Asee peer logo

Adaptive Learning: The Premise, Promise, and Pitfalls

Download Paper |


2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Computing Technology Session 2

Tagged Division

Computers in Education

Tagged Topic


Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


Petr Johanes Stanford University

visit author page

Petr Johanes is currently a PhD student in Learning Sciences and Technology Design (LSTD) at the Stanford University Graduate School of Education. He holds a B.S. and M.S. from the Department of Materials Science at Stanford University and has experience teaching engineering courses as well as researching engineering education, especially in the context of online learning. Right now, Petr is investigating the role of epistemic cognition in learning.

visit author page


Larry Lagerstrom Stanford University

visit author page

Larry Lagerstrom is an Associate Dean and Director of Summer Session at Stanford University (and previously was the Director of Academic Programs at the Stanford Center for Professional Development). Before coming to Stanford he taught computer programming and electrical engineering for sixteen years at U.C. Berkeley and U.C. Davis. He has degrees in physics, math, history, and interdisciplinary studies, including a PhD in the history of science and technology. He also has developed a MOOC on “Understanding Einstein: The Special Theory of Relativity.”

visit author page

Download Paper |


In a 2015 speech before the American Council of Education, John Hennessy, President and Professor of Engineering at Stanford University, laid out a vision for how new technological tools and pedagogical methods can improve higher education. He especially highlighted the opportunity to revitalize courses by crafting online and hybrid learning materials that adapt their speed, depth, and approach to the individual student. Others have made the same point. The National Academy of Engineering, for example, has listed “personalized and adaptive learning” as one of its Grand Challenges, and a Learning Analytics Workgroup, composed of thirty-seven representatives from universities, foundations, government entities, non-profit organizations, and for-profit companies, has put forth an “endgame” vision of “personalized cyber learning at scale for everyone on the planet for any knowledge domain.” Given that companies such as Knewton, Acrobatiq, Udacity, and Khan Academy are either commercializing or implementing adaptive learning technology, and online higher education institutions such as Western Governors University are building it into their courses, it is likely in the near future that engineering schools and faculty will face questions about their use of this and similar technologies that enhance learning. These questions may come from students and parents, of course, but also from the media and perhaps even accreditors. In this review paper, we aim to provide guidance to engineering education leaders and engineering faculty via three main goals. First, to explain what adaptive systems are and what kinds of data they require. Second, to categorize the main use cases and possibilities of adaptive systems. Third, to outline the current limitations and concerns surrounding adaptive systems. Engineering leaders and instructors can then determine if their pedagogical context is amenable to deploying these systems, and education researchers can navigate the current systems’ characteristics to find areas where to make impactful contributions.

Johanes, P., & Lagerstrom, L. (2017, June), Adaptive Learning: The Premise, Promise, and Pitfalls Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27538

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2017 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015