Student Knowledge Diagnosis Using Item Response Theory and Constraint-Based Modeling

One of the most popular student modeling techniques currently available is Constraint Based Modeling (CBM), which is based on Ohlsson's theory of learning from performance errors. It focuses on the domain principles to correct faulty knowledge and assumes that a student will reach a correct solution without violating these fundamental domain concepts. However, even though this is a powerful and computationally simple technique, most student models of CBM-based tutors handle simple long-term models or based on heuristics to quantitatively estimate the knowledge measured. In this paper we propose a student knowledge diagnosis model which combines CBM with the Item Response Theory (IRT). IRT is a probabilistic and data-driven theory which guarantees accurate and invariant student knowledge estimations. By means of this synergy between CBM and IRT we suggest the construction of long-term student models composed of the estimations of their knowledge. This paper also includes an experiment we have carried out with real students, which explores the validity of the diagnoses made with our model.

[1]  Antonija Mitrovic,et al.  Intelligent Tutors for All: The Constraint-Based Approach , 2007, IEEE Intelligent Systems.

[2]  Antonija Mitrovic,et al.  A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling , 2003, User Modeling.

[3]  Antonija Mitrovic,et al.  Constraint-based knowledge representation for individualized instruction , 2006, Comput. Sci. Inf. Syst..

[4]  Eduardo Guzmán,et al.  Improving Student Performance Using Self-Assessment Tests , 2007, IEEE Intelligent Systems.

[5]  José-Luis Pérez-de-la-Cruz,et al.  TAPLI: An Adaptive Web-Based Learning Environment for Linear Programming , 2003, CAEPIA.

[6]  R. Hambleton,et al.  Fundamentals of Item Response Theory , 1991 .

[7]  Stellan Ohlsson,et al.  Constraint-Based Student Modeling , 1994 .

[8]  Wim Jansen,et al.  Multilog: Multiple, Categorical Item Analysis and Test Scoring Using Item Response Theory , 1994 .

[9]  Cornelis A.W. Glas,et al.  Computerized adaptive testing : theory and practice , 2000 .

[10]  L. L. Thurstone,et al.  A method of scaling psychological and educational tests. , 1925 .

[11]  Antonija Mitrovic,et al.  Optimising ITS Behaviour with Bayesian Networks and Decision Theory , 2001 .

[12]  Claude Frasson,et al.  Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education , 1990 .

[13]  Antonija Mitrovic,et al.  Evaluating Adaptive Problem Selection , 2004, AH.

[14]  G. Dantzig On the Non-Existence of Tests of "Student's" Hypothesis Having Power Functions Independent of $\sigma$ , 1940 .

[15]  Eduardo Guzmán,et al.  A SOA-Based Framework for Constructing Problem Solving Environments , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[16]  Ricardo Conejo,et al.  Adaptive testing for hierarchical student models , 2007, User Modeling and User-Adapted Interaction.