Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining

TL;DR

This study uses educational data mining to identify key factors affecting students' academic progress in a virtual learning environment, revealing significant influences and suggesting system improvements.

Contribution

It identifies main factors influencing student trajectories and proposes modifications to course prerequisites based on data analysis within a virtual learning setting.

Findings

Strong correlation between student average and factors like English level, age, gender, residence, and over-stay.

The Syrian Crisis significantly impacts students' academic performance.

Recommendations for system enhancements to better monitor academic indicators.

Abstract

Our main goal is to discover the main factors influencing students' academic trajectory and students' academic evolution within such environment. Our results indicate strong correlation in this virtual learning environment between student average and some factors like: student's English level (despite the fact that Arabic language is the teaching language), student's age, student's gender, student's over-stay and student's place of residence (inside or outside Syria). Our results indicate also a need to modify the academic trajectory of students by changing the prerequisites of few courses delivered as a part of BIT diploma like Advanced DBA II, Data Security. In this research, the results also highlight the effect of the Syrian Crisis on students. Finally, we've suggested some future recommendations based on our observations and results to develop the current information system in SVU in order to help us to deduce some indicators more easily.