Strategies to Predict Students’ Exam Attendance

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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Villarreal, Gonzalo Luján, Artola, Verónica
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/171447
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-171447
record_format dspace
spelling I19-R120-10915-1714472024-10-15T20:41:03Z http://sedici.unlp.edu.ar/handle/10915/171447 Strategies to Predict Students’ Exam Attendance Villarreal, Gonzalo Luján Artola, Verónica 2024 2024-10-11 2024-10-15T14:02:25Z en Informática Educación regression analysis attendance prediction approval prediction effective resource planning 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 Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
spellingShingle Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
Villarreal, Gonzalo Luján
Artola, Verónica
Strategies to Predict Students’ Exam Attendance
topic_facet Informática
Educación
regression analysis
attendance prediction
approval prediction
effective resource planning
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.
format Objeto de conferencia
Objeto de conferencia
author Villarreal, Gonzalo Luján
Artola, Verónica
author_facet Villarreal, Gonzalo Luján
Artola, Verónica
author_sort Villarreal, Gonzalo Luján
title Strategies to Predict Students’ Exam Attendance
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
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/171447
work_keys_str_mv AT villarrealgonzalolujan strategiestopredictstudentsexamattendance
AT artolaveronica strategiestopredictstudentsexamattendance
_version_ 1826544377938313216

Ejemplares similares