Ordinal Regression by a Gravitational Model in the field of Educational Data Mining

Research areas:

• Methodology - Machine learning

Year:

2016

Type of Publication:

Article

Authors:

• Gómez-Rey, Pilar
• Fernandez-Navarro, Francisco
• Barbera-Gregori, Elena

Journal:

Expert Systems

Volume:

33

Number:

2

Pages:

161-175

BibTex:

@article{Pilar Gómez-Rey2015, author = "Pilar G{\'o}mez-Rey and Francisco Fernandez-Navarro and Elena Barbera-Gregori", 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{\&}#039; 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.", awards = "JCR(2015): 0.947 Position: 57/105 (Q3) Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", comments = "JCR(2015): 0.947 Position: 57/105 (Q3) Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", doi = "10.1111/exsy.12138", journal = "Expert Systems", note = "JCR(2015): 0.947 Position: 57/105 (Q3) Category: COMPUTER SCIENCE, THEORY {\&}amp; METHODS", number = "2", pages = "161-175", title = "{O}rdinal {R}egression by a {G}ravitational {M}odel in the field of {E}ducational {D}ata {M}ining", url = "http://doi.org/10.1111/exsy.12138", volume = "33", year = "2016", }

Note:

JCR(2015): 0.947 Position: 57/105 (Q3) Category: COMPUTER SCIENCE, THEORY & METHODS

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.

Comments:

JCR(2015): 0.947 Position: 57/105 (Q3) Category: COMPUTER SCIENCE, THEORY & METHODS