Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a dataset from a recent educational study on how teachers evaluate teaching concerning ethical awareness. The Bayesian classification approach has been implemented for the personal computer under the Linux operating system. It presents an appealing addition to the standard toolbox for exploratory data analysis of educational data. (Contains 4 figures and 21 references.) (Author/SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** A paper presented at the 1997 Annual Meeting of the American Educational Research Association (Chicago, IL, USA, March 1997) Bayesian Finite Mixtures for Nonlinear Modeling of Educational data U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 67rhis document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. O Henry Tirri and Tomi Silander Complex Systems Computation Group (CoSCo)* P.O. Box 26, Department of Computer Science FIN-00014 University of Helsinki, Finland PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY Henry Tirri Kirsi Tirri P.O. Box 38,Department of Teacher Education TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) FIN-00014 University of Helsinki, Finland Abstract In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a data set from a recent educational study. The Bayesian classification approach described has been implemented, and presents an appealing addition to the standard toolbox for exploratory data analysis of educational data.In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a data set from a recent educational study. The Bayesian classification approach described has been implemented, and presents an appealing addition to the standard toolbox for exploratory data analysis of educational data.