Use of eye tracking in medical education.

Eye tracking has become increasingly applied in medical education research for studying the cognitive processes that occur during the performance of a task, such as image interpretation and surgical skills development. However, analysis and interpretation of the large amount of data obtained by eye tracking can be confusing. In this article, our intention is to clarify the analysis and interpretation of the data obtained from eye tracking. Understanding the relationship between eye tracking metrics (such as gaze, pupil and blink rate) and cognitive processes (such as visual attention, perception, memory and cognitive workload) is essential. The importance of calibration and how the limitations of eye tracking can be overcome is also highlighted.

Identifying the response process validity of clinical vignette-type multiple choice questions: An eye-tracking study.

INTRODUCTION: Clinical vignette-type multiple choice questions (CV-MCQs) are widely used in assessment and identifying the response process validity (RPV) of questions with low and high integration of knowledge is essential. Answering CV-MCQs of different levels of knowledge application and integration can be understood from a cognitive workload perspective and this can be identified by using eye-tracking. The aim of the pilot study was to identify the cognitive workload and RPV of CV-MCQs of different levels of knowledge application and integration by the use eye-tracking. METHODS: Fourteen fourth-year medical students answered a test with 40 CV-MCQs, which were equally divided into low-level and high-level complexity (knowledge application and integration). Cognitive workload was measured using screen-based eye tracking, with the number of fixations and revisitations for each area of interest. RESULTS: We found a higher cognitive workload for high-level complexity (M = 121.74) compared with lower-level complexity questions (M = 51.94) and also for participants who answered questions incorrectly (M = 94.31) compared with correctly (M = 79.36). CONCLUSION: Eye-tracking has the potential to become a useful and practical approach for helping to identify the RPV of CV-MCQs. This approach can be used for improving the design and development of CV-MCQs, and to provide feedback to inform teaching and learning.[Box: see text].