## 4 Discussion  
In this study, we propose three distinct experiments to evaluate the progress of surgical skill acquisition by students who train on a laparoscopic VR simulator. For the first two experiments, we focus on the classification of the trainees' trials at different stages of surgical training: a 2-class classification (Beginning vs. End) and a 3-class classification (Beginning vs. Middle vs. End). Moreover, a regression model was evaluated to predict the remaining number of trials a student needs to _successfully_ complete a training task. Most of the ML algorithms used in the 2-class classification experiment presented accuracy percentages close to 80-98% (based on PCA) and 70-92% (based on [PERSON]'s score), showing a high-quality classification performance. The best-performing algorithm is SVM with the highest achieved accuracy of 97.1% (Task 5, based on PCA). After examining the results, it is evident that Task 7 presents the lowest accuracy, mainly due to the students' familiarity and extensive practice with the VR simulator across the previous six tasks. By the time the trainees reach Task 7, they have successfully completed Tasks 1 through 6, hence acquiring more skills that are essential for the successful completion of Task 7 (such as eye-hand coordination, depth perception, bimanual skills, laparoscopic orientation, and instrument manipulation). Consequently, there is a small difference between the  
Figure 5: Bar plots of the average feature importance of the developed binary classifiers for (**a**) Task 5, (**b**) Task 6, and (**c**) Task 7. Feature impact scatter plots for (**d**) Task 5, (**e**) Task 6, and (**f**) Task 7.  
performance metrics in the Beginning vs. the End of their training on this task. Moreover, when comparing results between the two-dimensionality reduction methods, PCA provides better results than [PERSON]'s score. This was expected due to the way that these techniques operate: PCA combines features while [PERSON]'s score ranks them before selection, resulting in a greater loss of variance. Moreover, the performance at the End of the training class is better probably due to the similar performance metrics achieved when trainees approach the successful completion of the task. For 3-class classification, LDA emerges as the top-performing algorithm (99.7%, Task 7), excelling not only in accuracy but also in specificity and sensitivity across all three tasks. Contrary to the trends observed in the 2-class classification, Task 7 demonstrates the highest scores. This can be attributed to the inclusion of the 'Trial number' feature, which was also highlighted by the [PERSON]'s score metric. Regarding the regression experiment, the SVR variants outperformed the other ML models. However, the LSTM model outperformed all ML models in Tasks 5 (MAE = 4) and 6 (MAE = 3.6) while in Task 7, its performance was similar to SVR (MAE = 3.6). These findings denote a promising performance that is also similar to that in other prediction tasks, such as the prediction of remaining duration in surgical operations [31]. In contrast, the naive approach performed poorly in Tasks 5 and 6 except for Task 7 where its performance was almost comparable to that of the ML algorithms (MAE = 4.1). When comparing the results for _all_, _short_, and _long_ training sessions, the performance of the ML algorithms is similar in all cases, and the same applies to the naive approach. In contrast, the LSTM model shows a clearly superior performance in shorter training performances (_fast_ learners) compared to the longer ones (_slow_ learners). This is due to its ability to better capture the variation in the trainee's performance metrics for a small number of trials whereas, for each ML model, the input is based solely on the current trial of the trainee. Besides the evaluation of models' performance, we leveraged techniques from the XAI domain to obtain a better understanding of the model prediction output and further interpretation of the key features involved in surgical skill acquisition.