledge, just like when reading a textbook or watching a video and filling an entry slip in the flipped classroom. (Quick side note - the obvious benefit of adaptive over such an approach, though, is that it adjusts content, pacing, order, and questions based upon students’ prior and current learning and preferences.)

When we think of adaptive learning in this light, the instructor remains central in designing the overall learning experience: writing outcomes, picking content, and designing activities and assessments through which students demonstrate competencies gained in the adaptive assignment. In other words, adaptive assignments are a fraction of the learning experience. What’s more, the adaptive system provides information to faculty members so that they can conduct timely, focused interventions early in the learning process (more on that below).

Myth 2: Adaptive learning is for online courses

It’s common to conflate adaptive learning with online learning. This misconception arises more than likely since adaptive systems are digital tools that mostly serve content online. That said, there are excellent examples of adaptive learning being used in onground and hybrid teaching and learning.

Arizona State University, for example, has revolutionized large-lecture instruction by assigning adaptive learning outside the classroom. University of Central Florida, meanwhile, playfully branded their hybrid adaptive learning approach as “blendaptive.” Here, students complete prep work in the adaptive system and the instructor reduces in-class time and customizes the learning experience according to students’ needs.

Still another approach, the emporium method, sees students completing adaptive activities during class time while instructional staff circulate to provide immediate support. Institutions such as Merrimack College and Oakton Community College have been recognized by the Online Learning Consortium for this innovative model.

Myth 3: There isn’t much data on the efficacy of adaptive learning

With so much promise for supporting student achievement, why hasn’t adaptive learning taken education by storm? One speculation laid out in the 2019 edition of EDUCAUSE’s Horizon Report is that “some institutions are waiting and watching while early adopters pilot, implement, and share what they have learned.”

Research on prior iterations of mastery learning (Bloom, 1984) and personalized learning via adaptive computer systems (VanLehn, 2011), however, lay a strong foundation for anticipating large improvements in student outcomes. Our own research at The American Women’s College has demonstrated significant, large effect sizes (d = 1.12) in math courses and significant, medium effects (d = .37) across 29 hybrid courses when adaptive learning was implemented (Anderson, Bushey, Devlin, & Gould, 2019).

Other studies summarized by Dziuban et al. (2018) have found similar evidence of learning improvement when considering course grades and the rates of students earning a D, F, or W in classes that use adaptive learning activities. Come on in, the water’s warm!

Myth 4: Adaptive learning is behaviorism

Noted progressive educator Alfie Kohn suggested that the type of computer-mediated personalized learning delivered by adaptive systems “not only assumes but perpetuates a ‘bunch-o’-facts approach’ to learning.” A world of cold, hard facts and discrete, granularized skills learned through cycles of “drill-and-kill” does sound scary when we want learners applying their learning, doesn’t it?

Still, ample research demonstrates that a large knowledge base is the essential foundation for transferring learning into similar and novel situations (Haskell, 2001). Indeed, this is how we’ve designed courses at The American Women’s College. Students begin their week with an adaptive learning assignment comprised of 10 to 15 smaller activities that represent the smallest building blocks of knowledge and skill. Questions at the end of each activity are written at the lower tiers of Bloom’s taxonomy to ensure base-level understanding.

Only after proficiency is demonstrated do students move to applying and constructing with their learning in group discussions, labs, summative assignments, etc. Instructors-as-social-constructivists are empowered to intervene early in the week to help struggling students move through their zones of proximal development.

Myth 5: Adaptive learning only works for math and science courses

Some of the most high-profile learning improvements achieved through adaptive learning are in the areas of math and sciences (think ASU’s BioSpine and UCF’s developmental math model recognized by Melinda Gates).

While most of the press focuses on these areas, any subject that has granular knowledge or skills and can be assessed with objective questions is a good candidate for adaptive learning. Introductory courses across the curriculum are especially ripe for incorporation of an adaptive layer, for example, since they tend to focus on developing a knowledge base for the subject. We made early efforts at The American Women’s College to bring up impactful adaptive learning in courses like introduction to business, introduction to sociology, and foundational courses in developmental psychology. The benefits, in the end, have been more dependent upon the quality of the course design and adherence to best practices rather than to the subject area.

As with other emerging technologies, educational professionals continue to scrutinize what adaptive learning’s place is and can be in our ecosystem. The good news is that many institutions have begun to compile best practices, successful models, and evidence of efficacy in the last several years. These trends bode well for the continued growth in the potential and impact of adaptive learning to drive improvements to student and instructor outcomes.


Anderson, J., Bushey, H., Devlin, M., & Gould, A. (2019). Cultivating student engagement in a personalized online learning environment. In E. Alqurashi (Ed.), Handbook of Research on Fostering Student Engagement With Instructional Technology in Higher Education (pp. 267-287). Hersey, PA: IGI Global.

Bloom, B. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4-16.

Dziuban, C., Moskal, P., Parker, L., Campbell, M., Holwin, C., & Johnson, C. (2018). Adaptive learning: A stabilizing influence across disciplines and universities. Online Learning, 22(3), 7-39. doi:10.24059/olj.v22i3.1465

Haskell, R. E. (2001). Transfer of learning: Cognition, instruction, and reasoning. San Diego, CA: Academic Press.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221. doi:10.1080/00461520.2011.611369

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