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Analyzing students’ performance using multi-criteria classification

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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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Authors and Affiliations

  1. Zayed University, Abu Dhabi, United Arab Emirates

    Feras Al-Obeidat, Abdallah Tubaishat, Anna Dillon & Babar Shah

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  1. Feras Al-Obeidat
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  2. Abdallah Tubaishat
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  3. Anna Dillon
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  4. Babar Shah
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Correspondence to Feras Al-Obeidat.

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Al-Obeidat, F., Tubaishat, A., Dillon, A. et al. Analyzing students’ performance using multi-criteria classification. Cluster Comput 21, 623–632 (2018). https://doi.org/10.1007/s10586-017-0967-4

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  • DOI: https://doi.org/10.1007/s10586-017-0967-4

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