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

Alicia Nieto-Reyes, Rafael Duque and Giacomo Francisci
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Alicia Nieto-Reyes: Department of Mathematics, Statistics and Computer Science, University of Cantabria, 39005 Santander, Spain
Rafael Duque: Department of Mathematics, Statistics and Computer Science, University of Cantabria, 39005 Santander, Spain
Giacomo Francisci: Department of Mathematics, University of Trento, 38122 Trento, Italy

Mathematics, 2021, vol. 9, issue 21, 1-14

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.

Keywords: computer-supported cooperative learning; non-parametric statistics; predictive methods; statistical data depth; supervised classification; random methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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