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2021 | OriginalPaper | Buchkapitel

Toward Prediction of Student’s Guardian in the Secondary Schools for the Real Time

verfasst von : Chaman Verma, Veronika Stoffová, Zoltán Illés, Deepak Kumar

Erschienen in: Recent Innovations in Computing

Verlag: Springer Singapore

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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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Literatur
3.
Zurück zum Zitat 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., 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
5.
Zurück zum Zitat 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) 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)
6.
Zurück zum Zitat Manvitha, P., Swaroopa, N.: Campus placement prediction using supervised machine learning techniques. Int. J. Appl. Eng. Res. 14(9), 2188–2191 (2019) Manvitha, P., Swaroopa, N.: Campus placement prediction using supervised machine learning techniques. Int. J. Appl. Eng. Res. 14(9), 2188–2191 (2019)
7.
Zurück zum Zitat 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 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
8.
Zurück zum Zitat 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., 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
9.
Zurück zum Zitat 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., 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)
10.
Zurück zum Zitat 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., 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)
11.
Zurück zum Zitat 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.: 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)
12.
Zurück zum Zitat 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., 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)
14.
Zurück zum Zitat 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.: 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)
15.
16.
Zurück zum Zitat 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., 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
17.
Zurück zum Zitat 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., 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
18.
Zurück zum Zitat 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.: 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
19.
Zurück zum Zitat 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.: 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
21.
22.
Zurück zum Zitat 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 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
Metadaten
Titel
Toward Prediction of Student’s Guardian in the Secondary Schools for the Real Time
verfasst von
Chaman Verma
Veronika Stoffová
Zoltán Illés
Deepak Kumar
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-8297-4_60