Abstract:

This paper describes the application of Problem-Based Learning (PBL) in the subject "Industrial Chemical Technology" of the Master's Degree in Industrial Engineering from the Universitat Politécnica de València (UPV). This subject includes contents related to the Basic Operations of Chemical Engineering, not previously studied by most students. This fact, together with the difficulties associated with the management of 300 students have conditioned the type of methodologies that can be applied and, therefore, the level of depth at which the contents can be developed. Therefore, the main methodology applied so far has been the participative masterclass, with problem solving in the classroom. Although the results in terms of percentage of students who pass the subject are very satisfactory, the approach to learning is superficial in some aspects and teachers believe that it should be adapted to master's level. To achieve deeper learning of students in the contents of the subject, it has been decided to partially modify the methodology of the subject, integrating Problem-Based Learning (ABP). This new methodology will consist of grouped resolution of problems integrated into the same real industrial process, which will allow students to also acquire a more global view of the contents covered in the subject. The level of the problems will be increased compared to those solved in the classroom and students will be required to search, select and justify additional data and information to solve the problems that will also be multidisciplinary.

All this innovation is part of an Innovation and Educational Improvement Project granted and funded by the “Vicerrectorado de Estudios, Calidad y Acreditación (VECA)” of the UPV. This work describes how the PBL has been defined for implementation in the subject and analyses the results achieved to date, identifying the aspects to be improved for the continuation of the methodology in the following academic years.

Keywords:

Problem-Based Learning, Chemical Technology, Industrial Engineering, deep learning.