Research Article
Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model
@ARTICLE{10.4108/eetel.4470, author={A Madhavi and A Nagesh and A Govardhan}, title={Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model}, journal={EAI Endorsed Transactions on e-Learning}, volume={9}, number={1}, publisher={EAI}, journal_a={EL}, year={2023}, month={12}, keywords={Course Recommendation, BERT, MLM, Transformers, Deep Transformer based Ensembled Attention Model, DTEAM}, doi={10.4108/eetel.4470} }- A Madhavi
A Nagesh
A Govardhan
Year: 2023
Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model
EL
EAI
DOI: 10.4108/eetel.4470
Abstract
The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber
Copyright © 2023 A. Madhavi et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
