Smart Online E-Learning Platform for Students using Educational Data Mining.
DOI:
https://doi.org/10.61841/tct9q686Keywords:
Educational Data Mining(EDM), instructor-led-non-gradedAbstract
The focal point of this examination was to utilize Educational Data Mining (EDM) methods to direct a quantitative investigation of understudy's communication with an e-learning framework through teacher-driven, non-evaluated, and reviewed courses. This activity is valuable for building up a rule for a progression of online short courses for them. A gathering of understudy's entrance conduct in an e-learning framework was dissected, and they were assembled by their course get to log records. The outcome indicated that the distinction in the learning situations could change the online access conduct of an understudy gathering. Enormous data technology is utilized here for the unstructured information that resembles recordings. The outcomes show that the understudies have a decent mechanical competency, have moderate competency in collaboration with learning substance, and have an absence of communication abilities with their learning network. A proposal to improve understudies' readiness in online cooperative learning is introduced
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1. Breiman, L. (1993). Better subset selection using the non-negative garotte. Technical Report. University of California, BerkeleChen, S., and Donoho, D. (1994). Basis pursuit. In 28th Asilomar Conf. Signals, Systems Computers, Asiloma
2. Efron, B., and Tibshirani, R. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
3. Lawson, C., and Hansen, R. (1974). Solving least squares problems. Englewood Cliffs: Prentice Hall
4. LeBlanc, M., and Tibshirani, R. (1994). Monotone shrinkage of trees. Technical Report. University of Toronto, Toront
5. Hastie, T., and Tibshirani, R. (1990). Generalized additive models. New York: Chapman and Hall
6. Zhang, P. (1993). Model selection via multifold CV. Ann. Statist., 21, 299-311.
7. A. Beimel, H. Brenner, S. P. Kasiviswanathan, and K. Nissim. Bounds on the sample complexity for private learning and private data release. Machine learning, 94(3), 2014.
8. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
9. J. C. Duchi, M. I. Jordan, and M. J. Wainwright. Local privacy and statistical minimax rates. In IEEE Symp. on Foundations of Computer Science (FOCS), 2013.
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