Student Satisfaction and Quality of Education in HEIs: Complex Comparative Analysis with the Use of Bayesian Networks


This project proposes the creation of based on Bayesian networks methodology  for monitoring subjective and normative-institutional student learning quality indicators (satisfaction with training and key performance indicators) from the perspective of an integrated approach. Bayesian network is estimated using a learning algorithm that can analyze the structure of probabilistic dependency of ordinal and nominal variables. Different scenarios are evaluated through effective computational Bayesian networks algorithms. The project involves the construction of Bayesian networks for Russian IHEs of different types and models of the comparative analysis in order to determine optimal strategies for change in student satisfaction levels and highlight the critical factors increase the efficiency and effectiveness of training.