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Predicting Academic Performance of Students Using a Hybrid Data Mining Approach.

Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student's information which can be used to invent valuable pattern pertaining learning behavior of students. The institutions of...

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Published in:Journal of Medical Systems Vol. 43; no. 6; pp. 1 - 16
Main Authors: Francis, Bindhia K., Babu, Suvanam Sasidhar
Format: research tables/charts Journal Article
Published: Springer Nature Jun2019
Online Access:View this record in EBSCOhost
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      dt: Jun2019
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1295-4
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          Francis, Bindhia K.
          Babu, Suvanam Sasidhar
        affil: Bharathiar University, Coimbatore, India
      sug:
        subj:
          Data Mining Methods
          Algorithms Utilization
          Student Performance Appraisal
          Human
          Educational Technology
          Decision Trees
          India
          Quality of Life
          Models, Educational
          Academic Failure
          Neural Networks (Computer)
          Academic Achievement
      ab: Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student's information which can be used to invent valuable pattern pertaining learning behavior of students. The institutions of education can utilize educational data mining to examine the performance of students which can support the institution in recognizing the student's performance. In data mining classification is a familiar technique that has been implemented widely to find the performance of students. In this study a new prediction algorithm for evaluating student's performance in academia has been developed based on both classification and clustering techniques and been ested on a real time basis with student dataset of various academic disciplines of higher educational institutions in Kerala, India. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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