Computer Science & Electrical
Publisher Name: IJRP
Views: 202 , Download: 72
|1||Chinenye C. Opara|
|3||Chukwuemeka P. Oleji|
Over the years, large record of student data exists in Institutions of higher learning because students graduate from these institutions yearly. This has necessitated the need to explore those data to discover some patterns and relationships existing in them and as well make strategic decisions for a better education system. This paper focused on developing an enhanced Hybrid data mining model to mine students’ progress and performance for knowledge discovery and for decision making purposes. This was implemented using Clustering Algorithms (k-means and k-representative) as a tool in data mining. The hybrid model was efficient to cluster the mixed dataset of student’s records. Object-Oriented and Analysis Design Methodology was adopted for the design, Java programming and NetBeans Integrated Development Environment (IDE) was used for the interfaces and Matlab editor respectively. The result shows that the proposed hybrid clustering algorithm improved K-means clustering algorithm for optimal solution and efficient clustering of mixed data sets with 99% performance and clustering error of 0.0025. Academic planners should implement the result of this study and as well make strategic decisions for a better education system.
Aggarwal, D., & Sharma, D., 2019. Application of Clustering for Student Result Analysis. 6, 50–53.
Govindasamy, K., 2018. Analysis of student academic performance using. 119(15), 309–323.
Jamesmanoharan, J., S. Hari Ganesh, M. Felciah, and A. K. Shafreenbanu (2014) "Discovering Students' Academic Performance Based on GPA Using K-Means Clustering Algorithm." In Computing and Communication Technologies (WCCCT), 2014 World Congress on 200-202. IEEE
Kumar, B., & Pal, S., 2011. Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications, 2(6). https://doi.org/10.14569/ijacsa.2011.020609
Mishra, T., Kumar, D., & Gupta, S, 2014. Mining students’ data for prediction performance. International Conference on Advanced Computing and Communication Technologies, ACCT, February, 255–262. https://doi.org/10.1109/ACCT.2014.105
Nagesh, A. S., & Satyamurty, C. V. S, 2018. Application of clustering algorithm for analysis of Student Academic Performance. International Journal of Computer Sciences and Engineering, 6(1), 381–384. https://doi.org/10.26438/ijcse/v6i1.381384
Nikhil Rajahyax and Rudresh S, 2012. Data Mining on Educational Domain Journal Research of Computer Science. June 2012. 56-63
Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C, 2010. Application of k Means Clustering algorithm for prediction of Students Academic Performance. 7, 292–295. http://arxiv.org/abs/1002.2425
Patel, J., & Yadav, R. S, 2015. Applications of Clustering Algorithms in Academic Performance Evaluation. OALib, 02(08), 1–14. https://doi.org/10.4236/oalib.1101623
Rana, S., & Garg, R, 2016. Evaluation of student’s performance of an institute using clustering algorithms. International Journal of Applied Engineering Research, 11(5), 3605–3609.
Shirwaikar, R., & Rajadhyax, N, 2012. Analyzing Students Performance Using Frequent Item Set Mining, Clustering & Classification. International Journal of Management & Information Technology, 1(2), 31–41. https://doi.org/10.24297/ijmit.v1i2.1444
Shruthi, P., & Chaitra, B. P, 2016. Student Performance Prediction in Education Sector Using Data Mining. International Journal of Advanced Research in Computer Science and Software Engineering, 6(3), 212–218.
Shiwani, R. and Roopali, G, 2016. Evaluation of Student’s Performance of an Institute Using Clustering Algorithms. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 5 (2016) 3605-3609© Research India http://www.ripublication.com.