Applying Machine Learning to Cognitive Modeling for
Cognitive Tutors
Noboru Matsuda, William W. Cohen, Jonathan Sewall, and
Kenneth R. Koedinger (2006). Applying Machine Learning to Cognitive Modeling
for Cognitive Tutors, Technical report CMU-ML-06-105, School of Computer
Science, Carnegie Mellon University.
Abstract: The aim of this study is to build an
intelligent authoring environment for Cognitive Tutors in which the author
need not manually write a cognitive model. Writing a cognitive model usually
requires days of programming and testing even for a well-trained cognitive
scientist. To achieve our goal, we have built a machine learning agent –
called a Simulated Student – that automatically generates a cognitive model
from sample solutions demonstrated by the human domain expert (i.e., the
author). This paper studies the effectiveness and generality of the
Simulated Student. The major findings include (1) that the order of training
problems does not affect a quality of the cognitive model at the end of the
training session, (2) that ambiguities in the interpretation of
demonstrations might hinder machine learning, and (3) that more detailed
demonstration can both avoid difficulties with ambiguity and prevent search
complexity from growing to impractical levels.
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