The use of Machine Learning Techniques in a Web-Based Learning Diagnosis System Program
Sunil Chandolu1, S. Prasad Babu Vagolu2, D.Usharajeswari3

1Sunil Chandolu*, Dept. of CS, GIS, GITAM( Deemed To Be University. Visakhapatnam.
2S. Prasad Babu Vogolu, Dept. of CS, GIS, GITAM( Deemed To Be University).
3D.Usharajeswari, Dept. of CS, GIS, GITAM( Deemed To Be University.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on March 30, 2020. | PP: 4861-4865 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8231038620/2020©BEIESP | DOI: 10.35940/ijrte.F8231.038620

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Abstract: This work proposes a canny learning finding framework that bolsters a Web-based topical learning model, which expects to develop students’ capacity of information incorporation by giving the students the chances to choose the learning themes that they are intrigued, and gain information on the particular subjects by surfing on the Internet to look through related adapting course-product and examining what they have realized with their associates. In view of the log documents that record the students’ past web-based learning conduct, an insightful analysis framework is utilized to give fitting learning direction to help the students in improving their investigation practices and grade online class interest for the teacher. The accomplishment of the students’ last reports can likewise be anticipated by the conclusion framework precisely. Our trial results uncover that the proposed learning finding framework can proficiently assist students with expanding their insight while surfing in the internet Web-based “topic based learning” model.
Keywords: Web-Based Learning, Theme-Based Learning, Fuzzy Expert Program, K-Nearest Neighbor, Naïve Bayesian Classifier, Support Vector Machines, Learning Diagnostics.
Scope of the Article: Web-Based Learning: Innovation and Challenges.