Abstract
This article discusses the occurrence and measurement of self-regulated learning (SRL) both in human tutoring and in computer tutors with agents that hold conversations with students in natural language and help them learn at deeper levels. One challenge in building these computer tutors is to accommodate, encourage, and scaffold SRL because these skills are not adequately developed for most students. Automated measures of SRL are needed to track progress in meeting this challenge. A direct approach is to train students on fundamentals of metacognition and SRL, which is the approach taken by iSTART, MetaTutor, and other agent environments. An indirect approach to promoting SRL, taken by AutoTutor, is to track the student's knowledge and SRL based on the student's language and to respond intelligently with discourse moves to promote SRL. This fine-grained adaptivity considers the student's cognitive states, the discourse interaction, and the student's emotional states in a recent AutoTutor version.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
