title: Self-Directed Learning: A Cognitive and Computational Perspective.
abstract: A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition and the limits to such advantages remain poorly understood. In this article, we review the issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, the development of efficient "active learning" algorithms that can select their own training data is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.
authors.affiliation: New York University todd.gureckis@nyu.edu.
authors.forename: Todd M
authors.initials: TM
authors.lastname: Gureckis
authors.affiliation: New York University.
authors.forename: Douglas B
authors.initials: DB
authors.lastname: Markant
keywords: active learning; intervention-based causal learning; machine learning; self-directed learning; self-regulated study