Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra [electronic resource] / Ryan S. J. d. Baker, Sujith M. Gowda and Michael Wixon.

In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of st...

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Bibliographic Details

Online Access:	Full Text (via ERIC)
Main Authors:	Baker, Ryan S., Gowda, Sujith M. (Author), Wixon, Michael (Author), Kalka, Jessica (Author), Wagner, Angela Z. (Author), Salvi, Aatish (Author), Aleven, Vincent (Author), Kusbit, Gail W. (Author), Ocumpaugh, Jaclyn (Author), Rossi, Lisa (Author)
Corporate Author:	International Educational Data Mining Society
Format:	Electronic eBook
Language:	English
Published:	[S.l.] : Distributed by ERIC Clearinghouse, 2012.
Subjects:	Academic Achievement. Algebra. Tutors. Computer Software. Prediction. Probability. Evaluation. Mathematics. Data. Tutoring.