Predicting Online Social Network Student Performance Using Enhanced RandomBayesian Algorithm


  • S. Senthamaraiselvi Research Scholar (Part Time), Dept. of Computer Science, Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India
  • K. Meenakshi Sundaram Associate Professor, Dept. of Computer Science, Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India


Data mining, Classification, Random Forest, Feature Selection


Online social networking sites, or OSNs, have become immensely popular in recent times. Many websites focus on locating and sharing various kinds of content as well as on making and keeping contacts. Statistical techniques for exploring data in educational settings and analyzing student performance are less effective than educational data mining. The research aims to employ various data mining approaches to investigate the effects of distinct variables on students' performance. This paper presents an Improved Mutual information based Filter Pearson’s Correlation (IMIFPC) feature selection with Enhanced RandomBayesian (ERB) classification method for predicting the student performance. IMIFPC is a feature selection technique that uses combination of Filter method of Pearson’s correlation with Mutual information method. The real-time OSN user dataset were used to test the proposed ERB approach to predict the students performance with classes (Excel and Vivekanandha) based on the data mining methods. The experimental results from the OSN User dataset demonstrate that the suggested approach using ERB classification performance achieved Accuracy of 98.00 and F1-Score of 97.99 percent.


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W. W. Yaacob, N. M. Sobri, S. M. Nasir, N. D. Norshahidi, and W. W. Husin, “Predicting student drop-out in higher institution using data mining techniques,” Journal of Physics: Conference Series, vol. 1496, p. 012005, 2020.

P. Sokkheyand and T. Okazaki, “Developing web-based support systems for predicting poor-performing students using educational data mining techniques,” Studies, vol. 11, no. 7, 2020.

Xin Chen, Mihaela and Krishna P.C, “Mining Social Media Data for Understanding Students Learning Experiences”, IEEE Transactions on Learning Technologies, 2014.

X. Xu, J. Wang, H. Peng, and R. Wu, “Prediction of academic performance associated with internet usage behaviors using machine learning algorithms,” Computers in Human Behavior, vol. 98, pp. 166–173, 2019.

A. Alhassan, B. Zafar, and A. Mueen, “Predict students’ academic performance based on their assessment grades and online activity data,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 4, 2020

Siemens and P. Long, “Penetrating the fog: Analytics in learning and education,” Educause Review, vol. 46, no. 5, pp. 30–32, 2011.

Boateng, R., & Amankwaa, A. (2016). The impact of social media on student academic life in higher education. Global Journal of Human-Social Science, 16(4), 1-8.

Amrieh EA, Hamtini T, Aljarah I. “Mining educational data to predict student’s academic performance using ensemble methods.” International Journal of Database Theory and Application. 2016;9(8):119–136.

Saheed YK, Oladele TO, Akanni AO, Ibrahim WM. “Student performance prediction based on data mining classification techniques.” Nigerian Journal of Technology. 2018;37(4):1087.

Hussain S, Muhsion ZF, Salal YK, Theodoru P, Kurtouglu F, Hazarika GC. “Prediction model on student performance based on internal assessment using deep learning.” International Journal of Emerging Technologies in Learning ({IJET}). 2019;14(08):4.

Naicker N, Adeliyi T, Wing J. “Linear support vector machines for prediction of student performance in school-based education.” Math Probl Eng. 2020

Kumar M, Sharma C, Sharma S, Nidhi N, Islam N. “Analysis of feature selection and data mining techniques to predict student academic performance.” In 2022 International Conference on Decision Aid Sciences and Applications (DASA), IEEE. 2022:1013–1017.

Stephen Opoku Oppong, "Predicting Students’ Performance Using Machine Learning Algorithms: A Review", Asian Journal of Research in Computer Science, Volume 16, Issue 3, 2023.

Jitender Kumar, Ritu Vashistha, Kushwant Kaur and Siroj Kumar Singh, "Machine Learning Techniques of Predicting Student's Performance", International Conference in Advances in Power, Signal, and Information Technology (APSIT), IEEE, 2023.

S. Senthamaraiselvi and K. Meenakshi Sundaram, "Reprecussion of Social Media in the Area of Education", Inernational Conference on Advanced Computing (ICAC), 2023.

S. Senthamaraiselvi and K. Meenakshi Sundaram, "A study on social networking usage amoung students", 4th International Conference on Artificial Intelligence Trending Towards Automation, 2023.




How to Cite

Senthamaraiselvi, S. ., & Sundaram, K. M. . (2023). Predicting Online Social Network Student Performance Using Enhanced RandomBayesian Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 384–390. Retrieved from



Research Article