STUDENT DROPOUT PREDICTION USING MACHINE LEARNING
DOI:
https://doi.org/10.33003/fjs-2023-0706-2103Keywords:
Student, Dropout, Prediction, Machine LearningAbstract
In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn't much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.
References
Babatope, A.O. and Rachael, A.E., (2020). Production of Biolubricants from neem seed oil catalyzed by calcium oxide from snail shell. ACTA Technica Corviniensis Bulletin of Engineering. Pp. 1-8.
Balat M. (2008). Global trends on the processing of biofuel,International Journal of Green Energy, 5: 212–38.
Behçet R.(2011).Performance and emission study of waste anchovy fish biodiesel in a diesel engine, Fuel Processing Technology, 92: 1187–1194.
Birla, A. Singh, B., Upadhayau, S.N. and Sharma, Y.C. (2012). Kinetic studies of synthesis of Biodiesel from waste frying oil using a heterogeneous catalyst derived from snail shell. Bio resource Technology 106:95-100
Brennan, L., and Owendi, P. (2010). Biofuels from Microalgae- a review of technologies for production , processing and extraction of biofuel and co-products. Renewable Sustainable Energy Rev. 14(2) 557-577.
Cheng.J.J. and Timilsina G.R (2011). Status and barriers of advanced biofuels technologies: a review. Renew.Energy. 36(12), 3541-3549
Edlund,M. Visser,H. and Heitland,P.(2002) Analysis of biodiesel by argon–oxygen mixed-gas inductively coupled plasma optical emission spectrometry, Journal of AnalyticalAtomic Spectrometry, 17: 232–235.
Eslick,P.R.C.G.D. Howe,C. Smith,R. and Priest,A. Bensoussan,(2009).Benefits of fish oil supplementation in hyperlipidemia: a systematic review and meta analysis,International Journal of Cardiology,136,4-16.
Gao, Y., Skutsch, M., Masera, O., and Pacheco, P. (2011). A global analysis of deforestation due to biofuel development.Center for International Forestry Research CIFOR.
Gumba, R.E., Saalah, S., Mission, M., Ongkudon, C.M and Anton A. (2016). Green biodiesel production: a review on feedstock, catalyst, monolithic reactor and supercritical fluid technology. Biofuel Research Journal (11) 431-477.
Hossain, S.A, Salleh B.M, Boyce A, Chowdhury A.N and Naqiuddin M. (2008). Biodiesel Fuel Production From Algae as Renewable Energy. American Journal of Biochemistry and Biotechnology. 4(3):250-251
Kahn,J. Rang,H. and Kriis,J. (2002).Advance in biodiesel fuel research, Proceedings of the Estonian Academy of Sciences Chemistry, 51: 75–117.
Lin, C.Y. and Lin,H.A. (2006).Diesel engine performance and emission characteristics of biodiesel produced by the peroxidation process, Fuel, 85: 298–305.
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