If you work in the field of education, you’re probably familiar with the term “personalized learning.” It’s all the buzz right now. Schools across the United States are talking about personalization and putting various forms of personalized learning into practice to meet their students’ diverse needs. Although educators across the country are using the terms “personalization” and “personalized learning,” there is little consensus about what these words mean or what types of practices they describe. This brief post will consider just a few conceptions of personalized learning that have been put forth in the literature on this approach to education.
One strand of literature is centered on the use technology to achieve personalization (see Chen, 2008; Lin, Yeh, Hung, & Chang, 2013). These authors use “personalized learning” to connote primarily web-based learning systems that adapt curriculum sequencing, pacing, and presentation based on the unique backgrounds, knowledge, preferences, interests, and learning goals of each student. Within this conception of the term, learning occurs primarily through engagement with digital programs as opposed to interactions with teachers and classmates toward particular learning goals. The role of the teacher is significantly reduced within this model of personalized learning as computer programs take on most of the responsibility for delivering the curriculum and tailoring certain elements of it to individual students’ preferences and needs. This definition is generally used by scholars in the field of educational technology.
Other definitions and applications of the term broaden personalization beyond the use of just technology to meet students’ diverse needs in the classroom. These conceptions share many similarities with more traditional classroom approaches but assert learning is not personalized unless the teacher tailors curriculum and instruction to the unique interests, preferences, and needs of each student. For example, The U.S. DOE (2010) defines personalized learning in its National Education Technology Plan 2010 as “instruction that is paced to learning needs, tailored to learning preferences, and tailored to the specific interests of different learners” (p. 12). With this and similar definitions of personalized learning, teachers remain largely in control of the educational process. They use their knowledge of students’ interests and needs to determine the pace, style, and content of curriculum for each student.
John Clarke (2013) asserts, however, that these types of teacher-directed approaches should be defined as individualization and not personalization. For Clarke, personalization involves students taking increased control of and responsibility for their learning whereas individualization occurs when teachers (or computer programs) tailor curriculum and instruction to students. As he puts it, “The difference between individualization lies in control. We can individualize education by imposing it, but students choose to personalize their own learning. Their volition drives their inquiry” (Clarke, 2013, pp. 6-7). Based on this definition, learning is only personalized when students are actively involved in determining what they learn, how they develop new knowledge and skills, and how they demonstrate their new proficiencies. This definition suggests many practices and approaches currently labeled as “personalized” could be better described as “individualized” because the teacher maintains control of curricular and instructional decisions for students.
Bray and McClaskey (2015) offer a similar definition of personalization as Clarke (2013), but they assert there are different “stages” of personalized learning. Stage One is primarily teacher-directed and shares many similarities with Clarke’s (2013) conception of individualization. The primary difference is that teachers design projects and activities in ways that allow for learner voice and choice, which introduces some degree of student control into the learning environment. In Stage Two, students begin to take on increasing responsibility for their learning and “co-design” learning experiences with teachers by determining how they best access and engage with new content, choosing the tools and strategies that are suited to their needs, creating assessments, and identifying extended learning opportunities that match their interests and aspirations (p. 77). Stage Three is “learner-driven” as students are fully self-directed and design their own learning experiences both within and outside the traditional classroom setting, create their own assessments, demonstrate their learning through public showcases and exhibitions of their work, and learn at their own pace in a competency-based system of assessment (p. 77). This classification scheme that Bray and McClaskey offer is useful for distinguishing among different degrees of personalization.
Campbell, Robinson, Neelands, Hewston, and Mazzoli (2007) make similar distinctions between “deep” and “shallow” personalization in their assessment of the United Kingdom’s movement toward personalized learning. Their conception of shallow personalization also shares many similarities with Clarke’s (2013) definition of individualization in that it involves teachers tailoring the curriculum and instruction to the unique students in their classrooms. For Campbell et al. (2007), shallow personalization attempts to make educational services more “streamlined, accessible, and efficient” but does not shift the relationship between teachers and students in a way that they become “co-producers of educational knowledge” (p. 144). Deep personalization, on the other hand, is “disruptive” because it requires “changed power relations over knowledge production” (p. 145). With deep personalization, teachers and students become partners in the learning process, and students are actively involved in making decisions about what, where, and how they learn. Students become co-producers and “co-authors of their educational script[s]” rather than passive recipients of educational services in a deeply personalized setting (p. 138).
While Campbell et al. (2007) distinguish between “deep” and “shallow personalization, Yong Zhao (2016) differentiates between “process” and “outcome” personalization (p. 9). For Zhao, process personalization entails customizing aspects of the learning process such as pace, content, products, and learning environments for each individual student. It allows students to work at their own pace, choose where and how they learn, and how they demonstrate new knowledge and skills. With process personalization, all students work toward mastery of the same established set of standards. Outcome personalization, on the other hand, allows students to determine the objectives of their educational experiences. With outcome personalization, there is not a prescribed curriculum that all students are required to follow or a common set of standards that all learners must meet. Instead, students work with teachers to develop unique educational programs toward their own goals that are based on their personal strengths and interests as learners.
This brief review of the literature demonstrates that scholars and practitioners use the term “personalized learning” to describe an assortment of educational practices with varying degrees of student and teacher control over the learning process. The way the term is currently used, both computer-based “remedial” math programs and student-directed and -determined curricula might be labeled as “personalized learning.” These practices, however, represent vastly different approaches to teaching and learning and reflect disparate educational values and philosophies. Therefore, it is critical for researchers to precisely define personalized learning in their studies and identify the theories and practices that undergird their understanding of the term and those that do not. Such definitional clarity will help advance scholarship on personalized learning by allowing researchers to distinguish different forms of personalization and investigate their unique associations with student, teacher, and school outcomes.
Bray, B., & McClaskey, K. (2015). Make learning personal: The what, who, wow, where, and why. Thousand Oaks, CA: Corwin.
Campbell, R.J., Robinson, W., Neelands, J., Hewston, R., & Mazzoli, L. (2007). Personalised learning: Ambiguities in theory and practice. British Journal of Educational Studies, 55(2), 135-154.
Chen, C. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2), 787-814.
Clarke, J.H. (2013). Personalized learning: Student-designed pathways to high school graduation. Thousand Oaks, CA: Corwin.
Leadbeater, C. (2004). Personalisation through participation: A new script for public services. London: Demos.
Lin, C.F., Yeh, Y., Hung, Y.H., & Chang, R.I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199-210.
U.S. Department of Education. (2010). Transforming American education: Learning powered by technology. Retrieved July 10, 2015, from https://www.ed.gov/sites/default/files/NETP-2010-final-report.pdf
Zhao, Y. (2016). Personalization and autonomy. In Y. Zhao, H. Tavangar, E. McCarren, G. F. Rshaid, & K. Tucker, The take-action guide to world class learners: How to make personalization and student autonomy happen (pp. 8–18). Thousand Oaks, CA: Corwin.