Machine Learning based Stream Selection of Secondary School Students in Bangladesh

Shabbir Ahmad, Md. Golam Rabiul Alam, Jia Uddin, Md Roman Bhuiyan, Tasnim Sakib Apon


In the Bangladeshi education system, there are three stages up to the secondary school certificate (SSC)- the primary (Primary Education Completion Certificate, or PEC), middle school (Junior School Certificate, or JSC), and SSC. A separate stream has to be chosen after the eighth grade, which could be any of the following streams: Science, Business Studies, and Humanities. The selection of a stream is very important for their future higher studies and career planning. Usually, students take the decision of selecting a stream based on PSC and JSC results only. To address this challenge, we have collected a dataset from different Bangladeshi schools, which consists of PSC and JSC students' records. There are 26 data for each student record including subject-wise student results, parent’s academic qualification, parent’s profession, parent’s monthly income, sibling information, district, etc. In the experimental analysis, a series of machine learning regression algorithms have been utilized. Moreover, we have employed various performance metrics in order to validate our model’s performance. The experimental results demonstrate that among the regressors, extreme gradient boosting algorithm’s performance were superior in both science and humanities streams. In the business stream however, Support Vector Machine’s performance is considerably better. It is expected that the analysis will help prospective students and stakeholders in their future decisions. Moreover, we have utilized Local Interpretable Model Agnostic Explanations that helps to increase the interpretability of the model.


Regression analysis; Stream recommendation system; secondary school.


P. Shruthi, and B. P. Chaitra. "Student performance prediction in education sector using data mining", 2016.

SSC Routine 2022 PDF All Education Board, July 2022. Available., Accessed July 31, 2022.

No science, arts or commerce in secondary education: A good idea?, November 24, 2020 (Accessed July 28, 2022). Available.

N. B., Sara, H. Rasmus, I. Christian, and A. Stephen, "High-school dropout prediction using machine learning: A Danish large-scale study." In ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence, pp. 319-24. 2015.

M.A. Rahman, "Factors Leading to Secondary School Dropout In Bangladesh: The Challenges to Meet the Sdg’s Targets." Journal of the Asiatic Society of Bangladesh, Science 47, no. 2, pp. 173-190, 2021.

M.N.I. Sarker, M. Wu, and M.A. Hossin, “Economic effect of school dropout in Bangladesh”, International journal of information and education technology, 9(2), pp.136-142, 2019.

M.S. Acharya, A. Armaan, and A.S. Antony, “A comparison of regression models for prediction of graduate admissions,” In 2019 international conference on computational intelligence in data science (ICCIDS) (pp. 1-5). IEEE, 2019.

O. El Aissaoui, Y. El Alami El Madani, L. Oughdir, A. Dakkak, and Y. El Allioui, “A multiple linear regression-based approach to predict student performance,” In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 9-23). Springer, Cham, 2019.

M.S. Zulfiker, N. Kabir, A.A. Biswas, P. Chakraborty, and M.M. Rahman, “Predicting students’ performance of the private universities of Bangladesh using machine learning approaches,” International Journal of Advanced Computer Science and Applications, 11(3), pp. 672-679, 2020.

R. Hasan, M.K.A. Ovy, I.Z. Nishi, M.A. Hakim, and R. Hafiz, “A Decision Support System of Selecting Groups (Science/Business Studies/Humanities) for Secondary School Students in Bangladesh,” In 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE, 2020.

N. Shahadat, M. Rahman, S. Ahmed, and B. Rahman, “Predicting higher secondary results by data mining algorithms with VBR: A feature reduction method,” In 4th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 164-169). IEEE, 2017.

K. Ahammad, P. Chakraborty, E. Akter, U.H. Fomey and S. Rahman, “A comparative study of different machine learning techniques to predict the result of an individual student using previous performances,” International Journal of Computer Science and Information Security (IJCSIS), 19(1), 2021.

K.M. Hasib, F. Rahman, R. Hasnat, and M.G.R. Alam, “A Machine Learning and Explainable AI Approach for Predicting Secondary School Student Performance,” In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0399-0405). IEEE 2022.

P. Cortez, and A.M.G., Silva, “Using data mining to predict secondary school student performance,” 2008.

P. Cortez, and M. Karagiannopoulos, D. Anyfantis, S.B. Kotsiantis, and P.E. Pintelas, “Feature selection for regression problems,” Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece, 2004.

Ramaswami, M. and Bhaskaran, R., “A CHAID based performance prediction model in educational data mining,” arXiv preprint arXiv:1002.114, 2010.

A. Sharma, and S. Dey, “A comparative study of feature selection and machine learning techniques for sentiment analysis,” In Proceedings of the 2012 ACM research in applied computation symposium (pp. 1-7), 2012.

C. Ma, B. Yao, F. Ge, Y. Pan, and Y. Guo, “Improving prediction of student performance based on multiple feature selection approaches,” In Proceedings of the 2017 International Conference on E-Education, E-Business and E-Technology (pp. 36-41), 2017.

M. Doshi, “Correlation based feature selection (CFS) technique to predict student Performance,” International Journal of Computer Networks & Communications, 6(3), p.197, 2014.

D. aulud, and A.M. bdulazeez, “Review on linear regression comprehensive in machine learning,” Journal of Applied Science and Technology Trends, 1(4), pp.140-147, 2020.

R. Costa-Mendes, T. Oliveira, M. Castelli, and F. Cruz-Jesus, “A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach,” Education and Information Technologies, 26(2), pp.1527-1547, 2021.

A.A. Elrahman, T.H.A. Soliman, A.I. Taloba, and M.F. Farghally, “A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook,” arXiv preprint arXiv:2203.03713, 2022.

D.P. Solomatine, D.L. Shrestha, “AdaBoost RT a boosting algorithm for regression problems,” In2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) 2004 Jul 25 (Vol. 2, pp. 1163-1168). IEEE.

A. Natekin, A. Knoll, “Gradient boosting machines, a tutorial. Frontiers in neurorobotics,” 2013 Dec 4; vol. 7, no. 21.

T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, “Xgboost: extreme gradient boosting. R package version 0.4-2. 2015 Aug 1; vol. 1, no. 4, pp. 1-4.

A De Myttenaere, B Golden, B Le Grand, F Rossi, “Mean absolute percentage error for regression models,”Neurocomputing. 2016 Jun 5;vol. 192, pp. 38-48.

KE O'Grady, “Measures of explained variance: Cautions and limitations,” Psychological Bulletin. 1982 Nov; vol. 92, no. 3, pp. 766.

MT Ribeiro, S Singh, C Guestrin, "Why should i trust you?," Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 2016 Aug 13, pp. 1135-1144.

Group prediction by Regressor-Dataset.xlsx. (n.d.). Google Docs.

M. S. I. Khan, N. Islam, J. Uddin, S. Islam, M. K. Nasir, “Water quality prediction and classification based on principal component regression and gradient boosting classifier approach,” Journal of King Saud UniversityComputer and Information Sciences, 2022, vol. 34,no. 8, pp. 4773-4781.

J. Uddin, F. N. Arko, N. Tabassum, T. R. Trisha, F. Ahmed, “Bangla sign language interpretation using bag of features and Support Vector Machine,” In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), 2017, pp. 1-4.

R. A. Khan, J. Uddin, S. Corraya, J. Kim, “Machine vision based indoor fire detection using static and dynamic features,” International Journal of Control and Automation, vol. 11, no. 6, pp. 87-98.

Full Text: PDF


  • There are currently no refbacks.


Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats