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Cluster Computing

Erschienen in:

25.07.2017

Analyzing students’ performance using multi-criteria classification

verfasst von: Feras Al-Obeidat, Abdallah Tubaishat, Anna Dillon, Babar Shah

Erschienen in: Cluster Computing | Ausgabe 1/2018

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Abstract

Education is a key factor for achieving long-term economic progress. During the last decades, higher standards in education have become easier to attain due to the availability of knowledge and resources worldwide. With the emergence of new technology enhanced by using data mining it has become easier to dig into data and extract useful knowledge from data. In this research, we use data analytic techniques applied to real case studies to predict students’ performance using their past academic experience. We introduce a new hybrid classification technique which utilize decision tree and fuzzy multi-criteria classification. The technique is used to predict students’ performance based on several criteria such as age, school, address, family size, evaluation in previous grades, and activities. To check the accuracy of the model, our proposed method is compared with other well-known classifiers. This study on existing student data showed that this method is a promising classification tool.

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Titel: Analyzing students’ performance using multi-criteria classification
verfasst von: Feras Al-Obeidat
Abdallah Tubaishat
Anna Dillon
Babar Shah
Publikationsdatum: 25.07.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 1/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0967-4

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