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Instructional Trend Spotlight: Personalized Learning
Many of you are busy teaching and/or working and don’t have a lot of time to research trends in education technology and online learning. To save you time, we’ll be periodically sending out snapshots of instructional trends to keep you in the know.

Instructional Trend Spotlight: Personalized Learning

by Heather Leslie

Definition of Personalized Learning

Personalized Learning, or Personalization, is a buzzword in education that comprises

everything from students getting to work at their own pace to a completely customized course

experience using adaptive technologies. Personalization is student-centered, or student-driven, in

which students co-create content and build knowledge through collaboration and connectivity to

the Internet (Beus, 2017). Personalization has also been described as “technology-assisted

differentiated instruction” (Feldstein, 2015). The US National Education Technology Plan 2017

defines personalized learning as “instruction in which the pace of learning and the instructional

approach are optimized for the needs of each learner. Learning objectives, instructional

approaches, content (and its sequencing) may all vary based on learner needs. In addition,

learning activities are meaningful and relevant to learners, driven by their interests, and often

self-initiated” (page 9).


History of Personalized Learning

The term “personalized learning” has been used since as early as the 1960s,

although there remains a lack of agreement on a single definition or concept (Epstein, 1961).

Proponents of personalized learning say that it is an evolving term (Fielder, 2011). The

personalization of learning, which can also be referred to as differentiated instruction, can occur

when the teacher customizes the learning experience for each student. This requires continually

assessing the student against clearly defined standards and goals and pathing the student through

a learning program (Herrington, 2000). Personalization can also be on the learner side where the

learner chooses a learning path based on skills, knowledge, or interest.


Prevalence of Personalized Learning

Technology has enabled the prevalence of personalization, known as adaptive

learning. This is a process in which machine learning is able to use learning analytics to provide

just-in-time intervention and support for students through the use of intelligent tutoring systems,

computerized adaptive testing, and unique learning paths. Colorado Technical University uses a

personalized learning system called Intellipath, for example, to assess students, provide them

feedback in real time, and allow them to progress through a course at their own pace (Johnson,

2016). More and more universities are investing in personalization or adaptive learning

technologies to increase retention and graduation rates (Raths, 2017).


Merits and Issues of Personalization

The idea that not every student learns the same way or at the same speed and that

instruction should be adapted to fit the needs of the individual student is not entirely controversial

among educators. However, when this concept of personalization is embedded with technology,

the issue becomes much more divisive. Instructors, for example, may fear losing their jobs to

artificial intelligence or “smart” automated Teacherbots and therefore resist this kind of

technology takeover (Popenici, 2017). And yet machines don’t joke, counsel, or inspire. Humans

learn from humans. Black children do better in school when they have a black teacher

(Gershenson, 2017). So this issue of teachers being replaced entirely by machines is not yet

feasible. Another potential caution of collecting large amounts of data about students is data

breaches and concern over student privacy (Wintrup, 2017).


Support of Personalization

Many educators are proponents of personalized learning and differentiated

instruction in order to tailor the learning to the student’s strengths and needs. Competency or

mastery-based education allows students to demonstrate mastery and if they cannot demonstrate

mastery, they do not advance in the course and are provided with remedial support more quickly

than, say, failing an entire course (Quinn, 2017). At the same time, personalized learning is

coming under scrutinyy because researchers say there is not evidence to support the claims made

by supporters of personalized learning (Herold, 2017).  A traditional model of

learning often results in what Salman Khan, founder of Khan Academy, refers to as the Swiss

cheese problem of holes in student understanding (Headden, 2013). Personalized learning is

much more learner centered as it focuses on what the learner needs to know and gaps in

knowledge rather than what the teacher needs to teach. Some skeptics may question the

effectiveness of personalized learning, but the same can be said about the efficacy of traditional

education. As schools and universities look for ways to customize education to the needs of

individual students in a way that’s scalable, many will look to personalized learning as the model

going forward into the future.


References


Beus, B. (2017, January 2). Top higher education learning trends to look for in 2017.

Retrieved Getting Smart: http://www.gettingsmart.com/2017/01/highereducation-

learning-trends-in-2017/


Epstein, S. (1961). The first book of teaching machines. Danbury, CT: Franklin Watts, Inc.


Feldstein, M. (2015, November 10). Why personalized learning matters to a new generation of

college students. Retrieved from EdSurge:

https://www.edsurge.com/news/2015-11-10-why-personalized-learning-matters-to-a-newgeneration-

of-college-students


Fielder, S. (2011). Personal learning environemts: concept of technology? International Journal

of Virtual and Personal Learning Environments, 2 (4), 1-11.


Gershenson, S. (2017). The long-run impacts of same-race teachers. Bonn: I.Z.A. Institute of

Labor Economics.


Headden, S. (2013). The promise of personalized learning. Education Next, 13 (4).


Herold, B. (2017, November 7). The case(s) against personalized learning. Retrieved from Ed Week: https://www.edweek.org/ew/articles/2017/11/08/the-cases-againstpersonalized-

learning.html


Herrington, J. (2000). An instructional design framework for authentic learning environments.

Educational Technology Research & Development, 48 (3), 23-48.


Johnson, C. (2016). Adaptive learning platforms: Creating a path for success. Colorado Technical University.

ECUCAUSE Review. Retrieved from https://er.educause.edu/articles/2016/3/adaptive-learning-platforms-creating-a-path-for-success


Office of Educational Technology. (2017). The United States National Education Technology Plan 2017. Washington, DC: Office

of Educational Technology.


Popenici, S. &. (2017). Exploring the impact of artificial intelligence on teaching and learning in

higher education. Research & Practice in Technology Enhanced Learning, 12 (1), 1-13.


Quinn, R. (2017, November 2). Advocates tout online 'personalized learning'. Charleston

Gazette. Retrieved from https://www.wvgazettemail.com/news/education/personalized-learning-pitched-as-alternative-to-current-wv-ed-methods/article_346aa079-b495-5270-9912-091ca8102971.html


Raths, D. (2917, January 4). Scaling up with adaptive learning. Retrieved

from Campus Technology: https://campustechnology.com/articles/2017/01/04/scaling-up-withadaptive-

learning.aspx


Wintrup, J. (2017). Higher education's panopticon? Higher Education Policy, 30 (1), 87-103.