Activating Learning at Scale

Abstract

Taking advantage of the vast history of theoretical and empirical findings in the learning literature we have inherited, this research offers a synthesis of prior findings in the domain of empirically evaluated active learning strategies in digital learning environments. The primary concern of the present study is to evaluate these findings with an eye towards scalable learning. Massive Open Online Courses (MOOCs) have emerged as the new way to reach the masses with educational materials, but so far they have failed to maintain learners' attention over the long term. Even though we now understand how effective active learning principles are for learners, the current landscape of MOOC pedagogy too often allows for passivity — leading to the unsatisfactory performance experienced by many MOOC learners today. As a starting point to this research we took John Hattie's seminal work from 2008 on learning strategies used to facilitate active learning. We considered research published between 2009 and 2017 that presents empirical evaluations of these learning strategies. Through our systematic search we found 126 papers meeting our criteria and categorized them according to Hattie's learning strategies. We found large-scale experiments to be the most challenging environment for experimentation due to their size, heterogeneity of participants, and platform restrictions, and we identified the three most promising strategies for effectively leveraging learning at scale as Cooperative Learning, Simulations & Gaming, and Interactive Multimedia