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Relating Student’s Performance with Individual Characteristics

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Abstract

A Portuguese Military Academy has a significant failure rate, losing about half of the admitted individuals during the course. This work aims to understand the causes associated to low income, through the analysis of the data of the individuals from the application to the end of the course, in order to identify the characteristics of individuals with greater and less likely to succeed in the Military Academy. This work was started in [1]. In an initial phase, some techniques of descriptive statistics of data analysis were used. The first step of this analysis, the candidates are analyzed independently, and the admitted and the finalists are analyzed together comparing the variables at the beginning and at the end of the courses. Simple statistical inference techniques were used [30], namely confidence intervals, parametric tests, contingency tables. Such analysis was completed in [31] using intermediate level inference techniques, namely analysis of variance (ANOVA). In the present manuscript we intend to complete this approach using a general linear models approach [20, 32] after the selection of factors by factorial analysis [2, 3, 8, 9]. The study evidences greater success for individuals entering in NA with better grades and for individuals taking notice of the application competition over the internet.

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Acknowledgements

This work was supported by Portuguese funds through the Center of Naval Research (CINAV), Portuguese Naval Academy, Portugal and The Portuguese Foundation for Science and Technology (FCT), through the Center for Computational and Stochastic Mathematics (CEMAT), University of Lisbon, Portugal, project UID/Multi/04621/2019.

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Correspondence to M. Filomena Teodoro .

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Teodoro, M.F., Delgado, A., Martins, J.M. (2023). Relating Student’s Performance with Individual Characteristics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-37108-0_18

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