Academic performance prediction associated with synchronous online interactive learning behaviors based on the machine learning approach

Liang, Gui-Qin; Jiang, Chun-song; Ping, Qiuzhe; Jiang, Xinyi

doi:10.1080/10494820.2023.2167836

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Comparing the performance of our prediction models with those of other studies presents challenges, since in our research, the target variable-self-reported academic performance (ACAD_PERFO)-is subjective, while in most of the others, the target variable is an objective measure like GPA or equivalent ratings [28,[77][78][79][80][81][82][83][84][85][86][87][88][89][90]. We were interested in how students perceived their performance, which is totally different from actual academic achievements.…”

Section: Models Metrics and Comparisonmentioning

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: Models Metrics and Comparisonmentioning

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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Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Khan,

Ali,

Khan

et al. 2024

Sci Rep

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The COVID-19 pandemic has had a significant impact on students’ academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students’ performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students’ performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features.

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Enhancing EFL reading and writing through AI-powered tools: design, implementation, and evaluation of an online course

Hsiao

Chang

2023

Interactive Learning Environments

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Academic performance prediction associated with synchronous online interactive learning behaviors based on the machine learning approach

Cited by 4 publications

References 35 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

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Enhancing EFL reading and writing through AI-powered tools: design, implementation, and evaluation of an online course

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