DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES

Atika Islam

atika.islam@riphah.edu.pk
Riphah International University Lahore, Riphah School of Computing and Innovation (RSCI) (Pakistan)
https://orcid.org/0009-0002-5400-7841

Faisal Bukhari


University of The Punjab Lahore, Faculty of Computing and Information Technology (FCIT) (Pakistan)
https://orcid.org/0000-0002-7703-9742

Muhammad Awais Sattar


Riphah International University (Pakistan)
https://orcid.org/0000-0002-2431-8182

Ayesha Kashif


Riphah International University Lahore, Riphah School of Computing and Innovation (RSCI) (Pakistan)

Abstract

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.


Keywords:

Educational Data Mining, Learning Analytics, Random Forest, Support Vector Classifier

[1] Akour I. et al.: Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach. JMIR Medical Education 7, 2021, e24032.
DOI: https://doi.org/10.2196/24032   Google Scholar

[2] Altabrawee H., Ali O. A. J., Ajmi S. Q.: Predicting students’ performance using machine learning techniques. Journal of University of Babylon for pure and applied sciences 27, 2019, 194–205.
DOI: https://doi.org/10.29196/jubpas.v27i1.2108   Google Scholar

[3] Aman F. et al.: A predictive model for predicting students academic performance. 10th International Conference on Information, Intelligence, Systems and Applications – IISA. IEEE, 2019, 1–4.
DOI: https://doi.org/10.1109/IISA.2019.8900760   Google Scholar

[4] Arnold K. E., Pistilli M. D.: Course signals at Purdue: Using learning analytics to increase student success. 2nd International Conference on Learning Analytics and Knowledge, 2012, 267–270.
DOI: https://doi.org/10.1145/2330601.2330666   Google Scholar

[5] Baraniuk R.: Open education: New opportunities for signal processing. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP, 2015.
  Google Scholar

[6] Bhardwaj B. K., Pal S.: Data Mining: A prediction for performance improvement using classify cation. arXiv preprint arXiv:1201.3418, 2012.
  Google Scholar

[7] Bhutto E. S. et al.: Predicting students’ academic performance through supervised machine learning. International Conference on Information Science and Communication Technology – ICISCT. IEEE, 2020, 1–6.
DOI: https://doi.org/10.1109/ICISCT49550.2020.9080033   Google Scholar

[8] Borge N.: Artificial intelligence to improve education/learning challenges. International Journal of Advanced Enginering & Innovative Technology – IJAEIT 2, 2016, 10–13.
  Google Scholar

[9] Chaudhury P. et al.: Enhancing the capabilities of student result prediction system. Second International Conference on Information and Communication Technology for Competitive Strategies, 2016, 1–6.
DOI: https://doi.org/10.1145/2905055.2905150   Google Scholar

[10] Clow D.: An overview of learning analytics. Teaching in Higher Education 2013, 18, 683–695.
DOI: https://doi.org/10.1080/13562517.2013.827653   Google Scholar

[11] Ever Y. K., Dimililer K.: The effectiveness of a new classification system in higher education as a new e-learning tool. Quality & Quantity 52, 2018, 573–582.
DOI: https://doi.org/10.1007/s11135-017-0636-y   Google Scholar

[12] Gray G., McGuinness C., Owende P.: An application of classification models to predict learner progression in tertiary education. IEEE International Advance Computing Conference – IACC. IEEE, 2014, 549–554.
DOI: https://doi.org/10.1109/IAdCC.2014.6779384   Google Scholar

[13] Huang S., Fang N.: Work in progress: Early prediction of students’ academic performance in an introductory engineering course through different mathematical modeling techniques. Frontiers in Education Conference Proceedings. IEEE, 2012, 1–2.
DOI: https://doi.org/10.1109/FIE.2012.6462242   Google Scholar

[14] Kolo D. K., Adepoju S. A., Alhassan J. K.: A decision tree approach for predicting students academic performance. I.J. Education and Management Engineering 5, 2015, 12–19.
DOI: https://doi.org/10.5815/ijeme.2015.05.02   Google Scholar

[15] Kotsiantis S. B.: Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review 37, 2012, 331–344.
DOI: https://doi.org/10.1007/s10462-011-9234-x   Google Scholar

[16] Mueen A., Zafar B., Manzoor U.: Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. International Journal of Modern Education & Computer Science 8, 2016.
DOI: https://doi.org/10.5815/ijmecs.2016.11.05   Google Scholar

[17] Osmanbegovic E., Suljic M.: Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business 10, 2012, 3–12.
  Google Scholar

[18] Oyedeji A. O. et al.: Analysis and prediction of student academic performance using machine learning. JITCE (Journal of Information Technology and Computer Engineering) 4, 2020, 10–15.
DOI: https://doi.org/10.25077/jitce.4.01.10-15.2020   Google Scholar

[19] Rachburee N., Punlumjeak W.: A comparison of feature selection approach between greedy, IG-ratio, Chi-square, and mRMR in educational mining. 7th International Conference on Information Technology and Electrical Engineering – ICITEE. IEEE, 2015, 420–424.
DOI: https://doi.org/10.1109/ICITEED.2015.7408983   Google Scholar

[20] Romero C., Ventura S.: Educational data mining: A survey from 1995 to 2005. Expert systems with applications 33, 2007, 135–146.
DOI: https://doi.org/10.1016/j.eswa.2006.04.005   Google Scholar

[21] Said M. A., Idris M., Hussain S.: Relationship between Social Behaviour and Academic Performance of Students at Secondary Level in Khyber Pakhtunkhwa. Pakistan Journal of Distance and Online Learning 4, 2018, 153–170.
  Google Scholar

[22] Sekeroglu B., Dimililer K., Tuncal K.: Student performance prediction and classification using machine learning algorithms. 8th International Conference on Educational and Information Technology, 2019, 7–11.
DOI: https://doi.org/10.1145/3318396.3318419   Google Scholar

[23] Singh A., Halgamuge M. N., Lakshmiganthan R.: Impact of different data types on classifier performance of random forest, naive bayes, and k-nearest neighbors algorithms. International Journal of Advanced Computer Science and Applications 8, 2017.
DOI: https://doi.org/10.14569/IJACSA.2017.081201   Google Scholar

[24] Thammasiri D. et al.: A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Systems with Applications 41, 2014, 321–330.
DOI: https://doi.org/10.1016/j.eswa.2013.07.046   Google Scholar

[25] Wolff A. et al.: Developing predictive models for early detection of at-risk students on distance learning modules. LAK Workshops, 2014.
  Google Scholar

Download


Published
2024-09-30

Cited by

Islam, A., Bukhari, F., Awais Sattar, M., & Kashif, A. (2024). DETERMINING STUDENT’S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 109–117. https://doi.org/10.35784/iapgos.6173

Authors

Atika Islam 
atika.islam@riphah.edu.pk
Riphah International University Lahore, Riphah School of Computing and Innovation (RSCI) Pakistan
https://orcid.org/0009-0002-5400-7841

Authors

Faisal Bukhari 

University of The Punjab Lahore, Faculty of Computing and Information Technology (FCIT) Pakistan
https://orcid.org/0000-0002-7703-9742

Authors

Muhammad Awais Sattar 

Riphah International University Pakistan
https://orcid.org/0000-0002-2431-8182

Authors

Ayesha Kashif 

Riphah International University Lahore, Riphah School of Computing and Innovation (RSCI) Pakistan

Statistics

Abstract views: 92
PDF downloads: 40


License

Creative Commons License

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