A Personalized Web Based E-Learning Recommendation System To Enhance and User Learning Experience
Nidhi Joshi1, Rajendra Gupta2

1Nidhi Joshi, Department of Computer Science, Rabindranath Tagore University, Raisen, Madhya Pradesh, India.
Rajendra Gupta, Department of Computer Science, Rabindranath Tagore University, Raisen, Madhya Pradesh, India.
Manuscript received on April 04, 2020. | Revised Manuscript received on April 11, 2020. | Manuscript published on May 30, 2020. | PP: 1186-1195 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9991038620/2020©BEIESP | DOI: 10.35940/ijrte.F9991.059120
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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: The key aim of the data mining techniques is to help the user by reducing the effort for exploring the data, recovering the patterns, and implementing applications that help to find the knowledge specific contents, decision making, and predictions. This research work develops a recommendation system by using the merits of data mining algorithms. They are used for designing web-based e-learning recommendation systems. This model aims to understand the user behavior and contents requirements of the learner. This purpose is solved by obtaining the information from the data source and producing the suggestions of suitable content to the learner. The concept of web content mining and web usage mining has been combined together for performing the required work. This technique involves the genetic algorithm and k-means clustering algorithm for designing the presented model. In this work the k-means clustering algorithm has been used to track user behavior and the genetic algorithm has been used as a search algorithm to find the necessary resources in the database. Finally, the presented system is implemented and its performance is measured. The estimated results demonstrate that the presented model enhances the accuracy of recommendations and also speeds up the computations. A related performance calculation has also provided to justify this conclusion. The obtained results demonstrate that this technique is acceptable for new generation application designs.
Keywords: Recommendation System, Web usages Mining, Web Content Mining, E-learning Resource Prediction, Weighted Recommendation System, Results Explication, Performance Calculation.
Scope of the Article: Data Mining