## 4 Discussion
In Tasks 5 and 6, kinematic metrics (i.e., path length, number of movements) have the greatest impact on the model's prediction while goal-oriented features do not influence the final decision. This outcome is logical because, even though trainees achieve the task goals in every trial, they do not always do so in the optimal way, which is the primary objective of the training process. Thus, for both tasks, a trainee is likely to be classified at the End of training if they move the instruments along the optimal (shortest) path, avoiding any unnecessary movement. For Task 7, neither kinematic nor goal-specific metrics have any impact on the classification. This suggests little difference between performance metric values for the two classes, which is expected because, as mentioned above, trainees have acquired many surgical skills that they can put into practice. Consequently, the models were able to classify the students' trials based solely on the 'Trial number' feature. Despite the study's findings, some limitations must be addressed. First, the dataset includes training trials from 23 medical students, which may not fully capture the variability in surgical skill acquisition. Second, the study focuses on basic training tasks rather than procedural skills where surgical maneuvers may be more complex. To enhance VR training and ML-based assessment, it would be beneficial to include the Objective Structured Assessment of Technical Skill (OSATS) score in order to provide further insight on how well the ML models' outputs correlate with expert opinions [34]. However, this was not feasible in the current study due to the large number of trials (total number = 1253) and mostly, the inability to have access to the video recordings of the trainees' trials. Moreover, testing the models in a non-virtual environment would be useful to further validate their effectiveness. However, previous studies using video-based ML assessment were unable to extract performance metrics comparable to those automatically provided by VR simulators [28]. Although some studies have successfully utilized kinematic features to provide valuable insights, these approaches often lack the detailed metrics available in VR environments, particularly those related to interaction events (e.g., number of clipping attempts, cutting maneuvers, etc.) [6]. Reaching this level of detail through video analysis would require manual observation and recording of each event, an impractical solution due to the significant human annotation workload involved. In future research, we aim to address these limitations by implementing several enhancements. First, we plan to include follow-up assessments involving postgraduate students, residents, and expert surgeons performing both procedural tasks and full surgical procedures. This will help validate the skills acquired and enable better generalization of our findings. Furthermore, we intend to incorporate both performance metrics and video data from training sessions. Additionally, we aim to extend the study by using vision-based feature extraction through deep learning techniques, ultimately developing a multi-input fusion deep learning model, a method that has shown promising results in similar research [13]. Overall, the findings presented in this work are promising for all three experiments. The created models could be used in real-life scenarios, such as training rooms, to provide an automated form of assessment for each trainee individually. With the help of XAI to guide professors, students will be provided with an insight into how their skills are evolving with each trial on the VR simulator, elevating the overall quality of surgical education.