Using Eye-Tracking Metrics to Predict Student Preferences Between a Campus Food Pantry and Alternative Options

Abstract: Food insecurity among college students causes a significant threat to academic success and overall well-being. According to the National Postsecondary Student Aid Study, more than 4 million students were food insecure during the 2019-2020 school year. While university food pantries work tirelessly to solve this issue, many students remain unaware of these resources or are hesitant to use them. It is important to understand how students perceive and engage with campus food pantries compared to popular campus dining options to improve outreach and reduce food insecurity. While surveys and focus groups can be useful, they may not fully capture the subconscious drivers of decision-making. This study leveraged both survey responses and eye-tracking data to investigate student preferences between a local college food pantry and prominent on-campus food options. Participants viewed 13 paired image scenarios, and Areas of Interest (AOIs) were defined to collect eye-tracking metrics: Time to First Fixation, Total Fixation Duration, and Fixation Count. An Extreme Gradient Boosting (XGBoost) model identified key eye-tracking metrics, using student’s reported food option choices for cross-validation. Results revealed that Fixation Duration was the strongest predictor, suggesting that prolonged visual attention correlated with preference. Additionally, students leaned toward the food pantry for convenience to receive shelf-stable snacks but opted for alternatives when seeking prepared meals. This research supports the development of more effective food assistance strategies that prioritize student needs and behaviors.

Keywords: college food insecurity, eye-tracking, machine learning

DOI: 10.54941/ahfe1006887

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