Vol. 4 No. 2 (2024): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Academic Advisor Prediction of Undergraduates Students Semester Final's Mark with Contextual Feedback Using Machine Learning Approach

PDF
  • Syeda Farjana Shetu,
  • Fatema Tuj Johora,
  • Md. Mehedi Hasan,
  • Marzan Tasnim Oyshi,
  • Ms. Nazmun Nessa Moon
Syeda Farjana Shetu
Daffodil International University
Actual journals and last information about wireless communication

Published 31.07.2024

Copyright (c) 2024 Syeda Farjana Shetu; Fatema Tuj Johora, Md. Mehedi Hasan, Marzan Tasnim Oyshi, Ms. Nazmun Nessa Moon (Co-Author)

Creative Commons License

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

Abstract

The aim of this research is to predict undergraduate students' academic performance using machine learning techniques. With the increasing availability of instructional data, there is a growing potential to utilize this information for educational purposes. Machine learning has become a common approach to predicting student performance, which can be beneficial for improving teaching strategies and student outcomes. This study focused on identifying challenges faced by graduate students who have low academic performance, and how their future performance can be predicted using historical data. The dataset used in this study was collected from a reputable academic institution and analyzed using various machine learning algorithms, such as Decision Trees, Random Forests, Support Vector Machines, Gradient Boosters, Linear Regressions, and Neural Network Regressions. The most effective algorithm was used to predict students' final semester grades. Feedback and suggestions for improvement were provided to students based on their predicted grades. The proposed system, named Academic Advisor, acts as a coach or guide for students, displaying their current academic status and providing customized targets to help them achieve better grades. This research can help educators and institutions improve their teaching strategies and enhance students' academic performance by utilizing machine learning techniques.

PDF

References

  1. Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (n.d.). Predicting student performance: an application of data mining methods with an educational web-based system. 33rd Annual Frontiers in Education, 2003. FIE 2003. doi:10.1109/fie.2003.1263284
  2. Kotsiantis, S. B., & Pintelas, P. E. (2005). Predicting students marks in Hellenic Open University. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05). doi:10.1109/icalt.2005.223
  3. Ross, M., Graves, C. A., Campbell, J. W., & Kim, J. H. (2013). Using Support Vector Machines to Classify Student Attentiveness for the Development of Personalized Learning Systems. 2013 12th International Conference on Machine Learning and Applications. doi:10.1109/icmla.2013.66
  4. Hamsa, H., Indiradevi, S. and Kizhakkethottam, J.J.: Student academic performance prediction model using decision tree and fuzzy genetic algorithm. In: Procedia Technology, 25:326–332, 2016.
  5. Mohan, M. G. M., Augustin, S. K., & Roshni, V. S. K. (2015). A BigData approach for classification and prediction of student result using MapReduce. 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS). doi:10.1109/raics.2015.7488404
  6. Acharya, A., and Sinha, D.: Early prediction of student’s performance using machine learning techniques. In: International Journal of Computer Applications, 107(1), 2014.
  7. Khan, B., Khiyal, M.S.H., and Khattak, M.D.: Final grade prediction of secondary school student using decision tree. International Journal of Computer Applications, 115(21), 2015.
  8. Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527-1543.
  9. Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3-12.
  10. Saleh, M. A., Palaniappan, S., & Abdalla, N. A. A. (2021). Education is An Overview of Data Mining and The Ability to Predict the Performance of Students. Edukasi, 15(1).
  11. Ramdas, B. R., Machhindra, M. B., Kailas, W. P., & Raut, M. V. (2019). Tracking and Predicting Student Performance Using Machine Learning.
  12. Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250.
  13. Geetha, R., Padmavathy, T., & Anitha, R. (2021). Prediction of the academic performance of slow learners using efficient machine learning algorithm. Advances in Computational Intelligence, 1(4), 1- 12.
  14. Shingari, I., & Kumar, D. (2018). Predicting Student Performance Using Classification Data Mining Techniques. International Journal of Computer Sciences and Engineering, 43-48.
  15. Felix, C., & Sobral, S. R. (2020, April). Predicting students’ performance using survey data. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1017-1023). IEEE.
  16. Altabrawee, H., Ali, O. A. J., & Ajmi, S. Q. (2019). Predicting students’ performance using machine learning techniques. JOURNAL OF UNIVERSITY OF BABYLON for pure and applied sciences, 27(1), 194-205.
  17. Sujatha, G., Sindhu. S., & Savaridassan, P. (2018). Predicting students performance using personalized analytics. International Journal of Pure and Applied Mathematics, 119(12), 229-238.
  18. Xu, J., Moon, K. H., & Van Der Schaar, M. (2017). A machine learning approach for tracking and predicting student performance in degree programs. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742-753.
  19. Muñoz-Bullón, F., Sanchez-Bueno, M. J., & Vos-Saz, A. (2017). The influence of sports participation on academic performance among students in higher education. Sport Management Review, 20(4), 365- 378
  20. Shetu, S. F., Saifuzzaman, M., Moon, N. N., Sultana, S., & Yousuf, R. (2020). Student’s Performance Prediction Using Data Mining Technique Depending on Overall Academic Status and Environmental Attributes. 3rd International Conference on Innovative Computing & Communications (ICICC) 2020, Advances in Intelligent Systems and Computing, 757–769. https://doi.org/10.1007/978-981-15-5148-2_66 Indexing: ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar, and Springerlink
  21. Vapnik, V.: The nature of statistical learning theory. In: Springer science & business media, 2013.
  22. Jin, C., De-lin, L., & Fen-xiang, M. (2009). An improved ID3 decision tree algorithm. 2009 4th International Conference on Computer Science & Education, 127-130.
  23. Gislason, P.O., Benediktsson, J.A. and Sveinsson, J.R.: Random forests for land cover classification. In: Pattern recognition letters, 27(4):294– 300, 2006.
  24. KangaraniFarahani, M., & Mehralian, S. (2013). Comparison between Artificial Neural Network and neuro-fuzzy for gold price prediction. 2013 13th Iranian Conference on Fuzzy Systems (IFSC). doi:10.1109/ifsc.2013.6675635
  25. K., S., V., S., & R., R. (2016). A comparative analysis on linear regression and support vector regression. 2016 Online International Conference on Green Engineering and Technologies (IC-GET), 1-5.
  26. Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. doi:10.1016/j.trc.2015.02.019