Identifying Learning Conditions that Minimize Mind Wandering by Modeling Individual Attributes
The propensity to involuntarily disengage by zoning out or mind wandering (MW) is a common phenomenon that has negative effects on learning. The ability to stay focused while learning from instructional texts involves factors related to the text, to the task, and to the individual. This study explored the possibility that learners could be placed in optimal conditions (task and text) to reduce MW based on an analysis of individual attributes. Students studied four texts which varied along dimensions of value and difficulty while reporting instances of MW. Supervised machine learning techniques based on a small set of individual difference attributes determined the optimal condition for each participant with some success when considering value and difficulty separately (kappas of .16 and .24; accuracy of 59% and 64% respectively). Results are discussed in terms of creating a learning system that prospectively places learners in the optimal condition to increase learning by minimizing MW.
Keywordsengagement mind wandering affect machine learning
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