Abstract
Predicting students’ academic performance (SAP) and dropout are crucial aspects of any academic institution management. It enhances the close monitoring of students’ academic progress and helps in averting possible negative consequences of poor students’ performances. Several models had been developed to predict SAP and dropout; however, the sole reliance on predictive accuracy for the decision on classifier’s performance in this domain and the unquenchable search for efficient models left room for the development of novel and more efficient prediction models. This paper presents novel hybrid prediction models that can be used to predict students’ academic performance and dropout using the Recursive Feature Elimination (RFE) technique. The models are hybrid of Support Vector Machine (SVM) and Naïve Bayes (NB) algorithms. The evaluation of the models in comparison with the existing models showed a better and well comparable performance.
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References
Solomon, D., Patil, P.P., Agrawal, P.: Predicting performance and potential difficulties of university student using classification: survey paper. Int. J. Pure Appl. Math. 118(18), 2703–2707 (2018)
Ameen, A.O., Alarape, M.A., Adewole, K.S.: Students’ academic performance and dropout predictions: a review. Malays. J. Comput. 4(2), 278–303 (2019)
Brownlee, J.: Classification Accuracy is Not Enough: More Performance Measures You Can Use (2014). Available at https://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/. Last accessed on 2019/06/05. F.: Article title. Journal 2(5), 99–110 (2016)
Akosa, J.S.: Predictive accuracy: a misleading performance measure for highly imbalanced data. SAS Global Forum, vol. 942, pp. 1–12 (2017). Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) Conference 2016. LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)
Aziz, A.A., Ismail, N.H., Ahmad, F., Hassan, H.: A framework for students’ academic performance analysis using Naïve Bayes classifier. J. Teknol. 3, 13–19 (2015)
Amra, I.A.A., Maghari, A.Y.A.: Students performance prediction using KNN and Naïve Bayesian. In: 8th International Conference on Information Technology (ICIT) Students, pp. 909–913. IEEE (2017)
Makhtar, M., Nawang, H., Nor, S., Shamsuddin, W.A.N.: Analysis on students performance using Naïve Bayes. J. Theor. Appl. Inf. Technol. 95(16), 3993–4000 (2017)
Agarwal, S., Pandey, G.N., Tiwari, M.D.: Data mining in education: data classification and decision tree approach. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 2(2), 140–145 (2012)
Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., Abreu, R.: A comparative study of classification and regression algorithms for modelling students’ academic performance. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 392–395 (2015)
Altaher, A., Barukab, O.M.: An intelligent hybrid approach for predicting the academic performance of students using genetic algorithms and neuro-fuzzy system. Int. J. Comput. Sci. Netw. Secur. 18(10), 64–70 (2018)
Chen, J., Feng, J., Sun, X., Wu, N., Yang, Z., Chen, S.: MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Math. Probl. Eng. (2019)
Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 43(6) (2019)
Pandey, U.K., Pal, P.S.: Data mining: a prediction of performer or underperformer using classification 2(2), 686–690 (2011)
Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers, USA (2012)
Kaur, G., Singh, W.: Prediction of student performance using Weka tool. Int. J. Eng. Sci. 17(January), 2229–6913 (2016)
Sultana, S., Khan, S., Abbas, M.A.: Prediction performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. Int. J. Electr. Eng. Educ. 54(2), 105–118 (2017)
Vamshidharreddy, V.S., Saketh, A.S., Gnanajeyaraman, R.: Student’s academic performance prediction using machine learning approach. Int. J. Adv. Sci. Technol. 29(9), 6731–6737 (2020)
Mueen, A., Zafar, B., Manzoor, U.: Modeling and predicting students’ academic performance using data mining techniques. Int. J. Mod. Educ. Comput. Sci. 8(11), 36–42 (2016)
Al-Fairouz, E.I., Al-Hagery, M.A.: The most efficient classifiers for the students’ academic dataset. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 11(9), 501–506 (2020)
Razaque, F., Soomro, N., Shaikh, S.A., Soomro, S., Samo, J.A., Kumar, N., Dharejo, H.: Using Naïve Bayes algorithm to students’ bachelor academic performances analysis. In: 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017, 2018-January (November), pp. 1–5 (2018)
Rajeshinigo, D., Jebamalar, J.P.A.: Educational mining: a comparative study of classification algorithms using WEKA. Int. J. Innov. Res. Comput. Commun. Eng. 5(3), 5583–5589 (2011)
Asogbon, M.G., Samuel, O.W., Omisore, M.O., Ojokoh, B.A.: A multi-class support vector machine approach for students academic performance prediction. Int. J. Multi. Curr. Res. 4(March), 210–215 (2016)
Eashwar, K.B., Venkatesan, R., Ganesh, D.: Student performance prediction using SVM. Int. J. Mech. Eng. Technol. (IJMET) 8(11), 649–662 (2017)
Bonifro, F.D., Gabbrielli, M., Lisanti, G., Zingaro, S.P.: Student dropout prediction. In: International Conference on Artificial Intelligence in Education, AIED, pp. 129–140 (2020)
Burman, I., Som, S.: Predicting students academic performance using support vector machine. In: Proceedings—2019 Amity International Conference on Artificial Intelligence, AICAI 2019, pp. 756–759 (2019)
Damuluri, S., Islam, K., Ahmadi, P., Qureshi, N.: Analyzing navigational data and predicting student grades using support vector machine. Emerg. Sci. J. 4(4), 243–252 (2020)
Scikit-learn.sklearn.feature_selection.RFE. Available at https://scikit-learn.org/stable/module/generated/sklearn.feature_selection.RFE.html. Last accessed on 2019/10/24
Kaushik, S.: Introduction to Feature Selection Methods with an Example (or how to select the right variables?) (2016). Available at www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables. Last accessed on 2019/10/24
Irvanisam, I.: Multiple attribute decision making with simple additive weighting approach for selecting the scholarship recipients at Syiah Kuala University. In: 2017 International Conference on Electrical Engineering and Informatics (ICELTICs), Banda Aceh, pp. 245–250 (2017)
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Alarape, M.A., Ameen, A.O., Adewole, K.S. (2022). Hybrid Students’ Academic Performance and Dropout Prediction Models Using Recursive Feature Elimination Technique. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_9
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