Level Up! with Personalized Learning

Co-authored by Dr. Melissa Swisher, Lecturer, Purdue University

The unrest in education is no secret: Many undergraduate students work full time and want the flexibility of online or distance learning classes, fewer students enroll in college classes (but see the projections from the National Center on Education Statistics), and teacher turnover remains high. Fads in education come and go, but none have stuck long enough to produce a revolution. Kamenetz (2018, November 16) classifies these movements in personalized learning as either (1) an emphasis on learners progressing at their own pace via computer programs or (2) learners working on projects, setting their own goals, and having one-on-one time with their instructors. Perhaps in the search for something new, we could explore older but effective, non-traditional educational technologies that meet the needs of these fads. We should look back because students and teachers deserve effective education (see Barrett et al., 1991; Michael, 1991).

We previously wrote about Khan Academy adopting mastery learning (criterion) with an individualized pace; the first type of Kamenetz’s personalized learning. In fact, personalized learning is the new (old; Keller, 1968, 1974) educational technology for K-12 learners embraced by the Bill & Melinda Gates Foundation, the Chan Zuckerberg Initiative, and many educators around the world.

Using technology in the classroom is great if incorporated appropriately. Technological advancements, in and of themselves, are not a guarantee that learners will master content. Clear, behavioral learning objectives that can be presented in person or through computer-aided personalized systems of instruction (see Brinckman, Rae, & Dwivedi, 2007; Pear & Crone-Todd, 1999; Pear & Novak, 1996; Rae & Samuels, 2011; Springer & Pear, 2008) are much more important to academic success than the method of presentation. Teachers (especially in K-12) are expected to collect daily measures of student learning; behavior analysis can provide the technology for those measures (e.g., Greer & McDonough, 1999), and many free tools are available to collect those data.

Personalized systems of instruction will only enable learners to master material if all four components plus lectures are included within a course: (1) proctors to grade unit quizzes, (2) frequent quizzes over instructional units, (3) immediate feedback on quizzes, and (4) learner control over their own progression (Calhoun, 1976). Giving learners more control over their education, especially with respect to when they complete units, is great for the student but can lead to procrastination (see Jarmolowicz, Hayashi, & St. Peter Pipkin, 2010). To encourage equally-distributed studying, teachers can recommend an appropriate pace (Henneberry, 1976), assign multiple deadlines (Ross & McBean, 1995), and/or allocate a progressive rather than fixed-points schedule for quizzes (Mahoney, 2017).

Kamenetz’s second type of personalized learning has broader impact. It will be incredibly difficult to accommodate every learner’s interests in a class of 25, let alone 200 students. A reasonable compromise is to create interdisciplinary courses or programs that will cover a multitude of interests. Interteaching (Boyce & Hineline, 2002; Saville, Cox, O’Brien, & Vanderveldt, 2011), Precision Teaching (Lindsley, 1992; Quigley, Peterson, Frieder, & Peck, 2018), and Active Student Responding (Kellum, Carr, & Dozier, 2001; Monro & Stephenson, 2009) could easily be incorporated within any course framework to give students a bigger role in their own education. Interteaching and precision teaching allow learners to collaborate in pairs to assess their knowledge and receive one-on-one tutoring when necessary. Rotating partners in interteaching creates a unique experience in which learners can bring their own interests and background knowledge to bear on course topics. Active Student Responding provides many opportunities for all learners to respond to frequent teacher questions, and the teacher can provide immediate feedback. This can approximate one-on-one interactions within larger classes. While none of these behavior analytic teaching technologies explicitly involves projects (Blumenfeld et al., 1991; Harris et al., 2015; Remijan, 2017) and student-initiated goals (Rowe, Mazzotti, Ingram, & Lee, 2017), teachers could also use those course elements (see Johnson, 2015 for suggestions).

 

Fads in education produce much enthusiasm in the beginning, but very few have had staying power–we even see this with behavior analytic teaching technologies. Perhaps we can use some older evidence-based education methods and teacher supports to meet the needs of the existing classroom and propel the personalized learning movement.

Image credits:

1. Cover image provided courtesy of stokpic under Pixabay license
2. Image provided courtesy of CDC under Pexels License
3. Image provided courtesy of Pixabay under Pixabay license
4. Image provided courtesy of Office of Naval Research under public domain