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Analyzing and Predicting Learning Levels of Students in Higher Education using Machine Learning Approach

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  • Qamar Rayees Khan
  • Parvez Abdulla
  • Majid Bashir Malik

Abstract

The genesis of the emerging field that lead to the growth of the analytical observations of the educational data and draw inferences based on the type and pattern of the data is Education Data Mining (EDM). This field has added the power of the decision making in education settings. The role of EDM solves many problems facing the educational institutions by generating the patterns from the data which affect the overall objective of the educational institutions. Various data mining techniques have already been used by the researchers to evaluate the impact of drop out ratio of the institutions using EDM. This paper shall explore the current field of study and identify the parameters that affect the Learning Levels of Students in Higher Education using a Machine Learning Approach. This paper emphasis on the prediction of learning levels of the students so that the institution may evolve a mechanism to bridge the gap for slow learners to perform as per their expectations. The dataset used in this paper is collected from the university students and the weka tool which is an open source tool is used for the experimental analysis. At the end, the model is evaluated using various performance evaluation parameters.

Suggested Citation

  • Qamar Rayees Khan & Parvez Abdulla & Majid Bashir Malik, 2017. "Analyzing and Predicting Learning Levels of Students in Higher Education using Machine Learning Approach," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(3), pages 962-966, July.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i3:id:hcseit1835289
    Note: Article URL: https://ijsrcseit.com/CSEIT1835289
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