A Modern Approach on Student Performance Prediction using Multi-Agent Data Mining Technique

L. Venkateswara Reddy*, K. Yogitha**, K. Bandhavi***, G. Sai Vinay****, G. Dinesh Kumar*****
* Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.
**-***** Student, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.
Periodicity:July - September'2015
DOI : https://doi.org/10.26634/jse.10.1.3628

Abstract

Among the evolving researches on data mining, one such field of interests is on the education. This emerging field of research in education is called Educational Data Mining, which means the data related to the field of education. One of the main concerns in educational field is the academic scores of a student, which helps in the growth of the student as well as the Institution. To predict a student's performance is a very important one in educational field. To maintain the scores and in order to increase the scores of a student the prediction of one's performance is necessary. To achieve this objective of predicting, the performance is fulfilled by the usage of data mining. A high prediction accuracy of the student's performance is more helpful to identify the slow performance students at the beginning of the learning process. Data mining techniques are used to analyze the models or patterns of data, and it is also helpful in the decision-making [19]. Boosting technique [21][22] is one of the most popular techniques for constructing classifier by ensemble to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of Boosting algorithm. It is applicable for the binary classification and not used in multiclass classification directly. Therefore, an extension for the AdaBoost is proposed which is SAMME boosting technique for the multiclass classification without reducing it to a set of sub-binary classification. In this paper, the authors have evaluated student's performance prediction system to predict the performance of the students based on their data with high prediction accuracy and provide help to the slow learning students by using optimization rules.

Keywords

Classification, Boosting, AdaBoost, Ensemble of Classifiers, Stagewise Additive Modeling using Multiclass Exponential Loss Function (SAMME), Student's Performance Prediction.

How to Cite this Article?

Reddy, L. V., Yogitha, K., Bandhavi, K., Vinay, G. S., and Kumar, G. D. (2015). Modern Approach Student Performance Prediction using Multi-Agent Data Mining Technique. i-manager’s Journal on Software Engineering, 10(1), 14-20. https://doi.org/10.26634/jse.10.1.3628

References

[1]. Abdullah AL-Malaise, Areej Malibari, and Mona Alkhozae, (2014). “Student's Performance Prediction System using Multi Agent Data Mining Technique”, International Journal of Data Mining and Knowledge Management Process (IJDKP), Vol.4, No.5, pp.1-19.
[2]. A. Rathee, and R.P. Mathur, (2013). “Survey on Decision Tree classification algorithms for the Evaluation of Student Performance”, International Journal of Computers and Technology, Vol.4, issues 2, pp.244-247.
[3]. Y. Freund, and R. Schapire, (1996). “Experiments with a new boosting algorithm”, 13th International Conference on Machine Learning, pp.148-156.
[4]. J. Zhu, H. Zou, S. Rosset, and T. Hastie, (2009). “Multiclass adaboost”, Statistics and Its Interface, Vol.2, pp.349-360.
[5]. A. Fernandez-Baldera, and L. Baumela, (2014). ”Multi-class boosting with asymmetric binary weaklearners”, Pattern Recognition, Vol.47, issue.5, pp.2080- 2090.
[6]. G. Agrawal and H. Gupta, (2013). “Optimization of C4.5 Decision Tree Algorithm for Data Mining Application”, International Journal of Emerging Technology and Advanced Engineering, Vol.3, issue.3, pp.341-345.
[7]. S. Kumar and M. Vijayalakshmi, (2012). “Mining Of Student Academic Evaluation Records in Higher Education”, International Conference on Recent Advances in Computing and Software Systems (RACSS), pp.67-70.
[8]. E. Osmanbegovi c, and M. Sulji c, (2012). “Data mining approach for predicting student performance”, Economic Review, Vol.10, issue 1.
[9]. M. Sukanya, S. Biruntha, S. Karthik, and T. Kalaikumaran, (2012). “Data Mining: Performance Improvement in Education Sector using Classification and Clustering”, International Conference on Computing and Control Engineering (ICCCE).
[10]. S. Yadav, B. Bharadwaj, and S. Pal, (2012). “Data mining applications: A comparative study for predicting student's performance”, International Journal of Innovative Technology & Creative Engineering (IJITCE), Vol.1, issue 12, pp.13-19.
[11]. S. Shanthi, and R.G. Ramani, (2012). “Gender specific classification of road accident patterns through data mining techniques”, IEEE International Conference on Advances In Engineering, Science And Management (ICAESM -2012), pp.359-365.
[12]. B. Verma, and A. Rahman, (2012). “Cluster- oriented ensemble classifier : Impact of multicluster characterization on ensemble classifier learning”, IEEE Transactions on Knowledge and Data Engineering, Vol.24, issue.4, pp.605-618.
[13]. B. Bhardwaj, and S. Pal, (2011). “Data Mining: A prediction for performance improvement using classification”, International Journal of Computer Science and Information Security (IJCSIS), Vol.9, issue.4.
[14]. M. Alkhattabi, D. Neagu, and A. Cullen, (2011). “Assessing information quality of e-learning systems: a web mining approach”, Computers in Human Behavior, Vol.27, issue 2, pp.862-873.
[15]. A. Tan and D. Gilbert, (2003). “Ensemble machine learning on gene expression data for cancer classification”, Applied Bioinformatics, Vol.2, No.3, pp.75- 83.
[16]. I. Paris, L. Affendey, and N. Mustapha, (2010). “Improving academic performance prediction using voting technique in data mining”, World Academy of Science, Engineering and Technology, Vol.4, pp.820-823.
[17]. K. Albashiri, (2010). An investigation into the issues of multi-agent data mining, PhD, University of Liverpool.
[18]. Y. Freund, and R. Schapire, (1999). “A short introduction to boosting”, Journal of Japanese Society For Artificial Intelligence, Vol.14, iss 5, pp.771-780.
[19]. Y. Freund, and R. Schapire, (1997). “A decisiontheoretic generalization of on-line learning and an application to boosting”, Journal of Computer and System Sciences, Vol.55, No.1, pp.119-139.
[20]. R. Nisbet, J. Elder, and G. Miner, (2009). Handbook of statistical analysis and data mining applications, 1st ed. Amsterdam: Academic Press/Elsevier.
[21]. J. Friedman, T. Hastie, and R. Tibshirani, (2000). “Additive logistic regression: a statistical view of boosting”, The Annals of Statistics, Vol.28, No.2, pp.337-407.
[22]. R. Schapire, and Y. Singer, (1999). 'Improved boosting algorithms using confidence-rated prediction', Machine Learning, Vol.37(1), pp.297-336.
[23]. A. Bakar, Z. Othman, A. Hamdan, R. Yusof, and R. Ismail, (2008). “Agent based data classification approach for data mining”, International Symposium on Information Technology, Vol.2, pp.1-6.
[24]. C. Chen, Y. Chen, and C. Liu, (2007). “Learning performance assessment approach using web-based learning portfolios for e-learning systems”, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol.37, issue.6, pp.1349.1359.
[25]. C. Wang, (2006). “New ensemble machine learning method for classification and prediction on gene expression data”, Conference on IEEE Engineering in Medicine and Biology Society, pp.3478-3481.
[26]. Y. Liu, (2005). “Drug Design by Machine Learning: Ensemble Learning for QSAR Modelling”, Fourth International Conference on Machine Learning and Applications (ICMLA'05).
[27]. W. Iba and P. Langley, (1992). “Induction of one-level decision trees”, Ninth International Workshop on Machine Learning, pp.233-240.
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