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Recommendation System for Compatibility Between the Teacher's Profile and the Subject to Teach

Abstract:

This work proposes the design of a prototype of a Recommendation System (RS) applied to the Software Engineering Program at the University of Guayaquil to assess the compatibility between the teaching profile and the subject to be taught. Its purpose is to reduce uncertainty in the decision-making process of the Academic Coordination department when assigning teachers to subjects, in order to streamline this procedure. The professional profile information of teachers is stored in a database for subsequent analysis using content-based filtering algorithms. Based on the similarity between each teacher's profile and the required subject description, the system suggests the most suitable teacher to teach that subject. This work is of a quantitative research nature since one of its objectives is to investigate and analyze RS. Additionally, it applies the Kanban agile methodology for model creation. The work is carried out using the Python programming language with the Django framework, and the chosen database is MySQL, as these tools are open source and meet the development needs.