International Journal of Artificial Intelligence in Education (2003), 13, to appear.
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Coaching Web-based Collaborative Learning based on Problem Solution Differences and Participation

MARIA DE LOS ANGELES CONSTANTINO-GONZALEZ
Dept. of Computer Sciences
ITESM Campus Laguna
Paseo del Tecnol—gico #751,
Col. Ampliaci—n la Rosita
Torre—n, Coah. 27250, M | xico
Voice: 52.871.729.6363
Fax: 52.871.729.6317
email: aconstan@campus.lag.itesm.mx

DANIEL D. SUTHERS
Dept. of Information and Computer Sciences
University of Hawai`i at Manoa
1680 East West Road, POST 309
Honolulu, HI 96822, USA
Voice: 1.808.956.3890
Fax: 1.808.956.3548
email: suthers@hawaii.edu
http://lilt.ics.hawaii.edu/

JOSE G. ESCAMILLA DE LOS SANTOS
Educational Technology and Information Department
ITESM - Virtual University
Av. Eugenio Garza Sada 2501 sur,
Edificio Cedes Semis—tano 1,
64849 Monterrey N.L., M | xico
Voice: 52.81.83.28.44.66
Fax: 52.81.83.28.40.55
email: jescamil@campus.ruv.itesm.mx




Abstract: This paper describes the design and evaluation of a coach that helps students collaborate while solving Entity Relationship modeling problems in a computer-mediated learning environment (COLER). Unlike previous work generally emphasizing dialogue analysis or expert models, this work evaluates a new approach to supporting collaboration that identifies learning opportunities based on differences between problem solutions and tracking levels of participation. The contribution made by these and other knowledge sources in the generation of collaboration advice was evaluated by comparing expert rankings of advice to the software coach's rankings, and by identifying the advice that would be lost if each respective knowledge source were removed. Results show that good quality advice can be obtained through these knowledge sources, although other knowledge sources may fill in gaps relative to the expert's performance. This work demonstrates how intelligent agents can produce reasonable collaboration advice in domains for which structured problem solutions exist by using a few basic knowledge sources, and illustrates several methods of evaluating the knowledge and reasoning of complex knowledge-based systems.
 
 
 
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