Comparative Study of Machine Learning Algorithms to Measure the Students’ Performance

Ganesan Kavitha

Abstract


Students’ performance in the continuous assessments needs to be monitored to identify the students who may not perform well in the final examination. The aim of the research is to predict the students at risk those who will not complete the course. In order to predict the students at risk, Machine Learning algorithms can be applied to the students’ data at hand to construct a model from the training data set. With the prediction model, testing data can be applied to identify the students at risk. In this paper, two Machine Learning Algorithms namely C4.5, and Naïve Bayes Algorithm are used to analyze the training data set to build the prediction models and tested on the testing data set. The accuracy level of the two algorithms were also computed and compared to identify the algorithm which yields results at higher accuracy.


Keywords


Machine Learning Algorithms; C4.5; Naïve Bayes Algorithm; Prediction Model.

Full Text:

PDF

References


Ganesan Kavitha & Dr. Lawrance Raj, Educational Data Mining and Learning Analytics – Educational Assistance for Teaching and Learning, International Journal of Computer & Organization Trends (IJCOR), Volume 41 Number 1, 2017.

Manohar Swamynathan, Mastering Machine Learning with Python in Six Steps – A Practical Implementation Guide to Predictive Dtat Analytics Using Python, India: Apress, 2017.

John Paul Mueller & Luca Massaron, Machine Learning for Dummies, Wiley, 2016.

Shai Shalev, Shwartz & Shai Ben David, Understanding Maching Learning: From Theory to Algorithms, Cambridge University Press, 2014.

Xindong et al., Top 10 Algorithms in Data Mining, IEEE Conference on Data Mining, 2006.

Qasem Al Radaideh, Emad Al Shawakfa & Mustafa Al Najjar, Mining Student Data Using Decision Trees, in the Proceedings of International Arab Conference on Information Technology, 2006.

Brijesh Kumar Baradwaj & Saurabh Pal, Mining Educational Data to Analyze Students’ Perofrmance, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.

Badr Hssina, Abdelkarim Merbouha, Hanane Ezzikouri & Mohammed Erritali, A Comparative Study of Decision Tree ID3 and C4.5, International Journal of Advanced Computer Science and Applications, Special Issue on Advances in Vehicular Ad Hoc Networking and Applications, 2014.

Barry de Ville, Decision Trees for Business Intelligence and Data Mining – Using SAS Enterprise Miner, SAS Press, 2006.

Saed Sayad, Real Time Data Mining, University of Totonto, 2016.


Refbacks

  • There are currently no refbacks.


 

 
  

 

  


About IJC | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJC is published by (GSSRR).