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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alicia Nieto-Reyes & Rafael Duque & Giacomo Francisci, 2021. "A Method to Automate the Prediction of Student Academic Performance from Early Stages of the Course," Mathematics, MDPI, vol. 9(21), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2677-:d:662221
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    References listed on IDEAS

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    1. Dyckerhoff, Rainer & Mozharovskyi, Pavlo, 2016. "Exact computation of the halfspace depth," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 19-30.
    2. Cuesta-Albertos, J.A. & Nieto-Reyes, A., 2008. "The random Tukey depth," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4979-4988, July.
    3. Nieto-Reyes, Alicia & Battey, Heather, 2021. "A topologically valid construction of depth for functional data," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    4. Anders I. MØrch & Silje Jondahl & Jan A. Dolonen, 2005. "Supporting Conceptual Awareness with Pedagogical Agents," Information Systems Frontiers, Springer, vol. 7(1), pages 39-53, February.
    5. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    6. Giovanni Saraceno & Claudio Agostinelli, 2021. "Robust multivariate estimation based on statistical depth filters," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 935-959, December.
    7. Cuesta-Albertos, J.A. & Nieto-Reyes, A., 2008. "The Tukey and the random Tukey depths characterize discrete distributions," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2304-2311, November.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.
    2. Luis González-De La Fuente & Alicia Nieto-Reyes & Pedro Terán, 2022. "Properties of Statistical Depth with Respect to Compact Convex Random Sets: The Tukey Depth," Mathematics, MDPI, vol. 10(15), pages 1-23, August.
    3. Marina Segura & Jorge Mello & Adolfo Hernández, 2022. "Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?," Mathematics, MDPI, vol. 10(18), pages 1-20, September.

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