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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