The Role of Time in Self-Directed Personalized Learning Environments: An Exploratory Analysis

Abstract

The assumption that there is a relationship between speed and ability (i.e., that students who learn faster are smarter) is implicit in the foundational design of traditional standardized education environments. This assumption was rooted in the theories of early educational psychologists who helped shape curricula and assessments in classrooms, and standardize practices and policies that still influence education today. However, as education institutions increasingly shift from standardized approaches to more personalized ones, there are reasons to question the validity of the “faster is smarter” view of learning. Personalized learning environments have the potential to afford flexibility and opportunity to all students in ways that were previously unmanageable. Advancements in science and the development of new technologies have expanded what is possible, but in order to take advantage of these advancements, we must question the assumptions that have shaped our standardized institutions and critically evaluate whether to carry them over into personalized learning environments. In this dissertation, I use data from 75 Algebra 1 students across 2 schools that have adopted a self-directed personalized learning model to examine questions of speed and ability. This dissertation study shows that while there was incredible individuality with respect to speed of learning, this individuality did not relate to overall academic achievement. Instead, what mattered most with respect to differences in achievement was whether the student reached mastery, regardless of how long it took. I found that in self-directed personalized learning environments, the factors that we expect should affect speed of learning and progress in the course (such as re-taking assessments multiple times) do not appear to make much difference, raising many questions about what kinds of cues, signals, and indicators teachers and researchers should consider in their practice and studies of these environments. The dissertation concludes by considering the implications that this insight has for issues of practice, research, and the future of personalized learning.