Beyond the Scoreboard: A Machine Learning Investigation of Online Games’ Influence on Jordanian University Students’ Grades
Table 3
Comparison of this study with state-of-the-art results.
This research
Previous research
In this study, we employed CNN in its original form, resulting in an impressive accuracy rate of 96.69%
In the study conducted by [17], the researchers focused on enhancing the CNN algorithm and transforming it into ACNN, a method previously employed, and achieved a notable accuracy rate of 96.8%
We employed CNN to examine how online games affect the academic performance and well-being of students at Jordanian universities
In the paper by [18], CNN was employed for traffic prediction. Furthermore, in the research conducted by [19], CNN was utilized to predict the remaining useful life (RUL) in carrying scenarios
In this research, we implemented a CNN architecture with four layers, resulting in a commendable accuracy rate of 96.69%
In the research conducted by [21], they devised an eight-layer CNN to extract self-learned features, which yielded a remarkable accuracy rate of 93.9%
In this study, CNN recommends that students decrease their gaming hours as a measure to safeguard their GPAs
