Multi-Dimensional Analysis to Predict Students’ Grades in Higher Education


  • Eslam Abou Gamie Postgraduate student at BUE
  • Samir Abou El-Seoud British University in Egypt - BUE
  • Mostafa Salama CSI - BUE
  • Walid Hussein Assistance Professor



Data mining, education data mining, MOODLE, feature selection, correlation analysis, learning activities, pedagogical approaches, classification


This work enhances the analysis of the student performance in the high education level. This model categorizes the features according to their relativeness to the teaching style and to the student activities on an Electronic Learning system. Several new features are proposed and calculated in each of these two categories/dimensions. This approach applies an extra level of machine learning that analyses the data based on a set of dimensions, and each dimensions includes a set of features. The prediction analysis is applied on each dimension separately based on a different classifiers. The best fitting classifier to each dimension ensures the enhancement of the local analysis accuracy and though enhances overall global accuracy. The accuracy of prediction of the student is enhanced to 87%. This study allows the detection of the correlation the features in different dimension. Furthermore, a study is applied on the scanned text documents for extracting and utilizing the features that represent the student uploads.

Author Biography

Samir Abou El-Seoud, British University in Egypt - BUE

BUE, Informatics and Computer Science. Professor




How to Cite

Abou Gamie, E., Abou El-Seoud, S., Salama, M., & Hussein, W. (2019). Multi-Dimensional Analysis to Predict Students’ Grades in Higher Education. International Journal of Emerging Technologies in Learning (iJET), 14(02), pp. 4–15.