Deep Learning-based Educational User Profile and User Rating Recommendation System for E-Learning

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Kulkarni Pradnya V.; Sachdeo Rajneeshkaur; Rai Sunil; Kale Rohini

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2023 | Volume:11 | Issue:43 Page(s): 185-195

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Deep Learning-based Educational User Profile and User Rating Recommendation System for E-Learning

Author(s)

Kulkarni Pradnya V. | Sachdeo Rajneeshkaur | Rai Sunil | Kale Rohini | Issue Writer Certificate

Keywords

E-LearningQ4

Recommendation SystemQ4

Machine LearningQ4

Deep LearningQ4

Abstract

In the current era of online learning, the Recommendation System for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or content-based filtering based on historical data, students’ previous grade, results, or user profiles. The eLearning system selected courses based on these parameters in a generalized manner rather than on a personalized basis. Personalized recommendations, information relevancy, choosing the proper course, and recommendation accuracy are some of the issues in eLearning Recommendation Systems. In this paper, existing conventional eLearning and course Recommendation Systems are studied in detail and compared with the proposed approach. We have used, the dataset of User Profile and User Rating for a recommendation of the course. K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Nave Bayes, Linear Regression, Linear Discriminant Analysis, and Neural Network were among the Machine Learning techniques explored and deployed. The accuracy achieved for all these algorithms ranges from 0. 81 to 0. 97. The proposed algorithm uses a hybrid approach by combining collaborative filtering and Deep Learning. We have improved accuracy to 0. 98 which indicate that the proposed model can provide personalized and accurate eLearning recommendation for the individual user.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops