Introduction
Distance
learning has gone through three generations technologically linked pedagogy,
from print and post, to mass media telecommunications such as television, radio
and film, and a third generation, driven by more interactive network supported
technologies, as exemplified by the world wide web (Anderson & Dron, n.d.).
The first two generations were typified by a “one-to-many” delivery system,
where information flowed out in a largely one way direction from instructor to
student (Simonson, Smaldino, & Zvacek, 2015). The third generation saw a
great increase in “many-to-many” learning, where student-student interactions
were far more common. It has been argued that this evolution has been driven
by, and contains many similarities to, industrialization in the manufacturing
sector. Otto Peters’ theory of industrialization of teaching (Simonson et al.,
2015) drew comparisons between the specialization of roles in developing and
delivering learning content, and the mass production of books and educational
resources, to the production of commercial goods. Now some argue, we are
entering a fourth generation, one of more “intelligent flexible learning”,
driven by algorithms and other technology enhancements, along with associated
changes in teaching theory and practice (Anderson, & Dron, 2012).
Pros and Cons
A
great many studies suggest that distance learning (and increasingly this means
online learning), produces equivalent outcomes to that which takes place in
face-to-face on-site institutional learning (Fonolahi, Jokhan, & others.,
2014; Kuo, Walker, Belland & Schroder, 2013; Naidu, 2014; Simonson et al.,
2015). While there have been some who question the validity of this research
(Miron, Horvitz, Gulosino, Huerta, Rice, Shafer, & Cuban, 2013; Xu, &
Jaggars, 2013), it appears that market demands will ensure that online
learning is here to stay for the foreseeable future. The MIT-Harvard led, free
online course and certification program, edX had over 10 million students in
2016, while Coursera enrolled over 23 million (Shah, 2016). Overall, 1 in 4
higher education students in America are enrolled in an online course (“Report:
One in four students”, 2016). Demand for access to low cost education is
growing globally. At edX they hope to one day serve over a billion students at
a time (Regalado, 2012).
Perceptions
Whatever
the perceptions of distance learning are now, they will almost certainly
improve over the next few years. As people come to accept technology as part of
life, and use it for daily communication and personal interactions, they will
increasingly see it as a viable option for learning as well (“The future of
education”, n.d.). In addition, the lessons of the past are used to improve the
products of tomorrow. Competition will also drive the adoption of best
practices, as technology allows for institutes to compete internationally for
students. The expectations of quality from the enormous number of potential
students looking to enroll each year will create pressure for these online programs
to match the rigor of on-campus programs (Agarwal & Paucek, 2015). Of
course perception is unlikely to ever surpass reality in any long term or
meaningful way. The proof is in the pudding so to speak. That means the best
way to improve perception is to improve the quality of online learning.
My
job as an instructional designer includes acting as an ambassador for effective
learning systems. This includes technology, theory and processes. As such it is
important that I am able to speak with knowledge and authority on the relative
strengths and weaknesses of online programs and supporting technology. To do
this I must stay up to date of current trends and research findings, and take
opportunities to share this information with policy makers and the public when
possible. I do feel however, that the most important thing I can do, is ensure
the products I work on are developed using sound instructional systems design
practices, and follow appropriate learning theory.
The Future
At
some point, probably in the not too distance future, a technology development
of some type will produce another paradigm shift in learning. It was only 28
years ago that the World Wide Web, followed by the release of the Netscape
Browser in 1994, changed the face of online course delivery. By enabling easy
to create hyperconnected media, digital learning content could be instantly
delivered to millions of students. The first iPhone was introduced to the U.S.
in 2007 (“8 Years of the iPhone”, 2014), and sparked a revolution in the use of
social media and on-the-go access to the Web. At some point soon, a new
technology will almost certainly create new opportunities for online learning.
Quite possibly this will be the aforementioned flexible and individualized
learning.
Tools
of the past enabled mass production of course content and delivery, providing
greater access to more people (Simonson et al., 2015), but it was one size fits
all. New tools are being developed to allow custom fit courses and content in a
variety of ways. Badge systems and focused accreditation programs such as edX’s
MicroMasters (Shah, 2016) provide increased options for students. Algorithms
that can read your emotional state and adjust the presentation of material
offer more personalized study (Kaliouby, 2017; Paul, 2014). During the initial
stages, there will likely be mixed feelings about efficiency and efficacy. But
if good instructional design practices are adhered to, improved outcomes,
followed by positive perceptions, are likely to follow.
Previous
generations of technology enhanced pedagogy and andragogy overlapped and merged
(Anderson, & Dron, 2012). Books are still used, and are more frequently
electronic, video is distributed online more than on CD, and other forms of
content and instructional interaction will also continue to find new and
effective forms in the digital realm. It is only a matter of time before
society as no longer perceives a distinct line between distance learning
and on-campus learning. Chip Paucek predicts that by 2020 “there will no longer
be online or on-campus students. Just students” (Agarwal & Paucek, 2015).
To me that also means there will no longer be online learning and on-campus
learning, there will just be learning. People will instead be arguing the
merits of tank VR learning versus transcranial stimulation learning. Or, more
likely trying to figure out what to bother teaching at all now that AI and
Robots are running everything. The rich will probably just inject their
children with RNA knowledge packs. If current trends continue, which seems more
than likely, computers twenty years from now will be 1,000 times as powerful as
today, and human genome editing will be common (Satell, 2015). These
developments suggest the world two decades from now is all but unimaginable.
Robin
References
8 Years of the
iPhone: An interactive timeline. (2014, July 27). Retrieved from http://time.com/2934526/apple-iphone-timeline/
Agarwal, A. &
Paucek, C. (2015, January 11). The future of online learning. Financial
Times [Web site]. Retrieved from https://www.ft.com/content/f8a03bbe-9802-11e4-b4be-00144feabdc0?mhq5j=e5
Anderson, T.,
& Dron, J. (2012). Learning technology through three generations of
technology enhanced distance education pedagogy. European Journal of Open,
Distance and E-Learning. Retrieved from
http://files.eric.ed.gov/fulltext/EJ992485.pdf
Fonolahi, A. V.,
Jokhan, A., & others. (2014). Are students studying in the online mode
faring as well as students studying in the face-to-face mode? Has equivalence
in learning been achieved? Journal of Online Learning and Teaching, 10(4),
598.
Kaliouby, R.
(2017). Computers can now read your emotions. Here’s why that’s not as scary as
it sounds. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2017/03/computers-can-now-read-your-emotions-here-s-why-that-s-not-as-scary-as-it-sounds/
Kuo, Y.-C.,
Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study
of student satisfaction in online education programs. The International
Review of Research in Open and Distributed Learning, 14(1), 16–39.
Naidu, S. (2014).
Looking back, looking forward: the invention and reinvention of distance
education. Distance Education, 35(3), 263–270. https://doi.org/10.1080/01587919.2014.961671
Miron, G.,
Horvitz, B., Gulosino, C., Huerta, L., Rice, J. K., Shafer, S. R., & Cuban,
L. (2013). Virtual Schools in the US 2013: Politics, Performance, Policy, and
Research Evidence. National Education Policy Center. Retrieved from https://eric.ed.gov/?id=ED558723
Paul, A.M.
(2014). Computer tutors that can read students’ emotions. The Hechinger
Report. Retrieved from http://hechingerreport.org/computer-tutors-can-read-students-emotions/
Regalado, A.
(2012, November 2). The most important education technology in 200 years.
Retrieved from http://www.technologyreview.com/news/506351/the-most-important-education-technology-in-200-years/
Report: One in
four students enrolled in online courses. (2016, February 25). Retrieved from https://onlinelearningconsortium.org/news_item/report-one-four-students-enrolled-online-courses/
Satell, G. (2015).
3 Reasons to believe the singularity is near. Forbes [Web site].
Retrieved from
https://www.forbes.com/sites/gregsatell/2016/06/03/3-reasons-to-believe-the-singularity-is-near/#6943f2157b39
Shah, D. (2016,
December 13). edX's 2016: Year in review. Retrieved from https://www.class-central.com/report/edx-2016-review/
Simonson, M.,
Smaldino, S., & Zvacek, S. (2015). Teaching and learning at a distance:
Foundations of distance education (6th ed.) Charlotte, NC: Information Age Publishing.
Xu, D., & Jaggars, S. S. (2013). The impact of online learning
on students’ course outcomes: Evidence from a large community and technical
college system. Economics of Education Review, 37, 46–57.