Optimization of Curriculum Content Using Data Mining Methods
Автор: Firudin T. Aghayev, Gulara A. Mammadova, Rena T. Malikova, Lala A. Zeynalova
Журнал: International Journal of Education and Management Engineering @ijeme
Статья в выпуске: 4 vol.14, 2024 года.
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The purpose of this article is to search and extract the necessary content, identifying curriculum topics. Classification and clustering of text documents are challenging artificial intelligence tasks. Therefore, an important objective of this study is to propose and implement a tool for analyzing textual information. The study used Data Mining methods to analyze text data and generate educational content. The work used methods for classifying text information, namely, support vector machines (SVM), Naive Bayes classifier, decision tree, K-nearest neighbor (kNN) classifier. These methods were used in developing the curriculum for the specialty “Cybersecurity” for the Faculty of Information and Telecommunication Technologies. About 48 curricula in this specialty were analyzed, topics and sections in disciplines were identified, and the content of the academic program was improved. It is expected that the results obtained can be used by specialists, managers and teachers to improve educational activities.
Curriculum content, Data Mining methods, Text Mining, semantic similarities, SVM, Naive Bayes classifier, decision tree, kNN classifier
Короткий адрес: https://sciup.org/15019318
IDR: 15019318 | DOI: 10.5815/ijeme.2024.04.02
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