Applying Attribute Selection Algorithms in Academic Performance Prediction

  • Mu Lin WongEmail author
  • S. Senthil
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


There is an essential need to identify effective and efficient attribute selection algorithms for developing predictive and descriptive classifiers to predict students’ academic performance. Personal, Academic and Socio-economic data were collected from 100 MCA students through questionnaire and cleansed. Eight attribute selection algorithms available in WEKA were applied to identify the best attributes. These attribute sets were then employed by four rule-based classifiers and four tree-based classifiers to examine the rate of accuracy, recall and AUC. The results proved that dimension reduction improves classifier performance by a maximum margin of 9% in accuracy, 25% in recall and 45% in AUC, when attributes were selected by subset-based algorithms. Both academic and socio-economic data are required to develop effective classifiers.


Educational data mining Academic performance prediction Attribute selection algorithm Rule-based classifiers Tree-based classifiers Historical data 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and ApplicationsREVA UniversityBengaluruIndia

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