TY - JOUR
T1 - Self-regulated learning in learning environments with pedagogical agents that interact in natural language
AU - Graesser, Arthur
AU - McNamara, Danielle
N1 - Funding Information:
The research on was supported by the National Science Foundation (SBR 9720314, REC 0106965, REC 0126265, ITR 0325428, REESE 0633918, ALT-0834847, DRK-12-0918409), the Institute of Education Sciences (R305G020018, R305H050169, R305B070349, R305A080589, R305A080594), and the Department of Defense Counter Intelligence Field Activity (H9C104-07-0014). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF, IES, or DoD.
PY - 2010/10
Y1 - 2010/10
N2 - 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.
AB - 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.
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U2 - 10.1080/00461520.2010.515933
DO - 10.1080/00461520.2010.515933
M3 - Article
AN - SCOPUS:77958141856
SN - 0046-1520
VL - 45
SP - 234
EP - 244
JO - Educational Psychologist
JF - Educational Psychologist
IS - 4
ER -