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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com