Data mining: Application of digital marketing in education
Main Article Content
Abstract
The excessive cost of inadequate management of stored information resources by companies means a significant loss for them, causing them to invest more than they should in technology. To overcome and avoid more significant losses, companies must counteract this type of problem. The present work's aim is to apply good data mining through digital business marketing that will allow ordering and filtering of the relevant information in the databases through RapidMiner, to supply the companies' databases with only relevant information for the normal development of their functions. For this purpose, the Knowledge Discovery Databases (KDD) methodology will be used, which will allow us to filter and search for information patterns that are hidden in order to take advantage of the historical data of investment per student in the educational sector and to establish a more accurate and efficient data prediction. As a result, it was found that over the years, the expenditure per student increases regardless of the area in which it is located, that although not in all provinces same amount is allocated, it is observed that it maintains an upward trend concerning the expenditures made, concluding that the KDD methodology allowed us to graph and showed how the expenditure allocated to the education sector has varied in the different grades of education, providing relevant information that will be useful for future related studies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
- Ahuja, R., Jha, A., Maurya, R., & Srivastava, R. (2019). Analysis of Educational Data Mining. Advances in Intelligent Systems and Computing, 741, 897-907. https://doi.org/10.1007/978-981-13-0761-4_85
- Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194. https://doi.org/10.1016/J.COMPEDU.2017.05.007
- Asor, J. R., Lerios, J. L., Sapin, S. B., Padallan, J. O., & Buama, C. A. C. (2021). Fire incidents visualization and pattern recognition using machine learning algorithms. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1427-1435. https://doi.org/10.11591/IJEECS.V22.I3.PP1427-1435
- Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537-553. https://doi.org/10.1007/S10639-017-9616-Z
- Andreas, B., Reischl, M., & Mikut, R. (2019). Data Mining Tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1309. https://doi.org/10.1002/WIDM.1309
- Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1-13. https://doi.org/10.1016/J.JII.2017.08.001
- Davari, M., Noursalehi, P., & Keramati, A. (2019). Data mining approach to professional education market segmentation: a case study. Journal of Marketing for Higher Education, 29(1), 45-66. https://doi.org/10.1080/08841241.2018.1545724
- Drayton-Brooks, S. M., Gray, P. A., Turner, N. P., & Newland, J. A. (2020). The use of big data and data mining in nurse practitioner clinical education. Journal of Professional Nursing, 36(6), 484-489. https://doi.org/10.1016/J.PROFNURS.2020.03.012
- Drayton-Brooks, S. M., Gray, P. A., Turner, N. P., & Newland, J. A. (2020). The use of big data and data mining in nurse practitioner clinical education. Journal of Professional Nursing, 36(6), 484-489. https://doi.org/10.1016/J.PROFNURS.2020.03.012
- Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. Ieee Access, 5, 15991-16005. https://doi.org/10.1109/ACCESS.2017.2654247
- Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335-343. https://doi.org/10.1016/J.JBUSRES.2018.02.012
- Konstantopoulou, G., Dimitra, V., Papakala, I., Styliani, R., Vasiliki, T., Ioakeimidi, M., Niros, A., Boutis, M., & Iliou, T. (2022). The mental resilience of employees in special education during the pandemic Covid-19. Advances in Mobile Learning Educational Research, 2(1), 246-250. https://doi.org/10.25082/AMLER.2022.01.008
- Lu, H., Setiono, R., & Liu, H. (2017). Neurorule: A connectionist approach to data mining. arXiv preprint arXiv:1701.01358. https://doi.org/10.48550/arxiv.1701.01358
- Mozombite-Jayo, N., Manrique-Jaime, F., Castillo-Lozada, S., Romero-Andrade, C., Giraldo-Retuerto, M., Delgado, A., & Andrade-Arenas, L. (2022). Systemic Analysis of the Use of Technological Tools in the University Learning Process. International Journal of Engineering Pedagogy, 12(4), 63-84. https://doi.org/10.3991/IJEP.V12I4.30833
- Papadakis, S. (2021). Advances in Mobile Learning Educational Research (AMLER): Mobile learning as an educational reform. Advances in Mobile learning educational research, 1(1), 1-4. https://doi.org/10.25082/AMLER.2021.01.001
- Rodrigues, M. W., Isotani, S., & Zarate, L. E. (2018). Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701-1717. https://doi.org/10.1016/J.TELE.2018.04.015
- Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/WIDM.1355
- Slater, S., Joksimović, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2017). Tools for educational data mining: A review. Journal of Educational and Behavioral Statistics, 42(1), 85-106. https://doi.org/10.3102/1076998616666808
- Sunar, P. K., Dahal, N., & Pant, B. P. (2023). Using digital stories during COVID-19 to enhance early-grade learners' language skills. Advances in Mobile Learning Educational Research, 3(1), 548-561. https://doi.org/10.25082/AMLER.2023.01.003
- Ünal, F. (2020). Data mining for student performance prediction in education. Data Mining-Methods, Applications and Systems, 28, 423-432. https://doi.org/10.5772/INTECHOPEN.91449