The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade Surprisingly, models currently in use do not allow for individual learning rates nor individualized estimates of student initial knowledge Corbett and Anderson, in their original articles, were interested in trying to add individualization to their model which they accomplished but with mixed results Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously, in a single step With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter We explore three difference strategies for setting the initial individualized knowledge parameters and report that the best strategy is one in which information from multiple skills is used to inform each student's prior Using this strategy we achieved lower prediction error in 33 of the 42 problem sets evaluated The implication of this work is the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill Adaptation of instruction based on individualized knowledge and learning speed is discussed as well as open research questions facing those that wish to exploit student and skill information in their user models.
[1]
Albert T. Corbett,et al.
A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems
,
2006,
Intelligent Tutoring Systems.
[2]
John R. Anderson,et al.
Knowledge tracing: Modeling the acquisition of procedural knowledge
,
2005,
User Modeling and User-Adapted Interaction.
[3]
Kenneth R. Koedinger,et al.
Performance Factors Analysis - A New Alternative to Knowledge Tracing
,
2009,
AIED.
[4]
R. Atkinson,et al.
An Approach to the Psychology of Instruction.
,
1972
.
[5]
Vincent Aleven,et al.
More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing
,
2008,
Intelligent Tutoring Systems.
[6]
S. Chipman,et al.
Cognitively diagnostic assessment
,
1995
.
[7]
R. Charles Murray,et al.
Reducing the Knowledge Tracing Space
,
2009,
EDM.
[8]
Jim Reye,et al.
Student Modelling Based on Belief Networks
,
2004,
Int. J. Artif. Intell. Educ..
[9]
Cristina Conati,et al.
User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 25-29, 2007, Proceedings
,
2007,
User Modeling.
[10]
Joseph E. Beck,et al.
Identifiability: A Fundamental Problem of Student Modeling
,
2007,
User Modeling.
[11]
Albert T. Corbett,et al.
Student Modeling in the ACT Programming Tutor: Adjusting a Procedural Learning Model With Declarative Knowledge
,
1997
.