Insights Through Gaze: Unraveling Visual Patterns in Domain-Specific Learning

Abstract: Human mind weaves visual experiences and cognitive processes together in its learning journey to build specific knowledge domains. In this study, we explore how prior knowledge influences what we see and visualize, suggesting that our experience profoundly shapes our perceptions and eye movements. Further, we aim to discern the level of domain knowledge expertise present in participants from diverse engineering fields, utilizing eye-tracking technology. Studies in the past, highlight that eye markers such as: fixation count, total scanning duration, and saccadic duration are essential in understanding the presence of domain-specific knowledge of an individual, further giving us valuable insights into the cognitive processing underlying the information processing and comprehension during the visual task. 102 graduate students (59 male and 43 female) with age ranging between 21 to 34 years (mean age of 27.23 and a standard deviation of 2.98) from different domain knowledge backgrounds were considered in this study. In specific, 33 students from the architecture domain, 36 from the mechanical domain, and 33 from diverse domains of humanities, computer sciences, and biosciences participated in this experiment. Participants with a weighted gaze of 80% and/or above were further considered to continue with the experiments. Participants were initially presented with the Raven’s Advanced Progressive Matrix (RAPM) task set to ensure that there was homogeneity in intellectual ability within the representative sample, aiming to mitigate the influence of intelligence on problem-solving task performance. Thereafter, architecture and mechanical domain-specific tasks were presented in order of increasing task complexity to all the participants. Participants’ eye movements were analyzed to identify distinct eye patterns associated with varying expertise levels and prior domain knowledge. Our findings reveal that there are significant differences in the eye movements across participants from various domains, suggesting that different inherent visual strategies were adopted to meet the demands of the tasks under consideration. Participants with prior domain knowledge (experts in the task) exhibited more efficient information processing with fewer fixations and shorter scanning durations than novice performing the same task. Several key eye markers based on dwells, fixations, saccades, and pupils were investigated to understand the relationship between visual perception, prior domain knowledge acquisition, and learning. Significant eye markers are instrumental in discerning individuals with varying domain-specific knowledge. Notably, metrics such as Total Time Duration, Total Dwell Time, Number of Fixations, and Average Fixation Duration exhibit significance in distinguishing individuals across different domains, each manifesting at distinct time intervals. This research contributes to understanding how visual strategies evolve across diverse knowledge domains in response to varying task complexities. In addition, this research gives a systematic analysis of the visual scanning process during problem-solving by individuals in different domain specializations. The use of machine learning models such as decision trees, random forests, and support vector machines to classify novice participants from experts based on eye markers is reported. Our experiments show as high as 70% accuracy in classifying participants with domain knowledge against those who do not have domain knowledge in a domain specific task. Through this research, educators and technologists can design more effective learning environments and personalized training programs with an emphasis on the nuanced interplay between visual perception and cognitive processes. This study contributes to the ongoing dialogue surrounding integrating visual elements in educational practices and underscores the transformative potential of eye-tracking technology in enhancing learning outcomes across diverse domains.

Keywords: Eye, Tracking, Domain, Specific knowledge, Visual Perception, Visual Search

DOI: 10.54941/ahfe1005063

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