Abstract
The major aim of the education institute is to provide the high-quality education to students. The way to attain the high quality in the education system is to determine the knowledge from the educational data and learn the attributes which influence the performance of the students. The extracted knowledge is used to predict the academic performance of the students. This paper presents the student performance prediction model by proposing the Map-reduce architecture based cumulative dragonfly based neural network (CDF-NN). The CDF-NN is proposed by training the neural network by the cumulative dragonfly algorithm (DA). Initially, the marks of the students from semester 1 to semester 7 are collected from different colleges. In the training phase, the features are selected from the student’s information and the intermediate data is generated by the mapper. Then, the intermediate data is provided to the reducer function which is built with the CDF-NN to provide the estimated marks of the students in a forthcoming semester. The proposed method is compared with the existing methods, such as Dragonfly- NN and Back prorogation algorithm for the evaluation metrics, MSE and RMSE. The proposed prediction model obtains the MSE of 16.944 and RMSE of 4.665.
Similar content being viewed by others
References
Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)
Kotsiantis, S.B.: Use of machine learning techniques for educational proposes: a decision support system for forecasting students grades. Artif. Intell. Rev. 37(4), 331–344 (2012)
Minaei-Bidgoli, B., Punch, W.F.: Using genetic algorithms for data mining optimization in an educational web-based system. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 2252–2263, Springer, Berlin, 2003
Wolff, A., Zdrahal, Z., Herrmannova, D., .Knoth, P.P.: Predicting student performance from combined data sources. In: Educational Data Mining, Studies in Computational Intelligence, vol. 524, pp. 175– 202, Springer, Cham, 2014
Guarín, C.E.L., Guzman, E.L., González, F.A.: A model to predict low academic performance at a specific enrollment using DATA mining. IEEE J. Learn. Technol. 10(3), 119–125 (2015)
García,E.P.I., Mora P.M.: Model prediction of academic performance for first-year students. In: Proceedings of International Conference on Artificial Intelligence, pp. 169-174, Puebla, Mexico, 2011
Touron, J.: The determination of factors related to academic achievement in the university: implications for the selection and counseling of students. High. Educ. 12(4), 399–410 (1983)
Lassibilille, G., Gomez, L.N.: Why do higher education students dropout? Evidence from Spain. Educ. Econ. 16(1), 89–105 (2008)
Chen, J.F., Hsieh, H.N., Do, Q.H.: Predicting student academic performance: a comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. J. Algorithms 7(4), 538–553 (2014)
Reynolds C.W.: Flocks, herds, and schools: a distributed behavioral model. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 25–34, ACM, New York, USA, 1987
Ramanathan, L., Geetha, A., Khalid, M., Swarnalatha, P.: Angelina Geetha, Khalid, M., Swarnalatha, P.: Student performance prediction model based on lion-wolf neural network. Int. J. Intell Eng. Syst. 10(1), 114–123 (2017)
Malvandi, S., Farahi, A.: Provide a method for increasing the efficiency of learning management systems using educational data mining. Indian J. Sci. Technol. 8(28), 1–10 (2015)
Ibrahim, Z., Rusli, D.: Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In: 21st Annual SAS Malaysia Forum, Shangri-La Hotel, Kuala Lumpur, 2007
Ibrahim, Z., Rusli, N.M., Janor, R.M.: Predicting students’ academic achievement: comparison between logistic regression, artificial neural network, and neuro-fuzzy. In: Proceedings of the International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008
Bhatnagar, K., Gupta, S.C.: Investigating and modeling the effect of laser intensity and nonlinear regime of the fiber on the optical link. J. Opt. Commun. 38(3), 341–353 (2017)
Yang, X.-S.: Nature-Inspired Meta Heuristic Algorithms, 2nd edn. Luniver Press, Frome (2010)
Cui, Z., Shi, Z.: Boid particle swarm optimization. J. Int. J. Innov. Comput. Appl. 2(2), 77–85 (2009)
Arsad, P.M., Buniyamin,N., Manan, J.A.: A neural network students’ performance prediction model (NNSPPM). In: Proceedings of IEEE International Conference on Smart Instrumentation, Measurement and Applications, Kuala Lumpur, Malaysia, 2013
Avoyan, H.: Machine Learning Is Creating A Demand For New Skills. www.forbes.com/sites/forbestechcouncil/2017/06/26/machine-learning-is-creating-a-demand-for-new- skills/#325004dc7ae2
Mantri, R., Jewalikar, A.: Implementation and performance analysis of academic-MapReduce algorithm (AcdMR). Int. J. Comput. Appl. 121(19), 17–20 (2015)
Tajunisha, N., Anjali, M.: Predicting student performance using MapReduce. Int. J. Eng. Comput. Sci. 4(1), 9971–9976 (2015)
Romero, C., López, M.I., Luna, J.M., Ventura, S.: Predicting students‘ final performance from participation in on-line discussion forums. Comput. Educ. 68, 359–472 (2013)
Asogwa, O.C., Oladugba, A.V.: Of students academic performance rates using artificial neural networks (ANNs). Am. J. Appl. Math. Stat. 3(4), 151–155 (2015)
Tung-Kuang Wu, Shian-Chang Huang, Hsiu-Ting Kao, Hsu Chang, and Ying-Ru Meng, “A MapReduce Implementation of the Genetic-Based ANN Classifier for Diagnosing Students with Learning Disabilities,” In Proceedings of the Seventh International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 30–35, Barcelona, Spain, 2013
Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. In: Proceedings of the 11th international joint conference on Artificial intelligence, vol. 1, pp. 762–767, Detroit, Michigan, 1989
Brajevic,I., Tuba, M.: Training feed-forward neural networks using firefly algorithm. In: Proceedings of the 12th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 156-161, 2013
Mirjalili, S., Hashim, S.Z.M., Sardroudi, H.M.: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput. 218(22), 11125–11137 (2012)
Kadrovach, B.A., Lamont, G.B.: A particle swarm model for swarm-based networked sensor systems. In: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 918–924, Madrid, Spain, 2002
Cui, Z.: Alignment particle swarm optimization. In: Proceedings of 8th IEEE international conference Cognitive Informatics, pp. 497–501, Kowloon, Hong Kong, China, 2009
Pires, E.S., Machado, J.T., de Moura Oliveira, P.B., Cunha, J.B., Mendes, L.: Particle swarm optimization with fractional-order velocity. Nonlinear Dyn. 61(1–2), 295–301 (2010)
Wu, K., Zhong, Y., Wang, X., Sun, W.: A novel approach to subpixel land-cover change detection based on a supervised back-propagation neural network for remotely sensed images with different resolutions. IEEE Geosci. Remote Sens. Lett. 14(10), 1750–1754 (2017)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
VeeraManickam, M.R.M., Mohanapriya, M., Pandey, B.K. et al. Map-Reduce framework based cluster architecture for academic student’s performance prediction using cumulative dragonfly based neural network. Cluster Comput 22 (Suppl 1), 1259–1275 (2019). https://doi.org/10.1007/s10586-017-1553-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1553-5