- Abdallah and O. Abdallah, “INVESTIGATING FACTORS AFFECTING STUDENTS’ SATISFACTION WITH E-LEARNING: AN EMPIRICAL CASE STUDY,” J. Educ. Online, vol. 19, no. 1, 2022, doi: 10.9743/jeo.2022.19.1.3.
- Z. Al Rawashdeh, E. Y. Mohammed, A. R. Al Arab, M. Alara, and B. Al-Rawashdeh, “Advantages and disadvantages of using E-learning in university education: Analyzing students’ perspectives,” Electron. J. e-Learning, vol. 19, no. 2, 2021, doi: 10.34190/ejel.19.3.2168.
- Hooda, “Learning Analytics Lens: Improving Quality of Higher Education,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, 2020, doi: 10.30534/ijeter/2020/24852020.
- Mustafa, “The personalization of e-learning systems with the contrast of strategic knowledge and learner’s learning preferences: an investigatory analysis,” Appl. Comput. Informatics, vol. 17, no. 1, 2021, doi: 10.1016/j.aci.2018.08.001.
- Essalmi, L. J. Ben Ayed, M. Jemni, Kinshuk, and S. Graf, “A fully personalization strategy of E-learning scenarios,” Comput. Human Behav., vol. 26, no. 4, 2010, doi: 10.1016/j.chb.2009.12.010.
- S. Rajper, N. A. Shaikh, Z. A. Shaikh, and G. A. Mallah, “Automatic Detection of Learning Styles on Learning Management Systems using Data Mining Technique,” Indian J. Sci. Technol., vol. 9, no. 15, 2016, doi: 10.17485/ijst/2016/v9i15/85959
- Dwi Yuniarti, E. Winarko, and A. Musdholifah, “Data mining for student assessment in e-leaming: A survey,” 2020, doi: 10.1109/ICIC50835.2020.9288533.
- Krishnan et al., “Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model,” Sustain., vol. 14, no. 6, 2022, doi: 10.3390/su14063378.
- Maggioni, “What is ... Data Mining,” Bull. Am. Math. Soc., vol. 59, no. 4, 2012.
- Neelamegam and E. Ramaraj, “Classification algorithm in Data mining : An Overview,” Int. J. P2P Netw. Trends Technol., vol. 4, no. 8, 2013.
- H. Liao, P. H. Chu, and P. Y. Hsiao, “Data mining techniques and applications - A decade review from 2000 to 2011,” Expert Systems with Applications, vol. 39, no. 12. 2012, doi: 10.1016/j.eswa.2012.02.063.
- Cherkassky and F. Mulier, from Data: Concepts, Theory and Methods. Wiley-IEEE Press, 2007.
- Gupta, A. Rawat, A. Jain, A. Arora, and N. Dhami, “Analysis of Various Decision Tree Algorithms for Classification in Data Mining,” Int. J. Comput. Appl., vol. 163, no. 8, 2017, doi: 10.5120/ijca2017913660.
- J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121–167, 1998, doi: 10.1023/A:1009715923555.
- E. Celebi and K. Aydin, Unsupervised learning algorithms. Springer, 2016.
- MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, vol. 1, pp. 281–296.
- Goldberg, “Introduction to semi-supervised learning,” Synth. Lect. Artif. Intell. Mach. Learn., vol. 6, pp. 1–116, 2009, doi: 10.2200/S00196ED1V01Y200906AIM006.
- E. van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Mach. Learn., 2020, doi: 10.1007/s10994-019-05855-6.
- Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning. Adaptive Computation and Machine Learning. 2010.
- Gharebaghi and A. Amiri, “LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2021.3139929.
- W. T. Fok et al., “Prediction model for students’ future development by deep learning and tensorflow artificial intelligence engine,” 2018, doi: 10.1109/INFOMAN.2018.8392818.
- K. Hamoud, “Selection of Best Decision Tree Algorithm for Prediction and Classification of Students’ Action,” Am. Int. J. Res. Sci. Technol. Eng. Math., vol. 16, no. 1, 2016.
- Huang and N. Fang, “Work in progress - Prediction of students’ academic performance in an introductory engineering course,” 2011, doi: 10.1109/FIE.2011.6142729.
- Raihana and A. M. Farah Nabilah, “Classification of students based on quality of life and academic performance by using support vector machine,” J. Acad. Univ. Teknol. MARA Negeri Sembilan, vol. 6, no. 1, 2018.
- Hasan, S. Palaniappan, A. R. A. Raziff, S. Mahmood, and K. U. Sarker, “Student Academic Performance Predicti on by using Decision Tree Algorithm,” 2018, doi: 10.1109/ICCOINS.2018.8510600.
- P. R. Vital, K. Sangeeta, and K. K. Kumar, “Student classification based on cognitive abilities and predicting learning performances using machine learning models,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, 2021, doi: 10.12785/ijcds/100107.
- Korting, “C4. 5 algorithm and Multivariate Decision Trees,” Image Process. Div. Natl. Inst. Sp. , no. Section 2, 2010.
- QualtricsXM, “What is ANOVA (Analysis Of Variance ) and what can I use it for?,” Manag., 2020.
|
