Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/43

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Browsing by Subject "Academic management"

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    Artificial intelligence tools for academic management: assigning students to academic supervisors
    (International Academy of Technology, Education and Development (IATED), 2020) Sanchez-Anguix, V.; Chalumuri, R.; Alberola, J. M.; Aydoğan, Reyhan; Computer Science; Chova, L. G.; Martinez, A. L.; Torres, I. C.; AYDOĞAN, Reyhan
    In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.