Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system

Abstract

Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of help seeking. Automated feedback on help seeking driven by this method led to a lasting improvement in students' help-seeking behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides help seeking. © Division 15, American Psychological Association.