Year: 2010
Type: article
Abstract: We investigated the possibility of predicting students' engagement and learning gains during a tutoring session from trait measures of motivation, engagement, burnout, cognitive ability, prior knowledge, and task related measures.Participants completed a multiple choice pretest, a learning session, a posttest, and a battery of individual differences tests and questionnaires.Multiple regression and exploratory factor analyses indicated that the individual differences measures yielded medium sized effects at predicting learning gains as well as engagement levels that were self-reported during the tutorial session.In general, self-reported interest in the task and confidence in learning from a computer tutor coupled with working memory capacity and attentional abilities were the major predictors of both engagement and learning.
Authors: Sidney K. D’Mello, Claire Williams, Patrick Hays, Andrew M. Olney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: Institute of Education Sciences
OpenAlex ID: W2623606279