A Bayesian hierarchical model for comparative evaluation of teaching quality indicators in higher education. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Fouskakis, D.; Petrakos, G.; Vavouras, I.
• Is Part Of: Journal of applied statistics. Volume 43:Issue 1(2016)
• Page Start: 195
• Page End: 211
• Publication Date: 2016-01-02
• Subjects: Bayesian beta regression -- Bayesian hierarchical models -- Dirichlet random coefficientmodel -- MCMC -- partial least-squares path modeling -- teaching quality evaluation; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2015.1054793
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : The problem motivating the paper is the quantification of students' preferences regarding teaching/coursework quality, under certain numerical restrictions, in order to build a model for identifying, assessing and monitoring the major components of the overall teaching quality. We propose a Bayesian hierarchical beta regression model, with a Dirichlet prior on the model coefficients. The coefficients of the model can then be interpreted as weights and thus they measure the relative importance that students give to the different attributes. This approach not only allows for the incorporation of informative prior when it is available but also provides user-friendly interfaces and direct probability interpretations for all quantities. Furthermore, it is a natural way to implement the usual constraints for the model coefficients. This model is applied to data collected in 2009 and 2013 from undergraduate students in the Panteion University, Athens, Greece and besides the construction of an instrument for the assessment and monitoring of teaching quality, it gave some input for a preliminary discussion on the association of the differences in students' preferences between the two time-periods with the current Greek socioeconomic transformation. Results from the proposed approach are compared with the ones obtained by two alternative statistical techniques.