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Simplifying Learning Experience on a Personalized Content Recommendation System for Complex Text Material in E-Learning
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Author(s): R. Angeline (SRM Institute of Science and Technology, India), S. Aarthi (SRM Instıtute of Science And Technology, India), Rishabh Jain (SRM Institute of Science and Technology, India), Muzamil Faisal (SRM Institute of Science and Technology, India), Abishek Venkatesan (SRM Instıtute of Science and Technology, India)and R. Regin (SRM Instıtute of Science and Technology, India)
Copyright: 2024
Pages: 16
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch006
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Abstract
Complex material is difficult to absorb in e-learning environments, which distracts and lowers learning outcomes. This initiative proposes that consumers watch simple movies with the same topic to improve learning. Text analytics recommends tailored videos to consumers. The algorithm can make more tailored recommendations by evaluating text interaction and learning preferences. The system simplifies learning and makes material more complicated and intelligible. Visual videos aid learning by improving memory and comprehension. Analyze the data before using the advice. NLP can extract key text content and context. Review results are used to develop related topics and themes. Next, find relevant video content using keywords and keywords list. Previous video data can be used to recommend video material. Videos should simplify content to help consumers understand and remember it. Text interactions should be considered when personalising video suggestions. Create user profiles using engagement indicators like time on page, scroll depth, and click behaviour.
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