Instruction based on adaptive learning technologies

Abstract

Abstract

In this chapter, we selectively review empirical evidence for adaptivity in learning technologies. We ask, what forms of adaptivity can help students learn better? We define what adaptivity means in the context of instruction, introduce a conceptual framework (the “Adaptivity Grid”) that distinguishes 15 different forms of adaptivity, and discuss experimental studies with students that isolate the effect of specific forms of adaptivity in learning technologies. We find empirical support for 12 of 15 forms of adaptivity. More specifically, we suggest forms that provide strong bang for the buck (i.e., produce greater learning gains relative to the effort needed to implement), particularly step-loop adaptation to student strategies and errors, design-loop adaptivity generally, and task-loop adaptations to student knowledge. The current chapter is different from its version in the prior edition in that we clarify key notions, include a more straightforward definition of adaptivity, add recent literature, and provide tables with concrete adaptations that have empirical support. It differs from other reviews of adaptivity in learning technologies in its use of a fine-grained taxonomy (the Adaptivity Grid) to develop a nuanced understanding of evidence, bringing us a step closer to an engineering practice of adaptive instruction grounded in empirical research.