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Adaptive Learning: The Premise, Promise, and Pitfalls

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2018

Conference Session

Computing Technology Session 2

Tagged Division

Computers in Education

Tagged Topic

Diversity

Page Count

14

Permanent URL

https://peer.asee.org/27538

Download Count

108

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Paper Authors

biography

Petr Johanes Stanford University

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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.

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biography

Larry Lagerstrom Stanford University

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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.”

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Abstract

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. https://peer.asee.org/27538

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