Determinants of admission in the academic performance of excellence in the career of military sciences

Gualán, Germán Geovanny Muñoz; Gualán, Erik Danilo Muñoz; Gualán, Alberth Patricio Muñoz

doi:10.37117/s.v1i22.745

Cited by 1 publication

(1 citation statement)

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“…The assessment of academic performance involves a spectrum of evaluation methods, capturing student progress in various coursework aspects, ranging from classroom practices to quizzes and examinations (Contreras et al, 2020). Evaluations of educational system effectiveness often consider critical indicators like annual dropout and graduation rates, acknowledging the multifaceted nature of academic achievement influenced by a diverse array of social, economic, and demographic factors (Muñoz Gualá n et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Feature Selection Techniques in Predictive Modeling of Mathematics Performance: An Ecuadorian Case Study

Sánchez-Pozo,

Chamorro-Hernández,

Mina

et al. 2023

ESM

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The field of educational research increasingly emphasizes predictive modeling of academic performance, focusing on identifying determinants of student success and crafting models to forecast future achievements. This investigation evaluates the efficacy of different feature selection techniques in predicting mathematics performance among Ecuadorian students, based on data from the 2021-2022 cycle of the Ser Estudiante test. Employing supervised logistic regression for classification, the study compares three feature selection methods: selection based on the highest k-scores, recursive feature elimination with cross-validation (RFECV), and recursive feature elimination (RFE). The assessment reveals that both the highest k-scores and RFECV methods are highly effective in isolating the most pertinent features for predicting mathematical prowess. These methods achieved prediction accuracies exceeding 90%, with k-scores attaining 96% and RFECV 92%. Furthermore, they demonstrated remarkable recall (94% and 97%, respectively) and F1-Score (96% and 91%, respectively). Additionally, both methods presented Receiver Operating Characteristic (ROC) curves with an area under the curve (AUC) of 99%, signifying superior discriminatory capability. The findings illuminate the critical role of judicious feature selection in enhancing the precision of predictive models for academic performance, particularly in mathematics. The results advocate for the application of these techniques in pinpointing key factors contributing to student success. This study not only contributes to the methodological discourse in educational data analysis but also provides practical insights for the Ecuadorian education system in leveraging data-driven approaches to enhance student outcomes.

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Section: Introductionmentioning

confidence: 99%