Machine Learning Department
School of Computer Science, Carnegie Mellon University
Applying Machine Learning to Cognitive Modeling
for Cognitive Tutors
Noboru Matsuda*, William W. Cohen,
Jonathan Sewall*, Kenneth R. Koedinger*
Keywords: Programming by demonstration, inductive logic
programming, cognitive modeing, cognitive tutor, authoring
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
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.
*Human-Computer Interaction Institute, Carnegie Mellon University