CFP last date
15 October 2024
Call for Paper
November Edition
IJAIS solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 15 October 2024

Submit your paper
Know more
Reseach Article

Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets

by God’swill Theophilus, Christopher Ifeanyi Eke
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: God’swill Theophilus, Christopher Ifeanyi Eke
10.5120/ijais2023451951

God’swill Theophilus, Christopher Ifeanyi Eke . Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets. International Journal of Applied Information Systems. 12, 41 ( Sep 2023), 23-32. DOI=10.5120/ijais2023451951

@article{ 10.5120/ijais2023451951,
author = { God’swill Theophilus, Christopher Ifeanyi Eke },
title = { Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2023 },
volume = { 12 },
number = { 41 },
month = { Sep },
year = { 2023 },
issn = { 2249-0868 },
pages = { 23-32 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number41/machine-learning-based-e-learners-engagement-level-prediction-using-benchmark-datasets/ },
doi = { 10.5120/ijais2023451951 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-14T17:32:45.357208+05:30
%A God’swill Theophilus
%A Christopher Ifeanyi Eke
%T Machine Learning-based E-Learners’ Engagement Level Prediction using Benchmark Datasets
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 23-32
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
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
  1. 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.
  2. 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.
  3. 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.
  4. R. Khandelwal and U. Kumar, “Available on : SSRN Applications of Artificial Neural Networks in E-Learning Personalization,” 2020.
  5. E. F. Buraimoh, “Predicting Student Success Using Student Engagement in the Online Component of a Blended-Learning Course,” pp. 1–65, 2021.
  6. T. Anderson, “Applications Of Machine Learning To Student Grade Prediction In Quantitative Business Courses,” vol. 1, no. 3, pp. 13–22, 2017.
  7. F. Junshuai, “Predicting Students’ Academic Performance with Decision and Neural Network,” Αγαη, vol. 8, no. 5, p. 55, 2019.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. N. Alruwais and M. Zakariah, “Student-Engagement Detection in Classroom Using Machine Learning Algorithm,” Electron., vol. 12, no. 3, 2023, doi: 10.3390/electronics12030731.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. M. B. Yildiz and C. Borekci, “Predicting Academic Achievement with Machine Learning Algorithms,” vol. 3, no. 3, 2020, doi: 10.31681/jetol.773206.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Data science applications in education E-learning Machine learning Engagement prediction Learning strategies.