Student Engagement Prediction in Online Session

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Y. Trilochan Sashank
V. Kakulapati
Sunil Bhutada


The individuals who make up the globe constantly advance into the future and improve both their personal lives and the conditions in which they live. One ‘s education is the basis of one ‘s knowledge. Humans' education has a significant impact on their behavior and IQ. Through the use of diverse pedagogical techniques, instructors always play a part in changing students' ways of thinking and developing their social and cognitive abilities. However, getting students to participate in an online class is still difficult. In this study, we created an intelligent predictive system that aids instructors in anticipating students' levels of interest based on the information they learn in an online session and in motivating them through regular feedback. The level of students' engagement is divided into three tiers based on their online session activities (Not engaged, passively engaged, and actively engaged). The given data was subjected to the application of Decision Trees (DT), Random Forest Classifiers (RF), Logistic Regression (LR), and Long Short-Term Memory Networks are among the numerous machine learning approaches (LSTM). According to performance measurements, LSTM is the most accurate machine learning algorithm. The instructors can get in touch with the students and inspire them by improving their teaching approaches based on the results the system produces.

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How to Cite
Sashank, Y. T. ., Kakulapati, V. ., & Bhutada, S. . (2023). Student Engagement Prediction in Online Session. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 43–47.


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