Abstract: Analyzing students' behaviour during online classes is vital for teachers to identify the strengths and weaknesses of online classes. This analysis, based on observing academic performance and student activity data, helps teachers to understand the teaching outcomes. Most Educational Data Mining (EDM) processes analyze students' academic or behavioural data; in this case, the accurate prediction of student behaviours could not be achieved. This study addresses these issues by considering student’s activity and academic performance datasets to evaluate teaching and learner outcomes efficiently. It is necessary to utilize a suitable method to handle the high dimensional data while analyzing Educational Data (ED), because academic data is growing daily and exponentially. This study uses two kinds of data for student behaviour analysis. It is essential to use feature reduction and selection methods to extract only important features to improve the student’s behaviour analysis performance. By utilizing a hybrid ensemble method to get the most relevant features to predict students’ performance and activity levels, this approach helps to reduce the complexity of the feature-learning model and improve the prediction performance of the classification model. This study uses Improved Principal Component Analysis (IPCA) to select the most relevant feature. The resultant features of the IPCA are given as input to an ensemble method to select the most relevant feature sets to improve the prediction accuracy. The prediction is done with the help of Residual Network-50 (ResNet50) is combined with a Support Vector Machine (SVM) to classify students' performance and activity during online classes. This performance analysis evaluates the students’ behaviour analysis model. The proposed approach could predict the performance and activity of students with a maximum of 98.03% accuracy for online classes, and 98.06% accuracy for exams.

Varsha Ganesh and S Umarani. “Ensemble Feature Selection for Student Performance and Activity-Based Behaviour Analysis”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507122

@article{Ganesh2024,
title = {Ensemble Feature Selection for Student Performance and Activity-Based Behaviour Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507122},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507122},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Varsha Ganesh and S Umarani}
}