A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course

The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful,...

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Autores principales:	Alicia Nieto-Reyes, Rafael Duque, Giacomo Francisci
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	computer-supported cooperative learning non-parametric statistics predictive methods statistical data depth supervised classification random methods Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/39d9f7bfc5a8487485d731fc74e5d045
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id	oai:doaj.org-article:39d9f7bfc5a8487485d731fc74e5d045
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spelling	oai:doaj.org-article:39d9f7bfc5a8487485d731fc74e5d0452021-11-11T18:14:41ZA Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course10.3390/math92126772227-7390https://doaj.org/article/39d9f7bfc5a8487485d731fc74e5d0452021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2677https://doaj.org/toc/2227-7390The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.Alicia Nieto-ReyesRafael DuqueGiacomo FrancisciMDPI AGarticlecomputer-supported cooperative learningnon-parametric statisticspredictive methodsstatistical data depthsupervised classificationrandom methodsMathematicsQA1-939ENMathematics, Vol 9, Iss 2677, p 2677 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computer-supported cooperative learning non-parametric statistics predictive methods statistical data depth supervised classification random methods Mathematics QA1-939
spellingShingle	computer-supported cooperative learning non-parametric statistics predictive methods statistical data depth supervised classification random methods Mathematics QA1-939 Alicia Nieto-Reyes Rafael Duque Giacomo Francisci A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
description	The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.
format	article
author	Alicia Nieto-Reyes Rafael Duque Giacomo Francisci
author_facet	Alicia Nieto-Reyes Rafael Duque Giacomo Francisci
author_sort	Alicia Nieto-Reyes
title	A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
title_short	A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
title_full	A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
title_fullStr	A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
title_full_unstemmed	A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course
title_sort	method to automate the prediction of student academic performance from early stages of the course
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/39d9f7bfc5a8487485d731fc74e5d045
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A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course

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