Artificial intelligence tools for academic management: assigning students to academic supervisors

Name: Artificial intelligence tools for academic management: assigning students to academic supervisors
Author: Sanchez-Anguix, V., Chalumuri, R., Alberola, J. M., Aydoğan, Reyhan

İsim	Artificial intelligence tools for academic management: assigning students to academic supervisors
Yazar	Sanchez-Anguix, V., Chalumuri, R., Alberola, J. M., Aydoğan, Reyhan
Basım Tarihi:	2020
Basım Yeri	- International Academy of Technology, Education and Development (IATED)
Konu	Apps for education, New projects and innovation, Academic management
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2340-1079
Kayıt Numarası	2e000f5a-5916-4bf2-bed3-133497484bfb
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	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.
Editör	Chova, L. G., Martinez, A. L., Torres, I. C.

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Artificial intelligence tools for academic management: assigning students to academic supervisors

Yazar Sanchez-Anguix, V., Chalumuri, R., Alberola, J. M., Aydoğan, Reyhan

Basım Tarihi 2020

Basım Yeri - International Academy of Technology, Education and Development (IATED)

Konu Apps for education, New projects and innovation, Academic management

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2340-1079

Kayıt Numarası 2e000f5a-5916-4bf2-bed3-133497484bfb

Lokasyon Computer Science

Tarih 2020

Örnek Metin 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.

Editör Chova, L. G., Martinez, A. L., Torres, I. C.