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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/171447
Ver los metadatos del registro completo
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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. |
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