## 2 Materials and Methods  
### VR Simulator and Data Acquisition  
The input data originated from 23 medical students with no prior experience in laparoscopy or VR simulation. The students followed a structured VR simulation curriculum during their undergraduate training at the Surgical Simulation Center of Athens University Medical School (AKISA), University General Hospital \"Attikon\", Greece. The employed VR simulator was the LAP Mentor(tm) laparoscopic simulator (Surgical Science, Goteborg, Sweden). The training tasks are integral components of the module 'Lapaaroscopic Skills 1', which comprises nine distinct tasks with each one focusing on a specific laparoscopic skill. The tasks are of increasing difficulty, starting from Task 1 where the student needs to locate ten balls using a camera, all through to Task 9 where the student needs to translocate objects using both hands. For this study, we identified three tasks indicative of the acquired surgical skills and with important educational value. Task 5 (_Clipping and Grasping_, Figure 1a) centers on the objective of securely holding and sealing leaking ducts within specific-colored segments, utilizing a pair of instruments. Task 6 (_Two-Handed Maneuvers_, Figure 1b) engages students in locating balls within a jelly-like substance and transferring them into a designated basket using a pair of instruments. Task 7 (_Cutting_, Figure 1c) requires safely cutting a circular shape without causing any damage, with the aid of two instruments (scissors and a grasper).  
Two important studies have examined the construct validity of the tasks included in the simulator's psychomotor skills module ('Laparoscopic Skills 1') [26, 27]. Task 5, followed by Tasks 6 and 7, has the most confirmed construct validity on more than one parameter. The postulated reason for this is the complexity of the tasks, which is translated into a greater number of maneuvers. Contrary to binary metrics (e.g., task completion) and scale parameters with a small range of values (e.g., number of dropped objects), other performance metrics such as the number of movements and number of cutting maneuvers can follow the progress of skill acquisition more reliably, as depicted in the learning curves published in [26, 27]. Moreover, metrics with higher and more varying values comprise a favorable substrate for computational analysis, including AI algorithms.  
The students' progress through the module adheres to a structured curriculum designed by the surgical education board of the simulation center based on the training guidelines proposed in [26]. Specifically, advancement through the tasks is contingent upon the _successful completion_ of the previous task (starting from Task 1 and continuing  
Figure 1: The three VR training tasks evaluated in this study: (**a**) Task 5 (Clipping and Grasping), (**b**) Task 6 (Two-Handed Maneuvers), and (**c**) Task 7 (Cutting).  
sequentially up to Task 9). Failure to complete a task requires the student to restart the module from Task 1. This process is reiterated until the student successfully completes all module tasks by performing two consecutive successful trials in every task. Hence, each student concludes the module at their individual pace, with the number of attempts not being constrained.  
It should be noted that the successful completion of a particular task is signified by several task-specific educational goals imposed by the educational curriculum. Specifically, the VR simulator provides an assessment report with a large number of task-specific performance metrics (Supplementary Materials, Table S1). In accordance with the educational objectives, the board has chosen a limited set of metrics for each task and established thresholds for each one. These thresholds, detailed in Table 1, delineate the criteria that a student must satisfy to consider a task as _'successfully completed'_.