A Goal-driven Personalized Educational Content Recommendation System for Self-Learning


Authors : L M R Jayasinghe; Dharshana Kasthurirathna

Volume/Issue : Volume 7 - 2022, Issue 11 - November

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3VeNgVU

DOI : https://doi.org/10.5281/zenodo.7467422

With the evolution of the Internet, modern learning patterns have improved significantly, helping to bring knowl-edge wherever the learner requires it. When considering the knowledge available in public domains, the speed of information filtering, and information exchangeability, traditional educational frameworks do have not sufficient powers to move with today’s world. As a result, emerging trends indicate that internet-based E-Learning technologies will be used to acquire vast amounts of knowledge. Also with this evolution, self-learning is highly encouraged in the present era. Since there are a lot of internet resources, when someone looks for educational content there could be many similar responses, and this is where we may need personalized recommendations to make life easy. Compared to the existing research of personalized recommendations in theeducation domain, there is a gap in providing plenty of relevant personalized educational resources to a student for self-learning. Thereby, the research, Goal-driven Personalized Educational Content Recommendation System for Self-learning is based on the personal competency level and the areas that need to beimproved of a student. The outcome of the research will provide insight into the perspectives and challenges of personalized recommendation-based self-learning based, primarily web-based learning, and provides a path to identifying appropriate solutions using Topic Modeling for key challenges in the education domain, with personalized recommendations for future research.

Keywords : Machine Learning, Topic Modeling, Text Extraction

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