Adaptive Learning, the ability for software
and hardware to analyze student input and change accordingly, has been present
in one form or another since the mid-twentieth century (Lowendahl, 2014). However,
many feel it has now reached a stage whereby industries and education alike
recognize its potential and are implementing real solutions, which according to
Drs, Thornburg, Soloway, and Rogers makes it an emerged technology (Laureate
Education, 2014a; Laureate Education, 2014b; Rogers, E.M.). According to
Lowendahl (2014), this is primarily due to the technology of online learning to
track and report student data in real time. Companies like Knewton,
Instructure, Blackboard, and Carnegie Learning have increased research into its
efficacy, while schools such ASU and the University of Alabama of instituted
successful adaptive learning programs for some courses (Lowendahl, 2014; Fain,
2014). Ben-Naim (as cited in Fain, 2014) categorizes those interested in
adaptive learning processes into (a) data collectors, (b) those focused on introductory
coursework, and (c) those creating adaptive platforms (like
Smart Sparrow).
The challenges faced by adaptive learning proponents
are still significant. First, for systems to adapt accurately to multiple
students, they need a massive data set to analyze. This data set can be very difficult
to obtain (Fain, 2014). Additionally, there are privacy concerns surrounding the
sharing of that data, and challenges with generalizing adaptation techniques
outside of a very few subjects (e.g. language, math, and SAT preparation)
(Lowendahl, 2014). Finally, Zimmer (2014) introduces the potential of poor
implementations by faculty members or trainers utilizing adaptive learning
platforms to generate content to sour the adoption potential of others.
However, if these challenges can be overcome,
the benefits to students and society are significant. In theory, if expanded to
multiple topics and successful for multiple student types, adaptive learning
systems could improve everything from student engagement, to grade point
averages, to graduation rates, and flexible learning. (Lowendahl, 2014; Zimmer,
2014). The impact these improvements would have on society are broad and
encompassing, and could include economic improvement, job growth, and national
increases in STEM performance.
The biggest pitfall is the lack of large and
structured data sets to steer adaptive learning processes. Solutions could
include agreements between governments, schools, and adaptive learning
companies to anonymously share data sets equally to engender the quickest time
to realization through healthy competition and collaborative research and
development.
Some examples of Adaptive Learning companies, platforms,
and schools:
Fain, P. (2014, October 10). Online and In Control. Retrieved from Inside Higher Ed Web site:
https://www.insidehighered.com/news/2014/10/10/emerging-adaptive-software-puts-faculty-members-charge-course-creation
Laureate Education. (Producer). (2014a). David Thornburg: What is emerging technology?
[Video File]. Video posted to Walden University Web site:
https://class.waldenu.edu/webapps/blackboard/content/listContent.jsp?course_id=_7461404_1&content_id=_25128482_1
Laureate Education. (Producer). (2014b). Elliot Soloway: Emerging vs. emerged
technologies [Audio File]. Video posted to Walden University Web site:
https://class.waldenu.edu/webapps/blackboard/content/listContent.jsp?course_id=_7461404_1&content_id=_25128482_1
Lowendahl, J. M. (2014, July 23). Hype Cycle for Education, 2014. Retrieved from Gartner Web site:
http://www.gartner.com/document/2806424?ref=exploremq
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.
Zimmer, T. (2014, December 1). The Adaptive Advantage: How E-Learning Will Change Higher Ed.
Retrieved from Forbes Web site:
http://www.forbes.com/sites/ccap/2014/12/01/the-adaptive-advantage-how-e-learning-will-change-higher-ed/