A PREDICITON MODEL BASED ON STUDENTS’S BEHAVIOR IN E-LEARNING ENVIRONMENTS USING DATA MINING TECHNIQUES
Keywords:Data Mining, E-Learning, Prediction, User behaviour, CRISP-DM
E-Learning has become an essential teaching approach during the COVID-19 pandemic. All over the world, various internet-based learning management systems (Google classroom, Moodle, etc.) were adopted to convey knowledge and enhance learning outcomes. However, measuring learning outcomes and knowledge acquisition in E-Learning environment is a controversial issue. To this end, this paper aims to predict learning outcomes using data mining techniques. Student data are collected and analyzed to construct the prediction model. The collected data covered students from various undergraduate studies. Cross-Industry Standard Process for Data Mining is used as a research model. The obtained result shows the significant of some attributes in predicting learning outcomes. Four correlation-based attributes selection schemas are applied. The selected attributes are examined using four data mining algorithms: random forest, k-nearest neighbors, Decision Tree, and neural network. The overall performance of the constructed mining models is evaluated using various performance measures: Accuracy, Precision, Recall and F1-score are calculated. Overall, an 86% accuracy is secured.
T. Favale, F. Soro, M. Trevisan, I. Drago, and M. Mellia, “Campus traffic and e-Learning during COVID-19 pandemic,” Comput. networks, vol. 176, p. 107290, 2020.
S.-H. Kim and S. Park, “Influence of learning flow and distance e-learning satisfaction on learning outcomes and the moderated mediation effect of social-evaluative anxiety in nursing college students during the COVID-19 pandemic: A cross-sectional study,” Nurse Educ. Pract., vol. 56, p. 103197, 2021.
A. M. Maatuk, E. K. Elberkawi, S. Aljawarneh, H. Rashaideh, and H. Alharbi, “The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors,” J. Comput. High. Educ., vol. 34, no. 1, pp. 21–38, 2022.
L. Yekefallah, P. Namdar, R. Panahi, and L. Dehghankar, “Factors related to students’ satisfaction with holding e-learning during the Covid-19 pandemic based on the dimensions of e-learning,” Heliyon, vol. 7, no. 7, p. e07628, 2021.
N. D. Oye, M. Salleh, and N. A. Iahad, “E-learning methodologies and tools,” Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 2, 2012.
K. Mukhtar, K. Javed, M. Arooj, and A. Sethi, “Advantages, Limitations and Recommendations for online learning during COVID-19 pandemic era,” Pakistan J. Med. Sci., vol. 36, no. COVID19-S4, p. S27, 2020.
S. Zarei and S. Mohammadi, “Challenges of higher education related to e-learning in developing countries during COVID-19 spread: a review of the perspectives of students, instructors, policymakers, and ICT experts,” Environ. Sci. Pollut. Res., pp. 1–7, 2021.
A. H. Al-Maqbali and R. M. Raja Hussain, “The impact of online assessment challenges on assessment principles during COVID-19 in Oman,” J. Univ. Teach. Learn. Pract., vol. 19, no. 2, pp. 73–92, 2022.
Z. Bilici and D. Özdemir, “Data Mining Studies in Education: Literature Review For The Years 2014-2020,” Bayburt Eğitim Fakültesi Derg., vol. 17, no. 33, pp. 342–376, 2022.
W. Xiao, P. Ji, and J. Hu, “A survey on educational data mining methods used for predicting students’ performance,” Eng. Reports, 2021.
J. Demšar, B. Zupan, G. Leban, and T. Curk, “Orange: From experimental machine learning to interactive data mining,” in European conference on principles of data mining and knowledge discovery, 2004, pp. 537–539.
D. F. Murad, R. Hassan, W. Wahi, and B. D. Wijanarko, “A User-Item Collaborative Filtering System to Predict Online Learning Outcome,” 2020.
T. T. Mai, M. Bezbradica, and M. Crane, “Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data,” Futur. Gener. Comput. Syst., vol. 127, pp. 42–55, 2022.
H. Zeineddine, U. Braendle, and A. Farah, “Enhancing prediction of student success: Automated machine learning approach,” Comput. Electr. Eng., vol. 89, p. 106903, 2021.
S. Chayanukro, M. Mahmuddin, and H. Husni, “Understanding and assembling user behaviours using features of Moodle data for eLearning usage from performance of course-student weblog,” in Journal of Physics: Conference Series, 2021, vol. 1869, no. 1, p. 12087.
Y. Luo, N. Chen, and X. Han, “Students’ Online Behavior Patterns Impact on Final Grades Prediction in Blended Courses,” in 2020 Ninth International Conference of Educational Innovation through Technology (EITT), 2020, pp. 154–158.
C. Neto, M. Brito, V. Lopes, H. Peixoto, A. Abelha, and J. Machado, “Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients,” Entropy, vol. 21, no. 12, p. 1163, 2019.
U. Thange, V. K. Shukla, R. Punhani, and W. Grobbelaar, “Analyzing COVID-19 Dataset through Data Mining Tool ‘Orange,’” in 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), 2021, pp. 198–203.
G. Taranto-Vera, P. Galindo-Villardón, J. Merchán-Sánchez-Jara, J. Salazar-Pozo, A. Moreno-Salazar, and V. Salazar-Villalva, “Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature,” J. Supercomput., vol. 77, no. 10, pp. 11481–11513, 2021.
G. D. Boca, “Factors influencing students’ behavior and attitude towards online education during COVID-19,” Sustainability, vol. 13, no. 13, p. 7469, 2021.
R. R. Estacio and R. C. Raga Jr, “Analyzing students online learning behavior in blended courses using Moodle,” Asian Assoc. Open Univ. J., 2017.
M. Kaushik, R. Sharma, S. A. Peious, M. Shahin, S. Ben Yahia, and D. Draheim, “A systematic assessment of numerical association rule mining methods,” SN Comput. Sci., vol. 2, no. 5, pp. 1–13, 2021.
C. Gonçalves, D. Ferreira, C. Neto, A. Abelha, and J. Machado, “Prediction of mental illness associated with unemployment using data mining,” Procedia Comput. Sci., vol. 177, pp. 556–561, 2020.
V. Giglioni, E. García-Macías, I. Venanzi, L. Ierimonti, and F. Ubertini, “The use of receiver operating characteristic curves and precision-versus-recall curves as performance metrics in unsupervised structural damage classification under changing environment,” Eng. Struct., vol. 246, p. 113029, 2021.
M. W. Rodrigues, S. Isotani, and L. E. Zarate, “Educational Data Mining: A review of evaluation process in the e-learning,” Telemat. Informatics, vol. 35, no. 6, pp. 1701–1717, 2018.
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci. (Ny)., vol. 507, pp. 772–794, 2020.
Y. Rodriguez-Ortega, D. M. Ballesteros, and D. Renza, “Copy-move forgery detection (CMFD) using deep learning for image and video forensics,” J. Imaging, vol. 7, no. 3, p. 59, 2021.
S. Kumar and I. Chong, “Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states,” Int. J. Environ. Res. Public Health, vol. 15, no. 12, p. 2907, 2018.
How to Cite
Copyright (c) 2022 Anwar Adnan Alnawas, Mohammed Al-Jawad, Hasanein Alharbi
This work is licensed under a Creative Commons Attribution 4.0 International License.