From crisis to opportunity: practices and technologies for a more effective post-COVID classroom

Regnier, Jeremie; Shafer, Ethan; Sobiesk, Edward; Stave, Nicholas; Haynes, Malcolm

doi:10.1007/s10639-023-11929-9

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Research must assess various frameworks to ensure the long-term sustainability of online education beyond the COVID-19 lockdown [32][33][34]. This evaluation should adopt a holistic approach, which considers both the individual circumstances of students and the casuistics of new online academic modalities.…”

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

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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: Introductionmentioning

confidence: 99%