Dalveren, Gonca Gökçe MenekşeDalveren, Gonca Gokce MenekseCagiltay, Nergiz ErcilÇağıltay, NergizInformation Systems EngineeringSoftware Engineering2024-07-052024-07-052018200144-929X1362-300110.1080/0144929X.2018.14603992-s2.0-85045144323https://doi.org/10.1080/0144929X.2018.1460399https://hdl.handle.net/20.500.14411/2945Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Cagiltay, Nergiz/0000-0003-0875-9276Today, with the advancements in the eye-tracking technology, it has become possible to follow surgeons' eye movements while performing surgical tasks. Despite the availability of studies providing a better understanding of surgeons' eye movements, research in the particular field of endoneurosurgery is very limited. Analysing surgeons' eye-movement data can provide general insights into how to improve surgical education programmes. In this study, four simulation-based task-oriented endoscopic surgery training scenarios were developed and implemented by 23 surgical residents using three different hand conditions; dominant, non-dominant, and both. The participants' recorded eye data comprised fixation number, fixation duration, saccade number, saccade duration, pursuit number, pursuit duration, and pupil size. This study has two main contributions: First, it reports on the eye-movement behaviours of surgical residents, demonstrating that novice residents tended to make more fixations and saccades than intermediate residents. They also had a higher fixation duration and followed the objects more frequently compared to the intermediates. Furthermore, hand conditions significantly affected the eye movements of the participants. Based on these results, it can be concluded that eye-movement data can be used to assess the skill levels of surgical residents and would be an important measure to better guide trainees in surgical education programmes. The second contribution of this study is the eye-movement event classifications of 10 different algorithms. Although the algorithms mostly provided similar results, there were a few conflicted values for some classifications, which offers a clue as to how researchers can utilise these algorithms with low sampling frequency eye trackers.eninfo:eu-repo/semantics/closedAccessEye trackingeye-movement classificationexperience levelhand conditionsurgical virtual environmentsendoneurosurgeryArticleQ2Q2375517537WOS:000430498500007