Data Modeling
Mathematical and Computational Data Modeling
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Volume 3 Number 1 2022
RESEARCH ARTICLE (Open Access)
Beyond Grades: A Machine Learning-Based Decision Support System for Academic Stream Selection in Bangladeshi Secondary Schools
Md. Mobarak Hossain
Data Modeling 3 (1) 1-8 https://doi.org/10.25163/data.3110802
Submitted: 24 February 2022 Revised: 12 April 2022 Accepted: 20 April 2022 Published: 22 April 2022
Abstract
Choosing between Science, Business Studies, and Humanities is, for a thirteen-year-old in Bangladesh, a decision with outsized consequences—one that, more often than not, is still made on the basis of prior exam results, family expectations, or the lingering prestige attached to the Science stream. This review-style study examines an attempt to bring something more systematic to that process: a machine learning–based decision support system trained on data from 364 students across three institutions. Twelve relatively simple attributes—covering academic history, study habits, and personal preferences such as feared subjects or hobbies—were fed into three classifiers: Naïve Bayes, Sequential Minimal Optimization, and Random Forest. The results were, perhaps unsurprisingly, modest but encouraging: Random Forest edged ahead with 84.9% accuracy, followed closely by Naïve Bayes (82.76%) and SMO (80.88%), figures that compare reasonably against prior career-prediction work despite the considerably smaller dataset. What stands out is less the headline number than the underlying argument—that even a handful of easily collectible, non-academic indicators can meaningfully supplement (not replace) the guidance students currently receive. Whether this constitutes a genuine step toward more equitable, individualized stream allocation, or simply a promising proof of concept awaiting larger and more diverse data, remains an open question this paper invites readers to weigh for themselves. Keywords— Artificial Intelligence, Machine Learning, Education, Data Mining
References
Daily Star. (2019, May 6). 107 schools with 100% fail rates. The Daily Star. https://www.thedailystar.net/country/news/107-schools-100-fail-rates-1739488
Kolorob. (2020, January). SSC routine 2020 PDF all education board. https://kolorob.com.bd/ssc-routine-2020/
Guo, Q., Chen, M., An, D., & Ye, L. (2019). Prediction of students' course failure based on campus card data. In 2019 International Conference on Robots & Intelligent System (ICRIS) (pp. 361–364). IEEE.
Pedro, M. O., Baker, R., Bowers, A., & Heffernan, N. (2013). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In Educational Data Mining 2013.
Sara, N.-B., Halland, R., Igel, C., & Alstrup, S. (2015). 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. 319–324).
Verma, C., & Illés, Z. (2019). Attitude prediction towards ICT and mobile technology for the real-time: An experimental study using machine learning. In The International Scientific Conference eLearning and Software for Education (Vol. 3, pp. 247–254). "Carol I" National Defence University.
Romero, C., & Ventura, S. (2010). 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), 601–618.
Hussain, S., Muhsion, Z. F., Salal, Y. K., Theodoru, P., Kurtoglu, F., & Hazarika, G. (2019). Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning (iJET), 14(8), 4–22.
Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506.
Nazareno, A., Lopez, M., Gestiada, G., Martinez, M., & Roxas-Villanueva, R. (2019). An artificial neural network approach in predicting career strand of incoming senior high school students. Journal of Physics: Conference Series, 1245, 012005.
Bartnick, L., Kappelman, M., Berger, J., & Sigman, B. (1985). The value of the California Psychological Inventory in predicting medical students' career choice. Medical Education, 19(2), 143–147.
Ade, R., & Deshmukh, P. (2014). An incremental ensemble of classifiers as a technique for prediction of student's career choice. In 2014 First International Conference on Networks & Soft Computing (ICNSC2014) (pp. 384–387). IEEE.
Roy, K. S., Roopkanth, K., Teja, V. U., Bhavana, V., & Priyanka, J. (2018). Student career prediction using advanced machine learning techniques. International Journal of Engineering and Technology, 7(2.20).
Lichtenberger, E., & George-Jackson, C. (2013). Predicting high school students' interest in majoring in a STEM field: Insight into high school students' postsecondary plans. Journal of Career and Technical Education, 28(1), 19–38.
Saa, A. A. (2016). Educational data mining & students' performance prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212–220.
Kumar, M., & Salal, Y. K. (2019). Systematic review of predicting student's performance in academics. International Journal of Engineering and Advanced Technology, 8(3), 54.
Ramesh, V., Thenmozhi, P., & Ramar, K. (2012). Study of influencing factors of academic performance of students: A data mining approach. International Journal of Scientific & Engineering Research, 3(7), 1–5.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier.
Eekhout, I. (2019). Missing data methods: Imputation methods. https://www.iriseekhout.com/missing-data/missing-data-methods/imputation-methods
Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In AAAI (Vol. 90, pp. 223–228).
Taheri, S., & Mammadov, M. (2013). Learning the naive Bayes classifier with optimization models. International Journal of Applied Mathematics and Computer Science, 23(4), 787–795.
Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Hakim, M. A., Hasan, M. Z., Alam, M. M., Hasan, M. M., & Huda, M. N. (2019). An efficient modified bagging method for early prediction of brain stroke. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1–4). IEEE.
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