Improving Courses Management by Predicting the Number of Students

Detalhes bibliográficos
Autor(a) principal: Vasco Taveira Gomes
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://repositorio-aberto.up.pt/handle/10216/88992
Resumo: Every year, in higher education institutions all around the world, millions of students are required to choose the curricular units they are interested in enrolling for the coming semesters. When managing courses and their respective units, colleges and universities aim to predict and understand these demands in order to better plan the next scholar year. By successfully predicting students' demands, universities are able to ensure their limited budgets and resources are allocated properly. This study intends to answer the needs of a course administrator regarding the number of students each curricular unit will have by analyzing and applying predictive models. The main focus of this work will be the prediction of the number of students enrolling in optional units, number of students per optional unit and number of students per non-optional unit in the syllabus. While some conclusions may be reached by simply measuring and extrapolating the difference in the number of students per year, identifying the cause of this difference is fundamental in the construction of a complete predictive model. Factors such as student grade average per unit, perceived difficulty or unit workload, that aren't always visible in the data, must also be taken into consideration. Once these factors are decided, the study will attempt to apply and compare different predictive functions. For the development phase of the investigation, this project will be applied to the course of Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto in the form of a case study.
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spelling Improving Courses Management by Predicting the Number of StudentsEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringEvery year, in higher education institutions all around the world, millions of students are required to choose the curricular units they are interested in enrolling for the coming semesters. When managing courses and their respective units, colleges and universities aim to predict and understand these demands in order to better plan the next scholar year. By successfully predicting students' demands, universities are able to ensure their limited budgets and resources are allocated properly. This study intends to answer the needs of a course administrator regarding the number of students each curricular unit will have by analyzing and applying predictive models. The main focus of this work will be the prediction of the number of students enrolling in optional units, number of students per optional unit and number of students per non-optional unit in the syllabus. While some conclusions may be reached by simply measuring and extrapolating the difference in the number of students per year, identifying the cause of this difference is fundamental in the construction of a complete predictive model. Factors such as student grade average per unit, perceived difficulty or unit workload, that aren't always visible in the data, must also be taken into consideration. Once these factors are decided, the study will attempt to apply and compare different predictive functions. For the development phase of the investigation, this project will be applied to the course of Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto in the form of a case study.2016-07-152016-07-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/88992TID:201322722engVasco Taveira Gomesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:27:01Zoai:repositorio-aberto.up.pt:10216/88992Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:23:59.127822Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Improving Courses Management by Predicting the Number of Students
title Improving Courses Management by Predicting the Number of Students
spellingShingle Improving Courses Management by Predicting the Number of Students
Vasco Taveira Gomes
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short Improving Courses Management by Predicting the Number of Students
title_full Improving Courses Management by Predicting the Number of Students
title_fullStr Improving Courses Management by Predicting the Number of Students
title_full_unstemmed Improving Courses Management by Predicting the Number of Students
title_sort Improving Courses Management by Predicting the Number of Students
author Vasco Taveira Gomes
author_facet Vasco Taveira Gomes
author_role author
dc.contributor.author.fl_str_mv Vasco Taveira Gomes
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description Every year, in higher education institutions all around the world, millions of students are required to choose the curricular units they are interested in enrolling for the coming semesters. When managing courses and their respective units, colleges and universities aim to predict and understand these demands in order to better plan the next scholar year. By successfully predicting students' demands, universities are able to ensure their limited budgets and resources are allocated properly. This study intends to answer the needs of a course administrator regarding the number of students each curricular unit will have by analyzing and applying predictive models. The main focus of this work will be the prediction of the number of students enrolling in optional units, number of students per optional unit and number of students per non-optional unit in the syllabus. While some conclusions may be reached by simply measuring and extrapolating the difference in the number of students per year, identifying the cause of this difference is fundamental in the construction of a complete predictive model. Factors such as student grade average per unit, perceived difficulty or unit workload, that aren't always visible in the data, must also be taken into consideration. Once these factors are decided, the study will attempt to apply and compare different predictive functions. For the development phase of the investigation, this project will be applied to the course of Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto in the form of a case study.
publishDate 2016
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