Recommendation System for Enhancing eLearning using Deep Learning

Kulkarni, Pradnya; Phatak, Rashmi; Bhate, Bela; Deshpande, Rutuja; Rai, Sunil

doi:10.1109/punecon46936.2019.9105685

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…It was updated with some features and had a test to gain the results. E-Learning takes the advantage of technology so it enables to be integrated in a large scale of E-learning such as E-Learning based on NRENS [7] that apply XMDD paradigm, using DIME low-code for data modeling, process modelling and prototyping. E-learning can change the whole education system and provide learning from various online learning.…”

Section: Literature Reviewmentioning

confidence: 99%

A Development of E-Learning-Based Instructional Media for English At Medan Civil Aviation Academy

Sylvia¹,

Hutabarat²

2021

Data Science: J. of Computing and Appl. Informatics

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The research aims to: (1) give comprehension of the concept of E-Learning as an instructional media; (2) develop E-Learning based instructional media for English; (3) know the effectiveness of E-Learning as an instructional media to improve cadets’ skill in English. E-Learning based instructional media was developed in Learning Management System (LMS) of Medan Civil Aviation Academy consisting of applicable features to be used easily and effectively by lecturer and cadets based on Elementary English lesson plan. The research was conducted at Medan Civil Aviation Academy in the odd semester of 2019/2020 academic year. The population was 144 cadets of first year and 24 cadets were taken as the sample through cluster random sampling technique. The method used in this research was research and development method and the technique of collecting data were by test and questionnaire.The results of the research concluded that: (1) cadets conducting E-Learning based instructional media achieved higher score (70,10) than conventional method (56,67); (2) development of E-Learning based instructional media showed high effectiveness in learning process gained from valid and reliable questionnaire (r11 = 0.8205); (3) development of E-Learning based instructional media was stated in “ a very good category” in term of usability aspect.

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Section: Literature Reviewmentioning

confidence: 99%

A Development of E-Learning-Based Instructional Media for English At Medan Civil Aviation Academy

Sylvia¹,

Hutabarat²

2021

Data Science: J. of Computing and Appl. Informatics

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“…To improve eLearning recommendation systems, it is critical to offer relevant data [12], [14], [17]. A customized eLearning recommendation system [11] is necessary to promote the acceptability of eLearning among students in all parts of India.…”

Section: -Introductionmentioning

confidence: 99%

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

Kulkarni,

Rai,

Sachdeo

et al. 2023

jist

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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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