Adaptive Upgradation of Personalized E-Learning Portal using Data Mining
Ravikiran R.K1, K.R. Anil Kumar2

1Ravikiran*, Department of Computer Applications, Acharya Institute of Graduate Studies, Bengaluru, India. Research Scholar, Research and Development Center, Bharathiar University, Coimbaore, India
2Dr. K.R. Anil Kumar, Professor of Computer Science & Principal Quality College of Management Studies and Science, Uttarahalli, Kengeri Main Road, Bengaluru, India 

Manuscript received on September 11, 2020. | Revised Manuscript received on November 04, 2020. | Manuscript published on November 10, 2021. | PP: 224-227 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81671110120| DOI: 10.35940/ijitee.A8167.1110120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Implementation of data mining techniques in e-learning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pre training program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his her performance in the pre training program. 
Keywords: Data Mining, E-Learning, Portal Up Gradation, Topic Recommendation, Clustering.