Abstract
To ease of school management for the identification of student’s protector during his or her schooling and evaluate the performance of a student, the guardian predictive models are presented with the help of machine learning algorithms. For this, standard secondary dataset of two secondary schools was considered from Portugal belonging to the language course. The initial dataset consisted of 649 instances and 33 features. These features belong to the student’s academic, demography, family and personal features. The guardian feature has been considered as a class variable and others (significant) features assumed as predictors. In the orange platform, three machine learning algorithms, support vector machine (SVM), random forest (RF) and neural network (NN), were used with three testing techniques. On one hand, the SVM computed the highest prediction probabilities of 0.996 for other class and another hand, the NN gives the largest prediction probabilities such as 0.906 for father class and 0.889 for mother class. The NN attained the most guardian prediction accuracy of 89% and outperformed others. Also, leave-one-out method significantly enhanced the prediction accuracy of each learner except the SVM. Also, it proved the NN learner slower with prediction time (23 s) and makes the SVM as faster with time (14 s). This study may not only helpful to the school management but also support the social administration of the district or state. Using the model, it must be significant to predict the care-taker of the student.
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References
Fernández, D.B., Gil, D., Mora, S.L.: Application of machine learning in predicting performance for computer engineering students: a case study. Sustain. J. 1–18 (2019). https://doi.org/10.3390/su11102833
Koutina, M., Kermanidis, K.L.: Predicting postgraduate student’s performance using machine learning techniques. In: IFIP Advances in Information and Communication Technology, vol. 364 (2011). https://doi.org/10.1007/978-3-642-23960-1_20
Verma, C., Illés, Z.: Attitude prediction towards ICT and mobile technology for the real-time: an experimental study using machine learning. In: The 15th International Scientific Conference eLearning and Software for Education, pp. 247–254, Romania (2019). https://doi.org/10.12753/2066-026X-19-171
Verma, C., Stoffová, V., Illés, Z.: Prediction of students’ awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results. Heliyon 5(6), 1–7 (2019a). https://doi.org/10.1016/j.heliyon.2019.e01806
Harinath, S., Prasad, A., Suma, H.S., Suraksha, A., Mathew, T.: Student placement prediction using machine learning. Int. Res. J. Eng. Technol. 6(4), 4577–4579 (2019)
Manvitha, P., Swaroopa, N.: Campus placement prediction using supervised machine learning techniques. Int. J. Appl. Eng. Res. 14(9), 2188–2191 (2019)
Bathla, Y., Verma, C., Kumar, N.: Smart approach for real time gender prediction of European School’s principal using machine learning. In: Proceeding of ICRIC 2019, Lecture Notes in Electrical Engineering (LNEE), pp. 159–175 Springer, Berlin (2019). https://doi.org/10.1007/978-3-030-29407-6_14
Verma, C., Tarawneh, A.S., Stoffová, V., Illés, Z., Dahiya, S.: Gender prediction of the European school’s teachers using machine learning: preliminary results. In: Proceeding of 8th IEEE International Advance Computing Conference, pp. 213–220, IEEE (2018). https://doi.org/10.1109/IADCC.2018.8692100
Verma, C., Stoffová, V., Illés, Z.: An ensemble approach to identifying the student gender towards information and communication technology awareness in european schools using machine learning. Int. J. Eng. Technol. 7(4), 3392–3396 (2018)
Verma, C., Illés, Z., Stoffová, V.: Gender prediction of Indian and Hungarian students towards ICT and mobile technology for the real-time. Int. J. Innov. Technol. Explor. Eng. 8(9S3), 1260–1264 (2019)
Verma, C., Stoffová, V., Illés, Z.: Ensemble methods to predict the locality scope of Indian and Hungarian students for the real time. In: Proceeding of ICACIE-2019, Advances in Intelligent Systems and Computing, pp. 1–13, Springer, Berlin (2020). (in press)
Verma, C., Stoffová, V., Illés, Z.: Real-time prediction of student’s locality towards information communication and mobile technology: preliminary results. Int. J. Recent Technol. Eng. 8(1), 580–585 (2019b)
Verma, C., Illés, Z., Stoffová, V.: Real-time classification of national and international students for ICT and mobile technology: an experimental study on Indian and Hungarian university. J. Phys.: Conf. Ser. 14032, 1–8 (2020). https://doi.org/10.1088/1742-6596/1432/1/012091
Verma, C., Stoffová, V., Illés, Z.: Feature selection to identify the residence state of teachers for the real-time. In: IEEE International Conference on Intelligent Engineering and Management, pp. 1–6, Accepted, London (2020)
Verma, C., Stoffová, V., Illés, Z.: Prediction of residence country of student towards information, communication and mobile technology for real-time: preliminary results. Procedia Comput. Sci. 167C, 224–234 (2020). In: Proceedings of ICCIDS-2019. Elsevier. https://doi.org/10.1016/j.procs.2020.03.213
Verma, C., Tarawneh, A.S., Illés, Z., Stoffová, V., Singh, M.: National identity predictive models for the real time prediction of European school’s students: preliminary results. In: IEEE International Conference on Automation, Computational and Technology Management, pp. 418–423, IEEE (2019). https://doi.org/10.1109/ICACTM.2019.8776842
Verma, C., Stoffová, V., Illés, Z.: Age group predictive models for the real time prediction of the university students using machine learning: preliminary results. In: 2019 IEEE Third International Conference on Electrical, Computer and Communication, pp. 1–7 (2019). https://doi.org/10.1109/ICECCT.2019.8869136
Verma, C., Illés, Z., Stoffová, V.: Study level prediction of Indian and Hungarian students towards ICT and Mobile Technology for the real-time. In: IEEE International Conference on Computation, Automation and Knowledge Management, pp. 219–223, UAE (2020). (in press). https://doi.org/10.1109/ICCAKM46823.2020.9051551
Verma, C., Illés, Z., Stoffová, V.: Real-time prediction of development and availability of ICT and mobile technology in Indian and Hungarian university. In: Proceeding of ICRIC 2019, Lecture Notes in Electrical Engineering (LNEE), pp. 605–615. Springer, Berlin (2020). https://doi.org/10.1007/978-3-030-29407-6_43
Verma, C., Illés, Z., Stoffová, V., Singh, M.: ICT and mobile technology features predicting the university of Indian and Hungarian student for the real-time. In: IEEE System Modeling & Advancement in Research Trends, pp. 85–90, IEEE (2020). https://doi.org/10.1109/SMART46866.2019.9117414
Singh, M., Verma, C., Kumar, R., Juneja, P.: Towards enthusiasm prediction of Portuguese school’s students towards higher education in real time. In: IEEE International Conference on Computation, Automation and Knowledge Management, pp. 427–431. IEEE (2020). https://doi.org/10.1109/ICCAKM46823.2020.9051459
Verma, C., Illés, Z., Stoffová, V.: Predictive modeling to predict the residency of teachers using machine learning for the real-time. In: Proceeding of FTNCT- 2019, Communications in Computer and Information Science (CCIS), pp. 592–601, Springer, Berlin (2020). https://doi.org/10.1007/978-981-15-4451-4_47
Singh, P.K., Kar, A.K., Singh, Y., Kolekar, M.H., Tanwar, S., Proceedings of ICRIC 2019, Recent Innovations in Computing, 2020, Lecture Notes in Electrical Engineering, vol. 597, pp. 3–920. Springer, Cham. https://doi.org/10.1007/978-3-030-29407-6
Singh, P.K., Bhargava, B.K., Paprzycki, M., Kaushal, N.C., Hong, W.C.: Handbook of wireless sensor networks: issues and challenges in current scenario’s. Adv. Intell. Syst. Comput. 1132, 155–437 (2020). https://doi.org/10.1007/978-3-030-40305-8
Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P.K., Hong, W.: Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8, 474–488 (2020). https://doi.org/10.1109/ACCESS.2019.2961372
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The first author’s work was support by Hungarian Government and European Social Funds under the project “Talent Management in Autonomous Vehicle Control Technologies (EFOP-3.6.3-VEKOP-16-2017-00001)”.
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Verma, C., Stoffová, V., Illés, Z., Kumar, D. (2021). Toward Prediction of Student’s Guardian in the Secondary Schools for the Real Time. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Chhabra, J.K., Sen, A. (eds) Recent Innovations in Computing. ICRIC 2020. Lecture Notes in Electrical Engineering, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-15-8297-4_60
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