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Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment
International Journal of Emerging Technologies in Learning ; 18(4):66-85, 2023.
Artigo em Inglês | Scopus | ID: covidwho-2264132
ABSTRACT
Students' grades can affect their future studies at university. The COVID-19 situation has resulted in a greater amount of online teaching, in which teachers and learners rarely interact, causing additional problems with academic performance. This research aims to design and develop an intelligent student relationship management platform (an intelligent SRM platform) using machine learning prediction for student empowerment. This research begins with the synthesis of the factors, the machine learning prediction process, and the platform components. The results of the synthesis establish the design of the platform. Undergraduate students' grades are then predicted using the decision tree algorithm. Students are divided into two groups, empowerment and non-empowerment groups, using this algorithm. The results show that the learning outcome prediction model has an accuracy of 100.00% and an F-measure of 100%. The most important factor for improving grades is the grade point average, with a weight of 0.637. Therefore, student empowerment to provide students with better grades is essential. This paper presents two approaches to student empowerment using artificial intelligence technology from the intelligent SRM platform and empowering teachers © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.
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Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Scopus Idioma: Inglês Revista: International Journal of Emerging Technologies in Learning Ano de publicação: 2023 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados de organismos internacionais Base de dados: Scopus Idioma: Inglês Revista: International Journal of Emerging Technologies in Learning Ano de publicação: 2023 Tipo de documento: Artigo