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Students Performance Prediction in Online Courses Using Machine Learning Algorithms

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Rao, G.M. and Kumar, K.K., 2021. Students Performance Prediction in Online Courses Using Machine Learning Algorithms. United International Journal for Research & Technology (UIJRT), 2(11), pp.74-79.


Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality, class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain existing models a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, prediction problem. we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final and suitable job for the student based on their academic performance and knowledge on skill set.

Keywords: machine learning, algorithms, RNN, data mining, recurrent neural network.


  1. Livieris, et al. (2012): Predicting students’ performance using artificial neural networks 8th PanHellenic Conference with International Participation Information and Communication Technologies, pp.321-328.
  2. Kotsiantis, et al. (2003): Preventing student dropout in distance learning systems using machine learning techniques Applied Artificial Intelligence, 18(5), pp.411- 426.
  3. Moucary, C.E., Khair, M. and Zakhem, W., 2011. Improving student’s performance using data clustering and neural networks in foreign-language based higher education. The Research Bulletin of Jordan ACM, 2(3), pp 27-34
  4. Yi, Hongsuk, Jung, H. and Bae, S., 2017, February. Deep Neural Networks for traffic flow prediction. In Big Data and Smart Computing (BigComp), 2017 IEEE International Conference on (pp. 328-331). IEEE.
  5. LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521(7553), pp. 436-444.
  6. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S. and Darrell, T., 2014, November. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia pp. 675-678
  7. Dahl, G.E., Yu, D., Deng, L. and Acero, A., 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1), pp.30- 42.
  8. Collobert, R. and Weston, J., 2008, July. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning pp.160-167
  9. Koutina M, Kermanidis KL. Predicting postgraduate students’ performance using machine learning techniques. InArtificial Intelligence Applications and Innovations 2011 (pp. 159-168). Springer, Berlin, Heidelberg.
  10. Saini, P. and Jain, A.K., 2013. Prediction using Classification Technique for the Students’ Enrollment Process in Higher Educational Institutions. International Journal of Computer Applications, 84(14). Springer, Berlin, Heidelberg.
  11. Agrawal, H. and Mavani, H., 2015. In Student Performance Prediction using Machine Learning.

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