Strategies to Predict Students’ Exam Attendance

Autores
Villarreal, Gonzalo Luján; Artola, Verónica
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This article presents a study on predicting student attendance to exams in a university setting. The study focused on the Concept of Algorithms, Data, and Programs course, a foundational course in systems bachelor. Two models were constructed: linear regression and polynomial regression of degree 3, aimed to predict the total number of attendees and the number of students who would pass the exam. We built a dataset that included information on student enrollment, previous exam attendance, grades, and other relevant factors. Students were classified into three groups: reduced exam, complete exam with prior attendance, and complete exam without prior attendance. The results showed that the models’ predictions were accurate enough, and that they could be used to ensure appropriate classroom occupancy without overcrowding or empty rooms. The models guided the allocation of students, optimizing space utilization while providing available seats for attending students. The study identified opportunities for improvement. One limitation was the assignment of attendance probabilities to achieve the overall predicted attendance. Future work could involve predicting attendance rates for each group individually. Additionally, implementing a classification model to categorise students into pass, fail, insufficient, and non-attendance groups would provide a more comprehensive understanding of student outcomes.
Este trabajo fue realizado utilizando el conjunto de datos "Tasa de asistencia y aprobación a exámenes de CADP" (Villarreal, 2023), al que puede accederse haciendo clic en "Documentos relacionados".
Facultad de Informática
Materia
Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/171447

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spelling Strategies to Predict Students’ Exam AttendanceVillarreal, Gonzalo LujánArtola, VerónicaInformáticaEducaciónregression analysisattendance predictionapproval predictioneffective resource planningThis article presents a study on predicting student attendance to exams in a university setting. The study focused on the Concept of Algorithms, Data, and Programs course, a foundational course in systems bachelor. Two models were constructed: linear regression and polynomial regression of degree 3, aimed to predict the total number of attendees and the number of students who would pass the exam. We built a dataset that included information on student enrollment, previous exam attendance, grades, and other relevant factors. Students were classified into three groups: reduced exam, complete exam with prior attendance, and complete exam without prior attendance. The results showed that the models’ predictions were accurate enough, and that they could be used to ensure appropriate classroom occupancy without overcrowding or empty rooms. The models guided the allocation of students, optimizing space utilization while providing available seats for attending students. The study identified opportunities for improvement. One limitation was the assignment of attendance probabilities to achieve the overall predicted attendance. Future work could involve predicting attendance rates for each group individually. Additionally, implementing a classification model to categorise students into pass, fail, insufficient, and non-attendance groups would provide a more comprehensive understanding of student outcomes.Este trabajo fue realizado utilizando el conjunto de datos "Tasa de asistencia y aprobación a exámenes de CADP" (Villarreal, 2023), al que puede accederse haciendo clic en "Documentos relacionados".Facultad de Informática2024info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/171447enginfo:eu-repo/semantics/altIdentifier/isbn/978-3-031-70807-7info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-031-70807-7_11info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-70807-7_11info:eu-repo/semantics/reference/hdl/10915/157959info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:46:03Zoai:sedici.unlp.edu.ar:10915/171447Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:46:03.649SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Strategies to Predict Students’ Exam Attendance
title Strategies to Predict Students’ Exam Attendance
spellingShingle Strategies to Predict Students’ Exam Attendance
Villarreal, Gonzalo Luján
Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
title_short Strategies to Predict Students’ Exam Attendance
title_full Strategies to Predict Students’ Exam Attendance
title_fullStr Strategies to Predict Students’ Exam Attendance
title_full_unstemmed Strategies to Predict Students’ Exam Attendance
title_sort Strategies to Predict Students’ Exam Attendance
dc.creator.none.fl_str_mv Villarreal, Gonzalo Luján
Artola, Verónica
author Villarreal, Gonzalo Luján
author_facet Villarreal, Gonzalo Luján
Artola, Verónica
author_role author
author2 Artola, Verónica
author2_role author
dc.subject.none.fl_str_mv Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
topic Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
dc.description.none.fl_txt_mv This article presents a study on predicting student attendance to exams in a university setting. The study focused on the Concept of Algorithms, Data, and Programs course, a foundational course in systems bachelor. Two models were constructed: linear regression and polynomial regression of degree 3, aimed to predict the total number of attendees and the number of students who would pass the exam. We built a dataset that included information on student enrollment, previous exam attendance, grades, and other relevant factors. Students were classified into three groups: reduced exam, complete exam with prior attendance, and complete exam without prior attendance. The results showed that the models’ predictions were accurate enough, and that they could be used to ensure appropriate classroom occupancy without overcrowding or empty rooms. The models guided the allocation of students, optimizing space utilization while providing available seats for attending students. The study identified opportunities for improvement. One limitation was the assignment of attendance probabilities to achieve the overall predicted attendance. Future work could involve predicting attendance rates for each group individually. Additionally, implementing a classification model to categorise students into pass, fail, insufficient, and non-attendance groups would provide a more comprehensive understanding of student outcomes.
Este trabajo fue realizado utilizando el conjunto de datos "Tasa de asistencia y aprobación a exámenes de CADP" (Villarreal, 2023), al que puede accederse haciendo clic en "Documentos relacionados".
Facultad de Informática
description This article presents a study on predicting student attendance to exams in a university setting. The study focused on the Concept of Algorithms, Data, and Programs course, a foundational course in systems bachelor. Two models were constructed: linear regression and polynomial regression of degree 3, aimed to predict the total number of attendees and the number of students who would pass the exam. We built a dataset that included information on student enrollment, previous exam attendance, grades, and other relevant factors. Students were classified into three groups: reduced exam, complete exam with prior attendance, and complete exam without prior attendance. The results showed that the models’ predictions were accurate enough, and that they could be used to ensure appropriate classroom occupancy without overcrowding or empty rooms. The models guided the allocation of students, optimizing space utilization while providing available seats for attending students. The study identified opportunities for improvement. One limitation was the assignment of attendance probabilities to achieve the overall predicted attendance. Future work could involve predicting attendance rates for each group individually. Additionally, implementing a classification model to categorise students into pass, fail, insufficient, and non-attendance groups would provide a more comprehensive understanding of student outcomes.
publishDate 2024
dc.date.none.fl_str_mv 2024
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-70807-7_11
info:eu-repo/semantics/reference/hdl/10915/157959
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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