Teacher-Subject Relationship Model Applying Text Mining
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Abstract
The study focuses on the relationship between the professional titles of university professors and the subjects assigned to them. The methodology used in the study is based on text mining and frequency analysis of words. A descriptive documentary methodology was employed, using empirical methods and comparative tools. Documents related to the subjects and the profiles of the teachers were collected, and processes of cleaning, vectorization, and similarity were performed on the texts.
The results obtained showed a significant correlation between the teaching profile and the contents of the subjects. An average correlation of 80% was found between the professors and the contents of the subjects in the Information Systems program. The same analysis was conducted for the professors in the Information Technology program, and an average correlation of 82% was found. Additionally, the correlation was analyzed in greater detail for specific teachers and subjects, revealing high levels of correlation, with percentages ranging from 82% to 88%.
The study utilized techniques such as word2Vec and cosine similarity to calculate the proximity between words and related concepts. The results support the importance of using text mining methods to analyze and understand the relationship between professors and subjects, providing a solid foundation for continuous improvement in educational management in the field of information technology. The study highlights the importance of implementing a decision-making culture based on the data generated in universities.
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Leslie Paullette Macías-Veliz, Universidad Técnica de Manabí - Ecuador
Student at the Faculty of Computer Science, studying the Information Systems Engineering program. Regarding this research, I consider it to have been challenging, fruitful, and enriching, as it has generated new knowledge and meaningful learning for me.
Gabriel Agustín Cotera-Ramírez, Universidad Técnica de Manabí - Ecuador
https://orcid.org/0000-0003-2726-8317
Master's degree in Management Informatics and New Technologies from Federico Santa María Technical University of Chile in 2008. Assistant professor at the Faculty of Computer Science at Technical University of Manabí. Research focus on Machine Learning, Data Processing, IoT
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