Self‐Directed Learning Favors Local, Rather Than Global, Uncertainty

• Published in: Cognitive science 2016-01, Vol.40 (1), p.100-120
• Main Authors: Markant, Douglas B., Settles, Burr, Gureckis, Todd M.
• Format: Article
• Language: English
• Subjects: Active Learning; Coding; Cognition & reasoning; Data Collection; Decision Making; Evaluation Methods; Humans; Independent Study; Information Retrieval; Information sampling; Information Seeking; Judgment; Learning; Machine learning; Models, Educational; Preferences; Psychological Theory; Search Strategies; Self‐directed learning; Supervised Machine Learning; Uncertainty
• ISSN: 0364-0213; 1551-6709
• DOI: 10.1111/cogs.12220

Collecting (or “sampling”) information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by “active learning” research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model‐based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a “local” sampling strategy, which may reflect cognitive constraints on the process of information gathering.