About this paper

Appears in:
Pages: 3909-3916
Publication year: 2010
ISBN: 978-84-613-5538-9
ISSN: 2340-1079

Conference name: 4th International Technology, Education and Development Conference
Dates: 8-10 March, 2010
Location: Valencia, Spain

CLASSIFYING OF TEACHING-LEARNING RESULTS FROM UNIVERSITY STUDENTS SURVEYS

J.M. Brotons Martinez, A. Rabasa Dolado, J.J. Rodríguez Sala

Miguel Hernández University (SPAIN)
Fuzzy mathematics was introduced by Zadeh in 1965, and the main difference to classical logic is that precise reasoning is only an extreme case of approximate reasoning, and everything is a question of degree. A proposal is no longer true or false, but it can have several intermediate levels: slightly true, quite, very, etc. Furthermore, when alternatives are set out, it is not possible to choose between one and another and each one has to be valued with a level (for instance, quite correct, very correct, etc) and it can be concluded that one proposal is acceptable with its corresponding level.

In this way, several models have been built using fuzzy preferences (Baet & Fodor, 1997; Fodor & Roubens, 1994; García-Lapresta, 2005; Nurmi, 1981; and Tanino, 1984). On the other hand, to capture the lack of precision in human behavior suitably, linguistic preferences are more adequate (Herrera, Herrera-Viedma, & Martínez, 2001; and Zadeh, 1975, 2001).

In short, the main objectives of the present paper are:
•The valuation of a satisfaction degree of a subject through a satisfaction survey of different items, in which a graduation in the responses is considered.
•Next, we must form homogeneous groups of students that present similar degree of satisfaction. For that purpose, we will use the transitive closure algorithm and the inverse transitive algorithm.
•And finally, other algorithm will allow us to put in order the previous homogeneous groups

We are aware of the fact that some steps of the present model are highly elaborate, but some computer programs are very useful for simplifying their operational activity. Anyway, the central element of our model is the correct valuation of the characteristics of the subject for which we will use a survey.

In order to simplify that complex computational activity we propose the usage of Data Mining methods such as Clustering and Classification Rule Sets. Both of them has been successfully applied in previous e-learning works, i.e. Khribi, Jemni & Nasraoui, 2008; Cocea & Weibelzahl, 2009 and Romero, Ventura, Zafra & Bra, 2009.
@InProceedings{BROTONSMARTINEZ2010CLA,
author = {Brotons Martinez, J.M. and Rabasa Dolado, A. and Rodr{\'{i}}guez Sala, J.J.},
title = {CLASSIFYING OF TEACHING-LEARNING RESULTS FROM UNIVERSITY STUDENTS SURVEYS},
series = {4th International Technology, Education and Development Conference},
booktitle = {INTED2010 Proceedings},
isbn = {978-84-613-5538-9},
issn = {2340-1079},
publisher = {IATED},
location = {Valencia, Spain},
month = {8-10 March, 2010},
year = {2010},
pages = {3909-3916}}
TY - CONF
AU - J.M. Brotons Martinez AU - A. Rabasa Dolado AU - J.J. Rodríguez Sala
TI - CLASSIFYING OF TEACHING-LEARNING RESULTS FROM UNIVERSITY STUDENTS SURVEYS
SN - 978-84-613-5538-9/2340-1079
PY - 2010
Y1 - 8-10 March, 2010
CI - Valencia, Spain
JO - 4th International Technology, Education and Development Conference
JA - INTED2010 Proceedings
SP - 3909
EP - 3916
ER -
J.M. Brotons Martinez, A. Rabasa Dolado, J.J. Rodríguez Sala (2010) CLASSIFYING OF TEACHING-LEARNING RESULTS FROM UNIVERSITY STUDENTS SURVEYS, INTED2010 Proceedings, pp. 3909-3916.
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