A FRAMEWORK FOR STUDENTS’ ACADEMIC PERFORMANCE ANALYSIS USING NAÃVE BAYES CLASSIFIER
DOI:
https://doi.org/10.11113/jt.v75.5037Keywords:
Higher learning institution, data mining, educational data mining, classification, Naïve Bayes Classifier, prediction, students’ academic performanceAbstract
Educational database of Higher Learning Institutions holds an enormous amount of data that increases every semester. Data mining technique is usually applied to this database to discover underlying information about the students. This paper proposed a framework to predict the performance of first year bachelor students in Computer Science course. Naïve Bayes Classifier was used to extract patterns using WEKA as a Data mining tool in order to build a prediction model. The data were collected from 6 year period intakes from July 2006/2007 until July 2011/2012. From the students’ data, six parameters were selected that are race, gender, family income, university entry mode, and Grade Point Average. By using Naïve Bayes Classifier, it would predict the class label “Grade Point Average†as a categorical value; Poor, Average, and Good. Result from the study shows that the students’ family income, gender, and hometown parameter contribute towards students’ academic performance. The prediction model is useful to the lecturers and management of the faculty in identifying students with weak performance so that they will be able to take necessary actions to improve the students’ academic performance.
References
Zhang, Y., S. Oussena, T. Clark, and H. Kim. 2010. Using Data Mining To Improve Student Retention In Higher Education-A Case Study. In 12th International Conference on Enterprise Information Systems (ICEIS). Portugal. June 8-12, 2010.
Kumar, S. A. and M. N. Vijayalakshmi. 2012. Mining of Student Academic Evaluation Records in Higher Education. In 2012 International Conference on Recent Advances in Computing and Software Systems. Chennai, India. April 25-27, 2012. 67-70.
Lye, C. T., L. N. Ng, M. D. Hassan, W. W. Goh, C. Y. Law, and N. Ismail. 2010. Predicting Pre-university Student’s Mathematics Achievement. In Procedia of Social and Behavioral Sciences. 8: 299-306.
Yadav, S. K., B. Bharadwaj, and S. Pal. 2012. Data Mining Applications : A Comparative Study for Predicting Student’s performance. International Journal Of Innovative Technology & Creative Engineering. 1(12): 13-19.
Kumar, S. P. and K. S. Ramaswami. 2010. Fuzzy K- Means Cluster Validation for Institutional Quality Assessment. In International Conference Communication and Computational Intelligence (INCOCCI).Erode. December 27-29, 2010. 628-635.
Sachin, R. B. and M. S. Vijay. 2012. A Survey and Future Vision of Data Mining in Educational Field. In 2nd International Conference on Advanced Computing & Communication Technologies. Rohtak, Haryana. January 7-8, 2012. 96-100.
Kumar, U. and P. S. Pal. 2011. Data Mining : A Prediction of Performer or Underperformer Using Classification. International Journal of Computer Science and Information Technologies (IJCSIT). 2(2): 686-690.
Tair, M. M. A. and A. M. El-halees. 2012. Mining Educational Data to Improve Students’ Performance : A Case Study. International Journal of Information and Communication Technology Research. 2(2): 140-146.
Parack, S., Z. Zahid, and F. Merchant. 2012. Application of Data Mining in Educational Databases for Predicting Academic Trends and Patterns. In IEEE International Conference on Technology Enhanced Education (ICTEE). Kerala. January 3-5, 2012. 1-4.
GarcÃa, E. P. I. and P. M. Mora. 2011. Model Prediction of Academic Performance for First Year Students. In 10th Mexican International Conference on Artificial Intelligence. Puebla. November 26-December 4, 2011 169-174.
Bhardwaj, B. K. and S. Pal. 2011. Data Mining : A prediction for Performance Improvement Using Classification. International Journal of Computer Science and Information Security (IJCSIS). 9(4): 136-140.
Sharma, M. 2011. Development of Predictive Model in Education System : Using Naïve Bayes Classifier. In Proceedings of the International Conference and Workshop on Emerging Trends in Technology (ICWET 2011). Mumbai, India. Febuary 25-26, 2011. 185-186.
Bhuvaneswari, R. and K. Kalaiselvi. 2012. Naive Bayesian Classification Approach in Healthcare Applications. International Journal of Computer Science and Telecommunications. 3(1): 106-112.
Balaniuk, R., P. Bessiere, E. Mazer, and P. Cobbe. 2012. Risk based Government Audit Planning using Naïve Bayes Classifiers. In Grana M. et al. (Eds.). Advances in Knowledge-Based and Intelligent Information and Engineering Systems. 1313-1323.
Aziz, A. A., N. H. Ismail, and F. Ahmad. 2013. Mining Students’ Academic Performance. Journal of Theoretical and Applied Information Technology. 53(3): 485-495.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
