Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment

Virginia Puyana-Romero (), Cesar Marcelo Larrea-Álvarez, Angela María Díaz-Márquez, Ricardo Hernández-Molina and Giuseppe Ciaburro
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Virginia Puyana-Romero: Department of Sound and Acoustic Engineering, Faculty of Engineering and Applied Sciences, Universidad de Las Américas (UDLA), Quito 170503, Ecuador
Cesar Marcelo Larrea-Álvarez: Faculty of Medical Sciences, Medical Career, Universidad de Especialidades Espíritu Santo, Guayaquil 092301, Ecuador
Angela María Díaz-Márquez: Innovation Specialist in Higher Education, Information Intelligence Directorate, Universidad de Las Américas (UDLA), Quito 170503, Ecuador
Ricardo Hernández-Molina: Laboratory of Acoustic Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
Giuseppe Ciaburro: Department of Architecture and Industrial Design, Università degli Studi della Campania Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, Italy

Sustainability, 2024, vol. 16, issue 11, 1-30

Abstract: In recent years, great developments in online university education have been observed, favored by advances in ICT. There are numerous studies on the perception of academic performance in online classes, influenced by aspects of a very diverse nature; however, the acoustic environment of students at home, which can certainly affect the performance of academic activities, has barely been evaluated. This study assesses the influence of the home acoustic environment on students’ self-reported academic performance. This assessment is performed by calculating prediction models using the Recursive Feature Elimination method with 40 initial features and the following classifiers: Random Forest, Gradient Boosting, and Support Vector Machine. The optimal number of predictors and their relative importance were also evaluated. The performance of the models was assessed by metrics such as the accuracy and the area under the receiver operating characteristic curve (ROC_AUC-score). The model with the smallest optimal number of features (with 14 predictors, 9 of them about the perceived acoustic environment) and the best performance achieves an accuracy of 0.7794; furthermore, the maximum difference for the same algorithm between using 33 and 14 predictors is 0.03. Consequently, for simplicity and the ease of interpretation, models with a reduced number of variables are preferred.

Keywords: online learning; domestic soundscape; self-reported academic performance; noise sources; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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