Deep Learning-based Educational User Profile and User Rating Recommendation System for E-Learning
محورهای موضوعی : IT Strategy
Pradnya Vaibhav Kulkarni
1
(School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune,India.)
Sunil Rai
2
(Department of Computer science and Engineering, MIT School of Engineering, MIT ADT University, India.)
Rajneeshkaur Sachdeo
3
(Department of Computer science and Engineering, MIT School of Engineering, MIT ADT University, India.)
Rohini Kale
4
(School of Polytechnique, Dr. Vishwanath Karad MIT World Peace University, Pune,India)
کلید واژه: E-Learning, Recommendation System, Machine Learning, Deep Learning,
چکیده مقاله :
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.
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.
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