In this paper, two predictive models have been designed namely students' assessments grades and final students' performance. The models can be used to detect the factors that influence …

Apr 30, 2024 · The study uses advanced machine learning algorithms to predict student performance, enhancing accuracy and enabling early intervention. It also allows for …

In this paper we use ML algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors.

In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various …

3 days ago · The rapid expansion of e-learning has resulted in a surge in educational data volume, presenting challenges in manually uncovering valuable information. Concurrently, …

Apr 30, 2024 · In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. …

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the …

To measure how the activities of learners could impact their learning achievement in MOOCs, the researchers found that Learning Analytics (LAs) in conjunction with machine learning, are …

Oct 1, 2022 · The findings suggest an extended model of TAM with IDT for the acceptance of the e-learning system used to improve students learning performance, which can help decision …

Jul 24, 2020 · In this paper, two predictive models have been designed namely students' assessments grades and final students' performance. The models can be used to detect the …