Abstract

The wide adoption of e-learning especially during and after the pandemic has given rise to the concern of learners’ motivation and involvement. E-leaner engagement level recognition over time has become critical since there is little to no physical interaction. In this paper, a benchmark dataset was utilized in predicting learners’ engagement levels in a blended e-learning system. Information Gain feature ranker was leveraged to ascertain the significance of the features. This study performed a comparative study on some machine learning algorithms including; Decision Tree, Naïve Bayes, Random Forest, Logistics Regression, Stochastic Gradient Descent, LogitBoost, Sequential Minimal Optimization, Voted Perceptron, and AdaptiveBoost. Each model was accessed using the 10-fold cross-validation. We measure the performance of the models before and after feature selection. The predictive results show that Sequential Minimal Optimization outperformed other models by attaining an accuracy of 90% with precision, recall, and f-measure values of 0.895, 0.897, and 0.895 respectively.

References

• A. Giannoulas, A. Stampoltzis, K. Kounenou, and A. Kalamatianos, “How Greek Students Experienced Online Education during Covid-19 Pandemic in Order to Adjust to a Post-Lockdown Period,” vol. 19, no. 4, pp. 222–232, 2021.
• R. Karasneh, S. Al-azzam, S. Muflih, S. Hawamdeh, and Y. Khader, “Attitudes and Practices of Educators Towards e-Learning During the COVID-19 Pandemic,” vol. 19, no. 4, pp. 252–261, 2021.
• A. M. Nortvig, A. K. Petersen, and S. H. Balle, “A literature review of the factors influencing e-learning and blended learning in relation to learning outcome, student satisfaction and engagement,” Electron. J. e-Learning, vol. 16, no. 1, pp. 45–55, 2018.
• R. Khandelwal and U. Kumar, “Available on : SSRN Applications of Artificial Neural Networks in E-Learning Personalization,” 2020.
• E. F. Buraimoh, “Predicting Student Success Using Student Engagement in the Online Component of a Blended-Learning Course,” pp. 1–65, 2021.
• T. Anderson, “Applications Of Machine Learning To Student Grade Prediction In Quantitative Business Courses,” vol. 1, no. 3, pp. 13–22, 2017.
• F. Junshuai, “Predicting Students’ Academic Performance with Decision and Neural Network,” Αγαη, vol. 8, no. 5, p. 55, 2019.
• J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Appl. Sci., vol. 10, no. 3, 2020, doi: 10.3390/app10031042.
• S. Leelavathy, R. Jaichandran, S. S. K, B. Surendar, A. K. Philip, and D. R. Ravindra, “Students Attention and Engagement Prediction Using Machine Learning Techniques,” vol. 7, no. 4, pp. 3011–3017, 2020.
• M. Hussain, W. Zhu, W. Zhang, and S. M. R. Abidi, “Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/6347186.
• G. Okereke, “A Machine Learning Based Framework for Predicting Student’s Academic Performance,” Phys. Sci. Biophys. J., vol. 4, no. 2, 2020, doi: 10.23880/psbj-16000145.
• G. Gorgun, S. N. Yildirim-Erbasli, and C. Demmans Epp, “Predicting Cognitive Engagement in Online Course Discussion Forums,” no. July, pp. 276–289, 2022, [Online]. Available: https://doi.org/10.5281/zenodo.6853149.
• K. Jawad, M. A. Shah, and M. Tahir, “Students’ Academic Performance and Engagement Prediction in a Virtual Learning Environment Using Random Forest with Data Balancing,” Sustain., vol. 14, no. 22, 2022, doi: 10.3390/su142214795.
• N. Alruwais and M. Zakariah, “Student-Engagement Detection in Classroom Using Machine Learning Algorithm,” Electron., vol. 12, no. 3, 2023, doi: 10.3390/electronics12030731.
• R. S. Olson, W. La Cava, P. Orzechowski, R. J. Urbanowicz, and J. H. Moore, “PMLB: A large benchmark suite for machine learning evaluation and comparison,” BioData Min., vol. 10, no. 1, pp. 1–13, 2017, doi: 10.1186/s13040-017-0154-4.
• M. R. Segal, “Machine Learning Benchmarks and Random Forest Regression Publication Date Machine Learning Benchmarks and Random Forest Regression,” Cent. Bioinforma. Mol. Biostat., p. 15, 2004, [Online]. Available: https://escholarship.org/uc/item/35x3v9t4.
• A. Moubayed, M. Injadat, A. Shami, and H. Lutfiyya, “Student Engagement Level in an e-Learning Environment: Clustering Using K-means,” Am. J. Distance Educ., vol. 34, no. 2, pp. 137–156, 2020, doi: 10.1080/08923647.2020.1696140.
• C. I. Eke, A. A. Norman, L. Shuib, and H. F. Nweke, “A Survey of User Profiling: State-of-the-Art, Challenges, and Solutions,” IEEE Access, vol. 7, pp. 144907–144924, 2019, doi: 10.1109/ACCESS.2019.2944243.
• H. Liu and B. Lang, “Machine learning and deep learning methods for intrusion detection systems: A survey,” Appl. Sci., vol. 9, no. 20, 2019, doi: 10.3390/app9204396.
• C. I. Eke, A. A. Norman, and L. Shuib, Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach, vol. 16, no. 6 June. 2021.
• A. S. Hashim, W. A. Awadh, and A. K. Hamoud, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020, doi: 10.1088/1757-899X/928/3/032019.
• H. Hassan, N. B. Ahmad, and S. Anuar, “Improved students’ performance prediction for multi-class imbalanced problems using hybrid and ensemble approach in educational data mining,” J. Phys. Conf. Ser., vol. 1529, no. 5, 2020, doi: 10.1088/1742-6596/1529/5/052041.
• M. B. Yildiz and C. Borekci, “Predicting Academic Achievement with Machine Learning Algorithms,” vol. 3, no. 3, 2020, doi: 10.31681/jetol.773206.
• B. K. Francis and S. S. Babu, “Predicting Academic Performance of Students Using a Hybrid Data Mining Approach,” J. Med. Syst., vol. 43, no. 6, 2019, doi: 10.1007/s10916-019-1295-4.
• P. Sun, M. D. Reid, and J. Zhou, “An improved multiclass LogitBoost using Adaptive-One-Vs-One,” Mach. Learn., vol. 97, no. 3, pp. 295–326, 2014, doi: 10.1007/s10994-014-5434-3.
• D. N. Lu, H. Q. Le, and T. H. Vu, “The factors affecting acceptance of e-learning: A machine learning algorithm approach,” Educ. Sci., vol. 10, no. 10, pp. 1–13, 2020, doi: 10.3390/educsci10100270.
• A. K. H. Ali Salah Hashim, Wid Akeel Awadh, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” 2020, doi: 10.1088/1757-899X/928/3/032019.