## 4 Discussion
### Limitations and Further Research
Another limitation is that the number of participants was gradually reduced as they moved through the modules. This study initially recruited 1264 participants, and enrolment was reduced across the various modules. This decrease could be caused by several factors, including the progression of the difficulty level, reduced motivation, or any other factors within or outside the class that cut down the time that a student has to undertake the modules. Such degradation could negatively affect the performance data and impact the representativeness of the results for other modules and the overall population of participants. Future studies should investigate the reasons for participant reduction and consider strategies to improve retention, such as enhancing engagement or providing incentives to encourage continued participation. Although overall dropout rates were documented, no systematic module-wise analysis of attrition was performed. Therefore, it is unclear whether dropout was driven by technical issues (e.g., VR usability), content complexity, or external factors such as time constraints. Informal feedback suggests that time limitations and declining motivation over time played a role, but this remains speculative. Future stud ies should incorporate module-specific analytics and user experience metrics to identify and address the root causes of attrition. Another limitation concerns the reliance on self-reported behavioral risk data, which may be influenced by recall errors or social desirability bias. No external validation, such as observational data or traffic records, was conducted due to the large-scale and anonymous nature of this study. To minimize bias, students were assured anonymity and were instructed that there were no correct or incorrect responses, encouraging honest reporting. Future studies should consider triangulating self-reported data with observational or mixed-method approaches to improve reliability. Additionally, this study did not collect data on contextual variables such as socioeconomic background, prior exposure to traffic safety education, or school-level resources. These unmeasured factors may have contributed to the observed performance differences between city and municipality students, limiting our ability to fully interpret these disparities. Future research should include these covariates to better understand the role of local contexts. Lastly, a methodological and ethical limitation of this study is the exclusion of non-disclosed gender participants due to the low percentage compared to males and females, which may inadvertently contribute to the invisibility of non-binary individuals in educational research. Future studies should aim to include and explore patterns among this group to enhance inclusivity and representation. These limitations present opportunities for future research to expand on this study's findings and further enhance our understanding of effective road safety education for secondary school students. Despite these constraints, this study offers valuable insights into the potential of e-learning platforms like R2S in promoting traffic safety among adolescents. Challenging Modules and Implications for Platform Improvement  
The data indicated that 'Drones' was the module the students understood the least, as shown by the lowest mean and the highest standard deviation. This finding suggests that interpreting traffic situations from an aerial perspective can be challenging for numerous students. Despite this, the current module presents a good chance for developing situational awareness and risk perception from the perspective that might benefit future drivers [60]. Conversely, the high performance in the \"Take a Bike Ride\" module reflects the strong cycling culture in Belgium and children's early exposure to cycling [68]. This success could be leveraged to introduce more advanced cycling scenarios or to draw parallels with safe driving behaviors as students approach driving age.