Título Ordinal regression by a gravitational model in the field of educational data mining
Publicación externa No
Medio Expert Syst
Alcance Article
Naturaleza Científica
Cuartil JCR 3
Cuartil SJR 3
Impacto JCR 1.18000
Impacto SJR 0.30900
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951964988&doi=10.1111%2fexsy.12138&partnerID=40&md5=fc38535dc93f608dd332eafa7c6ed243
Fecha de publicacion 01/04/2016
ISI 000373929300003
Scopus Id 2-s2.0-84951964988
DOI 10.1111/exsy.12138
Abstract Educational data mining (EDM) is a research area where the goal is to develop data mining methods to examine data critically from educational environments. Traditionally, EDM has addressed the following problems: clustering, classification, regression, anomaly detection and association rule mining. In this paper, the ordinal regression (OR) paradigm, is introduced in the field of EDM. The goal of OR problems is the classification of items in an ordinal scale. For instance, the prediction of students\' performance in categories (where the different grades could be ordered according to A > B > C > D) is a classical example of an OR problem. The EDM community has not yet explored this paradigm (despite the importance of these problems in the field of EDM). Furthermore, an amenable and interpretable OR model based on the concept of gravitation is proposed. The model is an extension of a recently proposed gravitational model that tackles imbalanced nominal classification problems. The model is carefully adapted to the ordinal scenario and validated with four EDM datasets. The results obtained were compared with state-of-the-art OR algorithms and nominal classification ones. The proposed models can be used to better understand the learning-teaching process in higher education environments.
Palabras clave educational data mining; ordinal regression models; students satisfaction; gravitational models
Miembros de la Universidad Loyola

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