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A Method to automate the prediction of student academic performance from early stages of the course

Abstract: 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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Mathematics, 2021, 9 (21), 2677

Editorial: MDPI

 Fecha de publicación: 22/10/2021

Nº de páginas: 14

Tipo de publicación: Artículo de Revista

 DOI: 10.3390/math9212677

ISSN: 2227-7390

Proyecto español: MTM2017-86061-C2-2-P