TY - GEN
T1 - Learning from Collaboratively Observing Videos during Problem Solving with Andes
AU - Craig, Scotty D.
AU - Vanlehn, Kurt
AU - Gadgil, Soniya
AU - Chi, Micki
N1 - Funding Information:
Acknowledgments. This project was supported by NSF award SBE-0354420 to the Pittsburgh Science of Learning Center. The authors of this paper would like to thank the PSLC Physics LearnLab for their help and support on this project.
Publisher Copyright:
© 2007 The authors and IOS Press. All rights reserved.
PY - 2007
Y1 - 2007
N2 - Learning by observation has long been a traditional method of learning. Recent work has pointed toward collaboratively observing tutoring as a promising new method for observational learning. Our current study tested this new method in the PSLC physics LearnLab where students were introduced two topics of rotational kinematics by observing videos while problem solving in Andes. The students were randomly assigned to a pair condition that collaboratively observed a video of an expert tutoring or providing an example, or to a solo condition that observed a video of an expert worked example. Several robust and normal learning measures were collected, however, to date only multiple choice measures have been analyzed. Students’ performance on the multiple choice questionnaires revealed significant pretest to posttest gains for all conditions. However, no differences have been found among conditions for normal learning measures.
AB - Learning by observation has long been a traditional method of learning. Recent work has pointed toward collaboratively observing tutoring as a promising new method for observational learning. Our current study tested this new method in the PSLC physics LearnLab where students were introduced two topics of rotational kinematics by observing videos while problem solving in Andes. The students were randomly assigned to a pair condition that collaboratively observed a video of an expert tutoring or providing an example, or to a solo condition that observed a video of an expert worked example. Several robust and normal learning measures were collected, however, to date only multiple choice measures have been analyzed. Students’ performance on the multiple choice questionnaires revealed significant pretest to posttest gains for all conditions. However, no differences have been found among conditions for normal learning measures.
UR - https://www.scopus.com/pages/publications/85006515006
UR - https://www.scopus.com/pages/publications/85006515006#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85006515006
T3 - Frontiers in Artificial Intelligence and Applications
SP - 554
EP - 556
BT - Artificial Intelligence in Education
A2 - Luckin, Rosemary
A2 - Koedinger, Kenneth R.
A2 - Greer, Jim
PB - IOS Press BV
T2 - 13th International Conference on Artificial Intelligence in Education, AIED 2007
Y2 - 9 July 2007 through 13 July 2007
ER -