The Acoustic Environment and University Students’ Satisfaction with the Online Education Method during the COVID-19 Lockdown

Puyana‐Romero, Virginia; Díaz-Márquez, Angela María; Ciaburro, Giuseppe; Molina, Ricardo Hernández

doi:10.3390/ijerph20010709

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…In terms of content, the survey encompassed inquiries related to personal information (age, gender, and semester), the domestic acoustic environment (identifying noise sources and levels), and noise interference during academic activities [11]. According to the type of activity, they were categorized as synchronous or autonomous, and the responses were classified on Likert scales ranging from 5 to 7 points.…”

Section: Online Surveymentioning

confidence: 99%

“…With a growing number of students opting for online courses, understanding their relative effectiveness through experimental studies has become progressively imperative for the improvement of education. For this reason, researchers allot serious efforts to evaluating the effects of the online methodology, either based on student satisfaction or student performance and its potential explicative factors [4][5][6][7][8][9][10][11]. So, because the COVID-19 lockdown forced a situation where activities significantly changed for a while, it offered a unique scenario to assess the status, challenges, and the implications of the online teaching approach as the unique alternative.…”

Section: Introductionmentioning

confidence: 99%

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

Puyana-Romero,

Larrea-Álvarez,

Díaz-Márquez

et al. 2024

Sustainability

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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.

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Section: Online Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Puyana-Romero,

Larrea-Álvarez,

Díaz-Márquez

et al. 2024

Sustainability

Self Cite

View full text Add to dashboard Cite

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Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library

Zhang,

Mui,

Masullo

et al. 2024

Acoustics

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Understanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. However, predictive models are not specifically tailored to predict students’ acoustic evaluations, particularly in educational settings. To bridge this gap, the present study conducted a field investigation in a university library, including a measurement and questionnaire survey. Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. The performance of these models was evaluated using five metrics, allowing for a comparative analysis. The results revealed that the models better predicted acoustic acceptance than acoustic satisfaction. Notably, the RF and GBM models exhibited the highest performance, with accuracies of 0.87 and 0.84, respectively, in predicting acoustic acceptance. Conversely, the SVM models performed poorly and were not recommended for acoustic quality prediction. The findings of this study demonstrated the feasibility of employing machine learning models to predict occupants’ acoustic evaluations, thereby providing valuable insights for future acoustic assessments.

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The Domestic Acoustic Environment in Online Education—Part 1: Differences by Gender, Perceived Academic Quality, and Self-Rated Performance

Puyana-Romero,

Díaz-Márquez,

Garzón

et al. 2024

Buildings

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The domestic environment plays a critical role in online education, particularly its acoustic aspect. This study investigates whether there are differences in the perception of acoustic variables between students who perceive their academic performance as negative (G1) and those who rate it positively (G2). It also explores differences between students who assess the quality of education as positive or negative, along with gender distinctions. Data were collected through an online survey conducted by Universidad de Las Américas, Quito, Ecuador, during the COVID-19 lockdown. Statistical analyses revealed significant differences in the perception of noise between groups G1 and G2: Students who rated their academic performance negatively experienced higher noise interference from sources like voices and household appliances. Similar patterns were observed when assessing educational quality. Gender differences also emerged, with women reporting higher levels of noise interference caused by sound sources (voices, TV/Radio/household appliances, and animals), and in the development of autonomous and synchronous tasks than men. The differences between the groups evaluated in pairs suggest that these aspects should be taken into account when designing domestic spaces and strategies to improve the quality of the acoustic environment for students.

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The Acoustic Environment and University Students’ Satisfaction with the Online Education Method during the COVID-19 Lockdown

Cited by 3 publications

References 68 publications

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

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

Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library

The Domestic Acoustic Environment in Online Education—Part 1: Differences by Gender, Perceived Academic Quality, and Self-Rated Performance

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