Author

  • Jean Pierre Atanas

    • Advanced University Program, the Petroleum Institute, Umm Al Naar, Abu Dhabi, United Arab Emirates
    • United Arab Emirates

Abstract

Understanding the true reasons behind students’ failure, and bringing preventive measures to this issue at early stages are invaluable in the educational learning process. Preventing problems such as language deficiency or misclassification of the students in the appropriate academic levels is primordial for any educational institution. Many factors influence the learning process of the students, such as the demographic characteristics, educational background as well as language barrier. This work highlights the most preponderant factors affecting students’ advancement in the learning process and provides support to academic administrators. It uses some of state of the art classification and regression algorithms in the application domain of predicting students’ progress. Datasets were filtered and trained using predictive algorithms. It is shown that Science learning and English language skills are highly correlated. Datasets are not always suitable for data mining unless it is preprocessed and well adapted to the context being studied. A tool has been developed to preprocess the data provided that feeds into Weka Data Mining Software to profile students’ performance.

Keywords

References

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How to Cite

Atanas, J. P. (2012). Measuring Students’ Performance with Data Mining. Journal of Education and Vocational Research, 3(5), 132-137. https://doi.org/10.22610/jevr.v3i5.60

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Copyright (c) 2012 Jean Pierre Atanas (Author)

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Section

Research Paper

Published in

Vol. 3 No. 5 (2012) Learning Analytics, Competencies & Education Returns View issue contents

Published

2012-05-15

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