Abstract
The ability to predict students' performance in educational settings like schools and universities is crucial. A key objective of this effort is to increase academic outcomes and prevent dropout rates, among other benefits. Automating student activities, encouraged by information collected from any technology-based learning tool, has an important role in the process here. Those big quantities of information ought to be completely studied theoretically and processed for gaining worthy insights concerning a student's background as well as interacting with scientific missions, facilitating the development of advanced ways and algorithms to predict students' performance. The current study reviews several contemporary mechanisms to analyze how a student behaves, along with a specific concentration on mechanisms of video analysis. Twenty-six research papers published between 2018 and 2023 were under review. The review focused on the significance of video analysis in comprehending how a student behaves and presents insight into future tendencies in this regard. The outcomes propose that the incorporation of those mechanisms could improve educational results by providing data-driven backup for every process of decision-making.
Recommended Citation
Rashid, Shatha Talib and Abdullah, Hasanen S.
(2025)
"Cutting-Edge Methods for Analyzing Student Behavior in Educational Settings: A Review,"
Journal of Soft Computing and Computer Applications: Vol. 2:
Iss.
1, Article 1019.
DOI: https://doi.org/10.70403/3008-1084.1019
