Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model
DOI:
https://doi.org/10.4108/eetiot.5369Keywords:
Hybrid Model, Grade Prediction, Stack ModelAbstract
With increasing technical procedures, academic institutions are adapting to a data-driven decision-making approach of which grade prediction is an integral part. The purpose of this study is to propose a hybrid model based on a stacking approach and compare its accuracy with those of the individual base models. The model hybridizes K-nearest neighbours, Random forests, XGBoost and multi-layer perceptron networks to improve the accuracy of grade prediction by enabling a combination of strengths of different algorithms for the creation of a more robust and accurate model. The proposed model achieved an average overall accuracy of around 90.9% for 10 epochs, which is significantly higher than that achieved by any of the individual algorithms of the stack. The results demonstrate the improvement of prediction results but using a stacking approach. This study has significant implications for academic institutions which can help them make informed grade predictions for the improvement of student outcomes.
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Raja, R. & Nagasubramani, P.. (2018). Impact of modern technology in education. Journal of Applied and Advanced Research. 3. 33. 10.21839/jaar.2018.v3iS1.165. DOI: https://doi.org/10.21839/jaar.2018.v3iS1.165
Richiţeanu-Năstase, Elena-Ramona & Stăiculescu, Camelia. (2018). University dropout. Causes and solution. 1. 71-75. DOI: https://doi.org/10.32437/mhgcj.v1i1.29
Baradwaj, B.K.; Pal, S. Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. 2012, 2, 63–69
Bain, S.; Fedynich, L.; Knight, M. The successful graduate student: A review of the factors for success. J. Acad. Bus. Ethics 2011, 3, 1.
Guo, Gongde & Wang, Hui & Bell, David & Bi, Yaxin. (2004). KNN Model-Based Approach in Classification. DOI: https://doi.org/10.1007/978-3-540-39964-3_62
Cutler, Adele & Cutler, David & Stevens, John. (2011). Random Forests. 10.1007/978-1-4419-9326-7_5. DOI: https://doi.org/10.1007/978-1-4419-9326-7_5
Chen, Tianqi and Carlos Guestrin. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016): n. pag. DOI: https://doi.org/10.1145/2939672.2939785
Popescu, Marius-Constantin & Balas, Valentina & Perescu-Popescu, Liliana & Mastorakis, Nikos. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems. 8.
Kurz, S., De Gersem, H., Galetzka, A. et al. Hybrid modeling: towards the next level of scientific computing in engineering. J. Math.Industry 12, 8 (2022). DOI: https://doi.org/10.1186/s13362-022-00123-0
Daniel, B. Big data and analytics in higher education: Opportunities and challenges. Br. J. Educ. Technol. 2015, 46, 904–920. DOI: https://doi.org/10.1111/bjet.12230
Namoun, A.; Alshanqiti, A. Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review. Appl. Sci. 2021, 11, 237. DOI: https://doi.org/10.3390/app11010237
Jayaprakash, Sujith & Krishnan, Sangeetha & V, Jaiganesh. (2020). Predicting Students Academic Performance using an Improved Random Forest Classifier. 238-243. 10.1109/ESCI48226.2020.9167547. DOI: https://doi.org/10.1109/ESCI48226.2020.9167547
Salal, Yass & Hussain, Mushtaq & Paraskevi, T.. (2021). Student Next Assignment Submission Prediction Using a Machine Learning Approach. 10.1007/978-3-030-71119-1_38. DOI: https://doi.org/10.1007/978-3-030-71119-1_38
Kanetaki, Zoe & Stergiou, Constantinos & Bekas, George & Troussas, Christos & Sgouropoulou, C.. (2022). A hybrid machine learning model for grade prediction in online engineering education. DOI: https://doi.org/10.3991/ijep.v12i3.23873
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016
Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937
Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052
Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603
Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579
Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484
Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470
Gong, Jing and Tao-An Chen. “Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes.” 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) (2022): 482-494. DOI: https://doi.org/10.1145/3524842.3528431
Samuels, Peter & Gilchrist, Mollie. (2014). Pearson Correlation.
Senthilnathan, Samithamby. (2019). Usefulness of Correlation Analysis. SSRN Electronic Journal. 10.2139/ssrn.3416918. DOI: https://doi.org/10.2139/ssrn.3416918
Muhammad Ali, Peshawa & Faraj, Rezhna. (2014). Data Normalization and Standardization: A Technical Report. 10.13140/RG.2.2.28948.04489.
Abdollahi, J., Nouri-Moghaddam, B. Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran J Comput Sci 5, 205–220 (2022). DOI: https://doi.org/10.1007/s42044-022-00100-1
Zhu, Qiuming A.. “On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset.” Pattern Recognit. Lett. 136 (2020): 71-80. DOI: https://doi.org/10.1016/j.patrec.2020.03.030
Grandini, Margherita et al. “Metrics for Multi-Class Classification: an Overview.” ArXiv abs/2008.05756 (2020): n. pag.
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