Yeni Anistyasari, Shintami C. Hidayati, Ekohariadi
An important instrument of academic performance is student engagement in online learning. This work built a prediction model based on decision trees to find elements influencing student engagement in online learning settings. Examined were involvement, interaction, responsiveness to criticism, and academic performance. Examining the correlations between these factors and student involvement levels using decision trees revealed According to the findings, student involvement was highly predicted by participation and interaction; followed by responsiveness to criticism and academic performance. The approach reveals great accuracy in estimating engagement levels, which let teachers find children who may need certain interventions or more help. Using this approach, teachers and designers of learning environments may be more aggressive in raising student involvement by means of more focused approaches. These results highlight the necessity of a data-driven strategy to increase the efficacy of online learning as well as provide insightful analysis for the creation of more flexible and sensitive educational technology suited for student requirements. © 2024 IEEE.
Universitas Negeri Surabaya, Faculty of Engineering, Surabaya, Indonesia; Institut Teknologi Sepuluh Nopember, Department of Informatics, Surabaya, Indonesia
