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

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 cri...

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Bibliographic Details
Main Author: Vicente, Henrique (author)
Other Authors: Figueiredo, Margarida (author), Dias, Almeida (author), Marques, José (author), Araújo, Isabel (author), Maia, Nuno (author), Ribeiro, Jorge (author), Neves, José (author)
Format: article
Language:eng
Published: 2019
Subjects:
Chemistry
Higher Education
Entropy
Predicative Vagueness
Knowledge Representation and Reasoning
Artificial Neural Networks
Many-Valued Empirical and Logical Machines
Online Access:http://hdl.handle.net/10174/24878
Country:Portugal
Oai:oai:dspace.uevora.pt:10174/24878
Description
Summary: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.

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