TY - GEN
T1 - Using problem-solving context to assess help quality in computer-mediated peer tutoring
AU - Walker, Erin
AU - Walker, Sean
AU - Rummel, Nikol
AU - Koedinger, Kenneth R.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Collaborative activities, like peer tutoring, can be beneficial for student learning, but only when students are supported in interacting effectively. Constructing intelligent tutors for collaborating students may be an improvement over fixed forms of support that do not adapt to student behaviors. We have developed an intelligent tutor to improve the help that peer tutors give to peer tutees by encouraging them to explain tutee errors and to provide more conceptual help. The intelligent tutor must be able to classify the type of peer tutor utterance (is it next step help, error feedback, both, or neither?) and the quality (does it contain conceptual content?). We use two techniques to improve automated classification of student utterances: incorporating domain context, and incorporating students' self-classifications of their chat actions. The domain context and self-classifications together significantly improve classification of student dialogue over a baseline classifier for help type. Using domain features alone significantly improves classification over baseline for conceptual content.
AB - Collaborative activities, like peer tutoring, can be beneficial for student learning, but only when students are supported in interacting effectively. Constructing intelligent tutors for collaborating students may be an improvement over fixed forms of support that do not adapt to student behaviors. We have developed an intelligent tutor to improve the help that peer tutors give to peer tutees by encouraging them to explain tutee errors and to provide more conceptual help. The intelligent tutor must be able to classify the type of peer tutor utterance (is it next step help, error feedback, both, or neither?) and the quality (does it contain conceptual content?). We use two techniques to improve automated classification of student utterances: incorporating domain context, and incorporating students' self-classifications of their chat actions. The domain context and self-classifications together significantly improve classification of student dialogue over a baseline classifier for help type. Using domain features alone significantly improves classification over baseline for conceptual content.
KW - Adaptive collaborative learning systems
KW - Computer-supported collaborative learning
KW - Intelligent tutoring
KW - Peer tutoring
UR - http://www.scopus.com/inward/record.url?scp=79957467234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79957467234&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13388-6_19
DO - 10.1007/978-3-642-13388-6_19
M3 - Conference contribution
AN - SCOPUS:79957467234
SN - 3642133878
SN - 9783642133879
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 155
BT - Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings
T2 - 10th International Conference on Intelligent Tutoring Systems, ITS 2010
Y2 - 14 June 2010 through 18 June 2010
ER -