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Some useful tactics to modify, map and mine data from intelligent tutors

Published online by Cambridge University Press:  22 May 2006

JACK MOSTOW
Affiliation:
Project LISTEN, School of Computer Science, Carnegie Mellon University, RI-NSH 4213, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA e-mail: mostow@cs.cmu.edu, joseph.beck@gmail.com
JOSEPH BECK
Affiliation:
Project LISTEN, School of Computer Science, Carnegie Mellon University, RI-NSH 4213, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA e-mail: mostow@cs.cmu.edu, joseph.beck@gmail.com

Abstract

Mining data logged by intelligent tutoring systems has the potential to discover information of value to students, teachers, authors, developers, researchers, and the tutors themselves – information that could make education dramatically more efficient, effective, and responsive to individual needs. We factor this discovery process into tactics to modify tutors, map heterogeneous event streams into tabular data sets, and mine them. This model and the tactics identified mark out a roadmap for the emerging area of tutorial data mining, and may provide a useful vocabulary and framework for characterizing past, current, and future work in this area. We illustrate this framework using experiments that tested interventions by an automated reading tutor to help children decode words and comprehend stories.

Type
Papers
Copyright
2006 Cambridge University Press

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