Affect, Meta-affect, and Affect Regulation During Complex Learning

Abstract

Complex learning of dif fi cult subject matter with educational technologies involves a coordination of cognitive, metacognitive, and affective processes. While extensive theoretical and empirical research has examined learners’ cognitive and metacognitive processes, research on affective processes during learning has been slow to emerge. Because learners’ affective states can signi fi cantly impact their thoughts, feelings, behavior, and learning outcomes, inquiry into how these states emerge and in fl uence engagement and learning is of vital importance. In this chapter, we describe several key theories of affect, meta-affect, and affect regulation during learning. We then describe our own empirical research that focuses on identifying the affective states that spontaneously emerge during learning with educational technologies, how affect relates to learning outcomes, and how affect can be regulated. The studies that we describe incorporate a variety of educational technologies, different learning contexts, a number of student populations, and diverse methodologies to track affect. We then describe and evaluate an affect-sensitive version of AutoTutor, a fully-automated intelligent tutoring system that detects and helps learners regulate their negative affective states (frustration, boredom, confusion) in order to increase engagement, task persistence, and learning gains. We conclude by discussing future directions of research on affect, meta-affect, and affect regulation during learning with educational technologies.