Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology MotivationAuthor : S. Peerbasha and M. Mohamed Surputheen
Volume 8 No.2 Special Issue:March 2019 pp 83-87
The development of many educational institutions is based on the performance of students learning and understanding capabilities. Here, we analyzed their academic profile with their grades and various cumulative attributes. The academic performance in learning their subjects could be improved by motivational approach. The analysis of student performance is carried out through knowledge-based data mining process. But, the problem is arrived by a probability of information prediction accuracy from student data set which is not accurate. Here, we propose a novel machine learning algorithm based on subspace clustering and multi-perspective classification techniques to identify psychological motivation required students. Also, the extraction of relational patterns to form enhanced clustering classes is done. This discovers the innovative relations between students and their educational performance in the various attributes using surf scale nested clustering approach based on an intelligent predicting system from soft computing processing tasks. This improves the data prediction rate by considering the time factor analysis and complexity to design and develop an efficient clustering algorithm which maximizes the clustering and classification accuracy for improving academic performance.
Knowledge Mining, Prediction, Cluster, Educational Data Mining, Classification
 Mandeep Kaur and Vimal Dev, “A Review on Performance Prediction of Students using Data mining”, Journal of Advanced Research in Information Technology, Systems & Management, Vol. 2, No 3&4, 2017.
 Amirah Mohamed Shahiri, Wahidah Husain and Nur’aini Abdul Rashid, “A Review on Predicting Student’s Performance using Data mining techniques”, Procedia Computer Science, Vol. 72, pp. 414 – 422, 2015.
 J. Bucko and L. Kakalejclk, “Machine learning techniques in the education process of students of economics”, IEEE, MIPRO 2017, pp 22-26, 2017.
 Nikhita Awasthi and Abhay Bansal, “Application of Data mining classification techniques on soil data using R”, International Journal of Advances in Electronics and Computer Science, Vol. 4, No. 1, Jan. 2017.
 D. Rajeshinigo and J. Patricia Annie Jebamalar, “Educational mining: A comparative study of classification algorithms using WEKA”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, No. 3, March 2017.
 Dutt, S. Aghabozrgi, M.A. Binti Ismail and H. Mahroeian, “Clustering algorithms applied in educational data mining”, International Journal of Information and Electronics Engineering, Vol. 5, No. 2, March 2015.
 Er. Anita Devi and Er. Jasjeet Kaur, “A Survey on Data mining and its current research directions”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 4, May 2017 (Special Issue).
 Ivon Arroyo and Beverly Park Woolf, “Inferring learning and attitudes from a Bayesian Network of log file data”, Amsterdam: IOS Press, pp. 33-40, 2005.
 Cristobal Romero and Sebastian Ventura, “Educational Data Mining: A Review of the State-of-the Art”, IEEE transactions on Systems, Man and Cybernetics- Part c: Applications and Reviews, Vol. 40, 2010.
 V. Ramesh, P. Parkavi and K. Ramar, “Predicting Student Performance: A Statistical and Data Mining Approach”, International Journal of Computer Applications, Vol. 63, No. 8, Feb. 2013.
 Hesam Izakian and Witold Pedrycz, “Anomaly Detection in Time Series Data using a Fuzzy c means clustering”, IEEE Xplore, 26 Sept. 2013.
 Coffrin, L. Corrin, P. De Barba and G. Kennedy, “Visualizing patterns of student engagement and performance in MOOCs”, in Proc of the learning analytics and knowledge, ACM, pp. 83-92, 2014.
 Hesamizakian and Witoldpedrycz, “Anomaly detection and characterization in spatial time series data: a cluster-centric approach”, IEEE transactions on fuzzy systems, Vol. 22, No. 6, December 2014.
 R. Bowman, O. Gulacar, and D. B. King, “Predicting student success via online homework usage”, Journal of Learning Design, Vol. 7, No. 2, pp. 47-61, 2014.