Improving the Perception of Chemistry in Higher Education Programs through Many-Valued Empirical Machines

Abstract

The inclusion of the chemistry field of study in higher education science and technology curricula aims to develop professionals who are able to analyze and solve multidisciplinary problems in a sustainable and correct way. Attending students to assess the role of chemistry in their education is critical to increasing success and improving their future professional practice. This article presents a Many-Valued Empirical Machine designed to capture Students' Perception of Chemistry in Higher Education Programs. The applied problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that articulate with an Artificial Neural Network approach to computing, being grounded on a view to knowledge representation and argumentation that considers not only the data entropic states but also its inherent Predicative Vagueness.