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

\bibitem{asee_peer_27538}
  Johanes, P., \& Lagerstrom, L. (2017, June), \emph{Adaptive Learning: The Premise, Promise, and Pitfalls}
  Paper presented at 2017 ASEE Annual Conference \& Exposition, Columbus, Ohio.
  10.18260/1-2--27538

Petr Johanes and Larry Lagerstrom.  "Adaptive Learning: The Premise, Promise, and Pitfalls".  2017 ASEE Annual Conference & Exposition, Columbus, Ohio, 2017, June.  ASEE Conferences, 2017.  https://peer.asee.org/27538 Internet.  08 May, 2024

\bibitem{asee_peer_27538}
  Petr Johanes and Larry Lagerstrom.
  "Adaptive Learning: The Premise, Promise, and Pitfalls".
  \emph{2017 ASEE Annual Conference \& Exposition, Columbus, Ohio, 2017, June}.
  ASEE Conferences, 2017.
  https://peer.asee.org/27538 Internet.  08 May, 2024

@INPROCEEDINGS{asee_peer_27538
author = "Petr Johanes and Larry Lagerstrom"
title = "Adaptive Learning: The Premise, Promise, and Pitfalls"
booktitle = "2017 ASEE Annual Conference \& Exposition"
year = "2017"
month = "June"
address = "Columbus, Ohio"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/27538}
number = {10.18260/1-2--27538}
}

TY  - CPAPER
AB  - 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.
AU  - Petr Johanes
AU  - Larry Lagerstrom
CY  - Columbus, Ohio
DA  - 2017/06/24
PB  - ASEE Conferences
TI  - Adaptive Learning: The Premise, Promise, and Pitfalls
UR  - https://peer.asee.org/27538
DO  - 10.18260/1-2--27538
ER  -