This study aims to identify students who are vulnerable of not being able to pass the Cisco certification examination. The main goal is to develop a model that will determine the significant attributes that influence students’ success in Cisco certification examination. The significant attributes were determined using logistic regression. The researcher conducted preliminary interviews in selected Cisco academies to determine prevailing issues. The study used sets of classification algorithms to generate models that were used for prediction. The main function of the model is to predict the probability of the examinee to pass a Cisco certification examination. The researcher used data mining tools such as WEKA and SPSS to derive the required models. Various data mining classification algorithms were used to identify the most accurate technique best suited for the given data set. The result of the experiment showed that the Logistic Regression algorithm is the most accurate algorithm to be used in the development of the predictive model.
data mining, classification algorithm, logistic regression, predictive model, students’ performance
Alaa el-Halees. (2009), “Mining students data to analyze e-Learning behavior: A Case Study”, 2009.
Baradwaj, B. K., & Pal, S. (2012). “Data Mining: A prediction for performance improvement using classification”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 9, No. 4, pp. 136-140, 2011
Baradwaj, B.K. and Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6
Brijesh, K.B. & Saurabh, P. (2011). Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl., 2(6): 63-69
Cisco Systems (2012). Retrieved May 1, 2015, from https://www.netacad.com/about-networking-academy/
Friedman, Jerome (2009).”The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Retrieved 2012-08-07
Garc´ıa-Saiz, D. and M. Zorrilla, 2011. Comparing classification methods for predicting distance students’ performance. Proceeding of the 2nd Workshop on Applications of Pattern Analysis Workshop and Conference, pp: 26-32
Gorikhan (2016). A study on Implementation of classification techniques to predict students’ results for Institutional Analysis
Kabakchieva, D. Predicting Student Performance by Using Data Mining Methods for Classification. Cybernetics and Information Technologies, 13(1):61–72, 2013
Kabra R. R., Bichkar R.S. (2011). Performance Prediction of Engineering Students using Decision Trees, International Journal of Computer Applications (0975- 8887), Vol-36-No.11
Kumar, A. and Vijayalakshmi M. (2011). EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT’S ACADEMIC PERFORMANCES.
Kumar A.D. et al (2014),A Survey on Predicting Student Performance. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5), 6147-6149
Kumar, A. and Priya, K. ( 2013). Improving the Student’s Performance Using Educational Data Mining. Int. J. Advanced Networking Applications. Volume: 04 Issue: 04, Pages 1680-1685 (2013), ISSN : 0975-0290
Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students’ Performance: A Case Study, 2012
Nguyen N., Paul J., and Peter H. (2007). A Comparative Analysis of Techniques for Predicting Academic Performance. In Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference. Pp. 7-12
Pandey, U. K. and Pal, S. (2011) “Data Mining: A prediction of performer or underperformer using classification”, (IJCSIT) International Journal of Computer Science and Information Technology, Vol. 2(2), pp.686-690, ISSN:0975-9646
Pandey, U. K. and Pal, S. “A Data mining view on class room teaching language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN: 1694-0814, 2011
Pandey, U. K., & Pal, S. (2011). Data mining: A prediction of performance or underperformer using classification. International Journal of Computer Science and information technology, 2(2), pp. 686- 690
Pittman, K. Comparison of data mining techniques used to predict student retention. PhD thesis, Nova Southeastern University, 2008
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2010). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education. Doi: 10.1002/cae.20456
Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2008, June). Data Mining Algorithms to Classify Students. In EDM (pp. 8-17)
Ruby, J. & David, K. (2014). Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms Algorithms – A Case Study. International Journal for research in Applied Science & Engineering Technology (IJRASET). Vol. 2 Issue XI, ISSN: 2321-9653
Sonali, A., G.N. Pandey and M.D. Tiwari, 2012. Data mining in education: Data classification and decision tree approach. Int. J. e-Educ. E-Bus. eLearn., 2(2): 140-144
Sree, G. & Ruppa, Ch. (2013). Data Mining: Performance Improvement in Education Sector Using Classification and Clustering Algorathim. IJIRD. Vol 2, Issue 7
Surjeet, K., & Saurabh, P. (2012). Data Mining Application in Enrolment Management: A Case Study. International Journal Of Computer Application (IJCA) Vol.41 No.5, pp 1-6
Surjeet K. Y and Saurabh P (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 2, 51-56, 2012
Tarun, I., Gerardo, B. and Tanguilig, B. (2014). Predictive Decision Support System for Licensure Examination Performance through Integration of Multiple Regression and PART Classification Models of Data Mining
Cite this article:
License:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
