A recommender system based on data mining techniques to support the automatic assignment of courses to teachers in higher education

F. Pesantez-Aviles, D. Calle-Lopez, V. Robles-Bykbaev, M. Rodas-Tobar, C. Vasquez-Vasquez

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

The talent management is a fundamental mainstay in the operation of any organization, regardless to its action scope. Nowadays there are several informatics tools aimed on supporting assignment of work positions according to candidates' profile as well as the organization needs in specific areas. However, in the area of higher education assigning courses to teachers is a challenging task due to the complex relations that exist among the different actors involved in the process (schedules, educational contents, teachers' profiles, etc.). Likewise, an incorrect assignment of courses to teachers can generate problems such as higher economic costs in educational management or non-compliance with strategic management indicators. In view of the foregoing, in this paper we present the first stage of a recommender system to generate courses distribution in higher education. Our proposal relies on data mining techniques as well as several metrics that can be adjusted to generate different courses distributions/assignments. Our recommender system was put to test with 133000 real registers of an Ecuadorian university and the achieved results are encouraging.

Original languageEnglish
Pages231-236
Number of pages6
DOIs
StatePublished - 29 Mar 2018
EventProceedings - 2017 International Conference on Information Systems and Computer Science, INCISCOS 2017 - Quito, Ecuador
Duration: 23 Nov 201725 Nov 2017

Conference

ConferenceProceedings - 2017 International Conference on Information Systems and Computer Science, INCISCOS 2017
Abbreviated titleINCISCOS 2017
Country/TerritoryEcuador
CityQuito
Period23/11/1725/11/17

Keywords

  • Data mining
  • courses assignment
  • higher education
  • recommender system

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