Virtual teaching assistant for undergraduate students using natural language processing & deep learning

• URL: http://arxiv.org/abs/2411.09001v1
• Date: Wed, 13 Nov 2024 20:02:17 GMT
• Title: Virtual teaching assistant for undergraduate students using natural language processing & deep learning
• Authors: Sadman Jashim Sakib, Baktiar Kabir Joy, Zahin Rydha, Md. Nuruzzaman, Annajiat Alim Rasel,
• Abstract summary: Many universities were forced to switch to online education as a result of COVID-19. A growing number of institutions are considering blended learning with some parts in-person and the rest of the learning taking place online. In this paper, we are offering a primary dataset, the initial implementation of a virtual teaching assistant named VTA-bot, and its system architecture.
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• Abstract: Online education's popularity has been continuously increasing over the past few years. Many universities were forced to switch to online education as a result of COVID-19. In many cases, even after more than two years of online instruction, colleges were unable to resume their traditional classroom programs. A growing number of institutions are considering blended learning with some parts in-person and the rest of the learning taking place online. Nevertheless, many online education systems are inefficient, and this results in a poor rate of student retention. In this paper, we are offering a primary dataset, the initial implementation of a virtual teaching assistant named VTA-bot, and its system architecture. Our primary implementation of the suggested system consists of a chatbot that can be queried about the content and topics of the fundamental python programming language course. Students in their first year of university will be benefited from this strategy, which aims to increase student participation and involvement in online education.

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