1 Steinbeis Hochschule Berlin (GERMANY)
2 Lucian Blaga University of Sibiu (ROMANIA)
3 Hochschule für Technik, Wirtschaft und Gestaltung Konstanz (GERMANY)
About this paper:
Appears in: INTED2009 Proceedings
Publication year: 2009
Pages: 3382-3389
ISBN: 978-84-612-7578-6
ISSN: 2340-1079
Conference name: 3rd International Technology, Education and Development Conference
Dates: 9-11 March, 2009
Location: Valencia, Spain
The main goal of the paperwork was to build a mathematical model, based upon fuzzy logic in order to assist the decision of including or eliminating a specialty discipline within an engineering sciences curricula.

At this time, the model will assist the humans to choose from an imposed portfolio of disciplines. Usually, this portfolio is imposed by the national authorities for higher education in each country. Of course, the number of disciplines within it is much higher as the number of disciplines which can be included in the curricula, so the universities have to choose between them, a process which is often extremely difficult.

The targeted engineering studies are at this time machine building domain, with a focus upon machine tools specialization, but the process should be the same for other specializations.
A list of competences which a graduate student should posses after studying the disciplines form above-mentioned portfolio will be build. The competences will be divided into equipments related, technological processes related, driving systems related, automations systems related, tools related and CAD/CAM/CAE related.

The list of competences will be distributed to industrial companies in order to be assessed. The management of the companies, which are the main employer of the graduates will be asked to grade every competence from the list with grades between 1 and 10.

Using this list and the grades, fuzzy models will be build for each discipline within the portfolio. The inputs of each fuzzy model will be a chosen set of competences from the list (different for each discipline) and the output will be the degree of inclusion in the curricula.

The fuzzyfication of the inputs will be made according to a chosen set of membership functions and to the grades received by each competence. The chose of a proper set of membership functions will be one the major contributions of this research.

As an example, such a membership function will use as linguistic variables the words “useless”, “necessary” and “mandatory”. Another membership function, which will be used for a secondary competence will use the linguistic variables the words “inefficient” and “efficient”.

Finally, the models will assist the decision of including the discipline in the curricula by allowing the user to calculate the degree of inclusion, in percents.
curricula, decision, fuzzy logic, inclusion, specialty disciplines.