10.1184/R1/6469829.v1
Noboru Matsuda
Noboru
Matsuda
William W. Cohen
William W.
Cohen
Jonathan Sewall
Jonathan
Sewall
Kenneth R Koedinger
Kenneth R
Koedinger
Applying Machine Learning to Cognitive Modeling for Cognitive Tutors
Carnegie Mellon University
2006
Programming by Demonstration
Inductive Logic Programming
Cognitive Modeling
Cognitive Tutor
Authoring
2006-07-01 00:00:00
Journal contribution
https://kilthub.cmu.edu/articles/journal_contribution/Applying_Machine_Learning_to_Cognitive_Modeling_for_Cognitive_Tutors/6469829
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.