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Home»Articles»Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology Motivation

Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology Motivation

Author : S. Peerbasha and M. Mohamed Surputheen
Volume 8 No.2 Special Issue:March 2019 pp 83-87

Abstract

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.

Keywords

Knowledge Mining, Prediction, Cluster, Educational Data Mining, Classification

Full Text:

References

[1] 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.
[2] 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.
[3] J. Bucko and L. Kakalejclk, “Machine learning techniques in the education process of students of economics”, IEEE, MIPRO 2017, pp 22-26, 2017.
[4] 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.
[5] 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.
[6] 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.
[7] 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).
[8] 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.
[9] 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.
[10] 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.
[11] Hesam Izakian and Witold Pedrycz, “Anomaly Detection in Time Series Data using a Fuzzy c means clustering”, IEEE Xplore, 26 Sept. 2013.
[12] 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.
[13] 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.
[14] 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.

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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.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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