A new ML-based approach to enhance student engagement in online environment.

ABSTRACT: The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student's engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student's engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine

SUBMITTER: Ayouni S 

PROVIDER: S-EPMC8580220 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature