Analyzing students’ performance using multi-criteria classification
Source of Publication
© Springer Science+Business Media New York 2018. 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.
Al-Obeidat, Feras; Tubaishat, Abdallah; Dillon, Anna; and Shah, Babar, "Analyzing students’ performance using multi-criteria classification" (2017). Scopus Indexed Articles. 1330.