A Comparative Study of School Parent Satisfaction Predictors using different Classifiers

  • Saronyo Lal Mukherjee The Bhawanipur Education Society College, Kolkata, INDIA
  • Shawni Dutta The Bhawanipur Education Society College, Kolkata, INDIA.
Keywords: Parent Satisfaction, Student performance, EDM, Machine learning, classification

Abstract

Educational data mining (EDM) is applied on voluminous student information for obtaining some useful information. This research focuses on the parents' satisfaction based on their executed study. Instead of focusing only from the educational institutions, it is also required to put concentration to the parents’ side. Depending on the factors such as how the student carries out their study, their examination result and many more, parental satisfaction is predicted. For carrying out the analysis of these parameters, machine learning methods are implemented and applied to the educational dataset. Several machine learning models such as Support Vector Machines (SVM), k-Nearest Neighbours (KNN), Decision Tree classifiers, and Multi-layer Perceptron classifier (MLP) are constructed for predicting parental satisfaction level. Comparative analysis shows the highest accuracy of 92% executed by the SVM model. Executing this predictive modeling will assist the parents to guide and motivate their children towards areas that demand improvement.

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References

Avella, John T., et al. "Learning analytics methods, benefits, and challenges in higher education: A systematic literature review." Online Learning 20.2 (2016): 13-29.

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.

Papamitsiou, Zacharoula K., and Anastasios A. Economides. "Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence." Educational Technology & Society 17.4 (2014): 49-64.

Driessen, Geert, Frederik Smit, and Peter Sleegers. "Parental involvement and educational achievement." British educational research journal 31.4 (2005): 509-532.

Singh, Amanpreet, Narina Thakur, and Aakanksha Sharma. "A review of supervised machine learning algorithms." 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com). IEEE, 2016.

Ma, Yunqian, and GuodongGuo, eds. Support vector machines applications. Vol. 649. New York, NY, USA:: Springer, 2014.

Guo, Gongde, et al. "KNN model-based approach in classification." OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, Berlin, Heidelberg, 2003.

Priyam, Anuja, et al. "Comparative analysis of decision tree classification algorithms." International Journal of current engineering and technology 3.2 (2013): 334-337.

Windeatt, Terry. "Accuracy/diversity and ensemble MLP classifier design." IEEE Transactions on Neural Networks 17.5 (2006): 1194-1211.

Chicco, Davide, and Giuseppe Jurman. "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation." BMC genomics 21.1 (2020): 1-13.

Harville, David A., and Daniel R. Jeske. "Mean squared error of estimation or prediction under a general linear model." Journal of the American Statistical Association 87.419 (1992): 724-731.

Sekeroglu, Boran, KamilDimililer, and Kubra Tuncal. "Student performance prediction and classification using machine learning algorithms." Proceedings of the 2019 8th International Conference on Educational and Information Technology. 2019.

Altabrawee, Hussein, Osama Abdul Jaleel Ali, and Samir Qaisar Ajmi. "Predicting Students’ Performance Using Machine Learning Techniques." JOURNAL OF UNIVERSITY OF BABYLON for pure and applied sciences 27.1 (2019): 194-205.

Kaur, Parneet, Manpreet Singh, and Gurpreet Singh Josan. "Classification and prediction based data mining algorithms to predict slow learners in education sector." Procedia Computer Science 57 (2015): 500-508.

Osmanbegovic, Edin, and Mirza Suljic. "Data mining approach for predicting student performance." Economic Review: Journal of Economics and Business 10.1 (2012): 3-12.

Guo, Bo, et al. "Predicting students performance in educational data mining." 2015 International Symposium on Educational Technology (ISET). IEEE, 2015.

Vijayalakshmi, V., and K. Venkatachalapathy. "Comparison of Predicting Student’s Performance using Machine Learning Algorithms." International Journal of Intelligent Systems and Applications 11.12 (2019): 34.

Guo, Philip J., Juho Kim, and Rob Rubin. "How video production affects student engagement: An empirical study of MOOC videos." Proceedings of the first ACM conference on Learning@ scale conference. 2014.

Manwaring, Kristine C., et al. "Investigating student engagement in blended learning settings using experience sampling and structural equation modeling." The Internet and Higher Education 35 (2017): 21-33.

Ding, Lu, Erkan Er, and Michael Orey. "An exploratory study of student engagement in gamified online discussions." Computers & Education 120 (2018): 213-226.

Atherton, Mirella, et al. "Using learning analytics to assess student engagement and academic outcomes in open access enabling programmes." Open Learning: The Journal of Open, Distance and e-Learning 32.2 (2017): 119-136.

Ibrahim Aljarah (November, 2016),Students' Academic Performance Dataset.Retrieved on June 06, 2020 from https://www.kaggle.com/aljarah/xAPI-Edu-Data

Bisong, Ekaba. "Introduction to Scikit-learn." Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA, 2019. 215-229.

Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1396-1400.

Published
2021-06-02
How to Cite
Saronyo Lal Mukherjee, & Shawni Dutta. (2021). A Comparative Study of School Parent Satisfaction Predictors using different Classifiers. International Journal for Research in Applied Sciences and Biotechnology, 8(3), 104-109. https://doi.org/10.31033/ijrasb.8.3.14
Section
Articles