Recently Educational data mining has gained the attention of the researcher in the research industry and also in the society because of the availability of a large amount of data. There is a need for turning such data into useful information and knowledge. At present, there is a lack of well defined diagnostic algorithm to predict the reason for student absenteeism. It is critical to identify the most significant attributes in a dataset using the traditional statistical methods. This paper focuses on overcoming the difficulties involved in analyzing the student dataset by using Machine Learning Techniques. For mining purpose, Data pre-processing is done on the dataset which is a collection of questionnaire gathered from students in a semi-rural institution. Multilayered Back Propagation Algorithm was used to construct the neural network with weights and bias by applying a transfer function in the dataset. The highly influencing attributes having high weights and bias from the dataset was chosen and a Neural Network was constructed. This knowledge is used to identify the reason for the leave taken by the students and helps the management and staff members to improve the performance of the student.

Educational Data Mining, Artificial Neural Networks, Multilayered Back Propagation Algorithm

[1]. Gurpreet Kaur, Manreet Sohal, “IOT Survey: The Phase Changer in Healthcare Industry”, International Journal of Scientific Research in Network Security and Communication, Vol-6, Issue-2, pp.34-39, 2016.
[2]. L.K. Ojha1 , L.K. Tiwary, R. Sharma,” Information Communication Technology Integration in Education”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.14-15, June-2016.
[3]. Shubham Billus, Shivam Billus, Rishab Behl,” Weather Prediction through Sliding Window Algorithm and Deep Learning”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.01-05, October -2018.
[4]. Kaur, P., Singh, M., &Josan, G. S. “Classification and prediction based data mining algorithms to predict slow learners in the education sector”. Procedia Computer Science, Vol57, pp.500-508, 2015.
[5]. Reale, C., Gavin, K., Librić, L., &Jurić-Kaćunić, D. “Automatic classification of fine-grained soils using CPT measurements and Artificial Neural Networks”. Advanced Engineering Informatics, Vol 36, pp.207-215, 2018.
[6]. Wong, W. T., & Hsu, S. H. “Application of SVM and ANN for image retrieval”. European Journal of Operational Research, Vol173, Issue 3, pp.938-950, 2006.
[7]. Jia, W., Zhao, D., & Ding, L. “An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample”, Applied Soft Computing, Vol48, pp.373-384, 2016.
[8]. Bondarenko, A., Aleksejeva, L., Jumutc, V., &Borisov, A. “Classification Tree Extraction from Trained Artificial Neural Networks”, Procedia Computer Science, Vol104, pp.556-563, 2017.
[9]. Gallego, A. J., Calvo-Zaragoza, J., Valero-Mas, J. J., & Rico-Juan, J. R.“Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation”. Pattern Recognition, Vol74, pp.531-543, 2018.
[10]. Rizk, Y., Hajj, N., Mitri, N., &Awad, M. “Deep Belief Networks and Cortical Algorithms: A Comparative Study for Supervised Classification”, Applied Computing and Informatics, 2018.
[11]. Ougiaroglou, S., Diamantaras, K. I., &Evangelidis, G.“Exploring the effect of data reduction on Neural Network and Support Vector Machine classification”, Neurocomputing, 2018.
[12]. Zhang, J., Williams, S. O., & Wang, H. “Intelligent computing system based on pattern recognition and data mining algorithms”, Sustainable Computing: Informatics and Systems, 2017.