Academic risk forecast model for undergraduate students of the National University of Engineering
DOI:
https://doi.org/10.21754/iecos.v21i1.1073Keywords:
machine learning, data integration, predictionAbstract
The present work, uses unstructured information in order to predict the academic risk of a student, making use of Machine Learning techniques.
Phases:
- Construction of the datamart: The data from the different sources will be integrated to build the objective data repository, which will be divided into two: Training data and test data,
- Training of the model: which consists in elaborating the training model based on data from the
datamart, applying vectorial support machine. - Validation of the model: It consists of evaluating the model obtained previously, using the test data from the datamart.
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References
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https://newsinitiative.withgoogle.com/training/states/consumer_insights/pdfs/gni-new-consumer-insights-playbook.pdf
DBi Data Business Intelligence - Havas. (2019). Obtenido de Google Analytics: ¿Y tú qué necesitas? ¿la versión gratuita o 360?: https://dbibyhavas.io/es/blog/google-analytics-y-tu-que-necesitas-la-version-gratuita-o-360/
Google Analytics Developers. (2019). Obtenido de Enviar datos a Google Analytics: https://developers.google.com/analytics/devguides/collection/analyticsjs/sending-hits?hl=es-419
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning with Applications in R. California: Springer.
Jeffares, A. (Noviembre de 2019). Towards Data Science. Obtenido de K-means: A Complete Introduction: https://towardsdatascience.com/k-means-a-complete-introduction-1702af9cd8c
Kladnik, M., Stopar, L., Fortuna, B., & Mladenić, D. (2017). Audience Segmentation Based on Topic Profiles. Jožef Stefan Institute and Jožef Stefan International Postgraduate School, 1.
Lopez, G., Seaton, D. T., Ang, A., Tingley, D., & Chuang, I. (2017). Google BigQuery for Education: Framework for Parsing and Analyzing edX MOOC Data. L@S '17: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale.
Syakur, M. A., Khotimah, B. K., Rochman, E. M., & Satoto, B. D. (2018). Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster. IOP Conference Series: Materials Science and Engineering, 1.
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Published
2020-11-13
How to Cite
Garrafa Aragón, H., & Soto-Rodríguez, I. (2020). Academic risk forecast model for undergraduate students of the National University of Engineering. Revista IECOS, 21(1), 121–129. https://doi.org/10.21754/iecos.v21i1.1073
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Research Articles
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Copyright (c) 2020 IECOS Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
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