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.
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