Predict high school students' final grades using basic machine learning

Sigit Sugiyanto

Abstract


To improve the quality of students, teachers must be able to take precautionary measures to deal with students who are lacking or have the potential to experience deficiency. Student ratings are temporary, however, have a profound impact on students' mental and enthusiasm for learning. As a teacher, it is very important to make predictions in dealing with this matter because if the ranking has been issued, it is too late. In this article, we will discuss and make Student grade predictions using basic machine learning, we will also discuss the continuity between student data and machine learning

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Keywords


MAE; Machine Learning; Student Grade Prediction; Educational Data Mining

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References


Romero, Cristóbal, and Sebastián Ventura. "Educational data mining: a review of the state of the art." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40.6 (2010): 601-618.

Baker, Ryan SJD, and Kalina Yacef. "The state of educational data mining in 2009: A review and future visions." JEDM| Journal of Educational Data Mining 1.1 (2009): 3-17.

Dutt, Ashish, Maizatul Akmar Ismail, and Tutut Herawan. "A systematic review on educational data mining." Ieee Access 5 (2017): 15991-16005.

Peña-Ayala, Alejandro. "Educational data mining: A survey and a data mining-based analysis of recent works." Expert systems with applications 41.4 (2014): 1432-1462.

De Villiers, Ethel-Michele, et al. "Classification of papillomaviruses." Virology 324.1 (2004): 17-27.

Ngai, Eric WT, Li Xiu, and Dorothy CK Chau. "Application of data mining techniques in customer relationship management: A literature review and classification." Expert systems with applications 36.2 (2009): 2592-2602.

Ahmad, Fadhilah, Nur Hafieza Ismail, and Azwa Abdul Aziz. "The prediction of students’ academic performance using classification data mining techniques." Applied Mathematical Sciences 9.129 (2015): 6415-6426.

Bhardwaj, Brijesh Kumar, and Saurabh Pal. "Data Mining: A prediction for performance improvement using classification." arXiv preprint arXiv:1201.3418 (2012).

Antonie, Maria-Luiza, Osmar R. Zaiane, and Alexandru Coman. "Application of data mining techniques for medical image classification." Proceedings of the Second International Conference on Multimedia Data Mining. 2001.

Thomas, Emily H., and Nora Galambos. "What satisfies students? Mining student-opinion data with regression and decision tree analysis." Research in Higher Education 45.3 (2004): 251-269.

Ruß, Georg. "Data mining of agricultural yield data: A comparison of regression models." Industrial Conference on Data Mining. Springer, Berlin, Heidelberg, 2009.

Bakar, Zuriana Abu, et al. "A comparative study for outlier detection techniques in data mining." 2006 IEEE conference on cybernetics and intelligent systems. IEEE, 2006.

Weber, Ben G., and Michael Mateas. "A data mining approach to strategy prediction." 2009 IEEE Symposium on Computational Intelligence and Games. IEEE, 2009.

Yeh, I-Cheng, and Che-hui Lien. "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients." Expert Systems with Applications 36.2 (2009): 2473-2480.

Majumdar, Jharna, Sneha Naraseeyappa, and Shilpa Ankalaki. "Analysis of agriculture data using data mining techniques: application of big data." Journal of Big data 4.1 (2017): 20.

Chai, Tianfeng, and Roland R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature." Geoscientific model development 7.3 (2014): 1247-1250.

Willmott, Cort J., and Kenji Matsuura. "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance." Climate research 30.1 (2005): 79-82.

Franses, Philip Hans. "A note on the mean absolute scaled error." International Journal of Forecasting 32.1 (2016): 20-22.

Akmal, “Predicting Dropout on E-learning Using Machine Learning,” J. Appl. Data Sci., vol. 1, no. 1, pp. 29–34, 2020, [Online]. Available: http://bright-journal.org/Journal/index.php/JADS/article/view/6.


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Journal of Applied Data Sciences

ISSN : 2723-6471 (Online)
Organized by : Departement of Information System, Universitas Amikom Purwokerto, Indonesia; Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia.
Website : http://bright-journal.org/JADS
Email : taqwa@amikompurwokerto.ac.id (principal contact)
    husniteja@uinjkt.ac.id (managing editor)
    support@bright-journal.org (technical issues)

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