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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/21/2677 |
| _version_ | 1797512066573008896 |
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| author | Alicia Nieto-Reyes Rafael Duque Giacomo Francisci |
| author_facet | Alicia Nieto-Reyes Rafael Duque Giacomo Francisci |
| author_sort | Alicia Nieto-Reyes |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:56:39Z |
| format | Article |
| id | doaj.art-39d9f7bfc5a8487485d731fc74e5d045 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T05:56:39Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-39d9f7bfc5a8487485d731fc74e5d0452023-11-22T21:17:14ZengMDPI AGMathematics2227-73902021-10-01921267710.3390/math9212677A Method to Automate the Prediction of Student Academic Performance from Early Stages of the CourseAlicia Nieto-Reyes0Rafael Duque1Giacomo Francisci2Department of Mathematics, Statistics and Computer Science, University of Cantabria, 39005 Santander, SpainDepartment of Mathematics, Statistics and Computer Science, University of Cantabria, 39005 Santander, SpainDepartment of Mathematics, University of Trento, 38122 Trento, ItalyThe 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.https://www.mdpi.com/2227-7390/9/21/2677computer-supported cooperative learningnon-parametric statisticspredictive methodsstatistical data depthsupervised classificationrandom methods |
| spellingShingle | Alicia Nieto-Reyes Rafael Duque Giacomo Francisci A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course Mathematics computer-supported cooperative learning non-parametric statistics predictive methods statistical data depth supervised classification random methods |
| title | 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_short | 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 |
| topic | computer-supported cooperative learning non-parametric statistics predictive methods statistical data depth supervised classification random methods |
| url | https://www.mdpi.com/2227-7390/9/21/2677 |
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