Towards Independent Students' Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks

Abstract

During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment. Now, we are back to the traditional blended learning environment and wish to understand the students’ attitudes and perceptions of online learning, knowing that they are able to compare blended and online modes. The aim of this paper is to present the performed predictive analysis regarding the students’ online learning performance taking into account their opinion. The predictive models are created through a supervised machine learning algorithm based on Artificial Neural Networks and are characterized with high accuracy. The analysis is based on generated synthetic datasets, ensuring a high level of students’ privacy preservation.