An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree

Improving student’s academic performance is not an easy task for the academic community of higher learning. The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average (GPA) in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment lab -work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. In this paper, we present a hybrid procedure based on Decision Tree of Data mining method and Data Clustering that enables academicians to predict student’s GPA and based on that instructor can take necessary step to improve student academic performance

@article{Shovon2012,
  title     = {An Approach of Improving Student’s Academic Performance by using K-means clustering algorithm and Decision tree},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {8},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Hedayetul Islam Shovon and Mahfuza Haque},
  doi       = {10.14569/IJACSA.2012.030824},
  url       = {https://doi.org/10.14569/IJACSA.2012.030824}
}