Assessment of Offline and Online Learning Mode in the Performance of Student during Corona Virus Pandemic Era using Machine Learning in Python


Authors : Dr. Wilson MUSONI; Florence MUKAMABANO

Volume/Issue : Volume 7 - 2022, Issue 12 - December

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3CJD0P8

DOI : https://doi.org/10.5281/zenodo.7527704

The shift from offline to online learning mode that many countries adopted during the covid-19 pandemic necessitates a greater knowledge on how students cope with these changes and its effect on their performance. The offline/traditional learning system has many advantages especially in facilitating interactions and idea exchanges between students and teachers. However little is known about the effect of this transition to online learning considering the skills that students and teachers have in using innovative technologies and programs. We assessed the students’ performance in both offline and online learning mode during the coronavirus pandemic period. We also examined the effective learning mode between the online and offline mode just comparing both learning modes through python libraries. We found that there is a significant difference in the students’ performance between online and offline learning modes. Our results show that during this covid-19 pandemic students performed well in online learning mode compared to students who used and continued using offline learning. The prediction tools used suggest that the use of online and offline learning modes also improved the quality of education and empowered higher learning institutions to enhance strategies of more flexibility in offering more web-based classes. Consequently, the online learning mode appears to be particularly important in improving the education quality and the academic performance.

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