The ubiquity of internet-based nutrition information sharing indicates an opportunity to use social computing platforms to promote nutrition literacy and healthy nutritional choices. We conducted a series of experiments with unpaid volunteers using an online Nutrition Knowledge Test. The test asked participants to examine pairs of photographed meals and identify meals higher in a specific macronutrient (e.g., carbohydrate). After each answer, participants received no feedback on the accuracy of their answers, viewed proportions of peers choosing each response, received correctness feedback from an expert dietitian with or without expert-generated explanations, or received correctness feedback with crowd-generated explanations. The results showed that neither viewing peer responses nor correctness feedback alone improved learning. However, correctness feedback with explanations (i.e., modeling) led to significant learning gains, with no significant difference between explanations generated by experts or peers. This suggests the importance of explanations in social computing-based casual learning about nutrition and the potential for scaling this approach via crowdsourcing.
Abstract
The ubiquity of internet-based nutrition information sharing indicates an opportunity to use social computing platforms to promote nutrition literacy and healthy nutritional choices. We conducted a series of experiments with unpaid volunteers using an online Nutrition Knowledge Test. The test asked participants to examine pairs of photographed meals and identify meals higher in a specific macronutrient (e.g., carbohydrate). After each answer, participants received no feedback on the accuracy of their answers, viewed proportions of peers choosing each response, received correctness feedback from an expert dietitian with or without expert-generated explanations, or received correctness feedback with crowd-generated explanations. The results showed that neither viewing peer responses nor correctness feedback alone improved learning. However, correctness feedback with explanations (i.e., modeling) led to significant learning gains, with no significant difference between explanations generated by experts or peers. This suggests the importance of explanations in social computing-based casual learning about nutrition and the potential for scaling this approach via crowdsourcing.
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Marissa Burgermaster, Krzysztof Z. Gajos, Patricia Davidson, and Lena Mamykina. The role of explanations in casual observational learning about nutrition. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI '17, pages 4097-4145, New York, NY, USA, 2017. ACM.
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