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.
- Sofiane Abbar, Yelena Mejova, Ingmar Weber. 2015. You tweet what you eat: Studying food consumption through Twitter. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 3197--3206. http://doi.acm.org/10.1145/2702123.2702153Google ScholarDigital Library
- Elena Agapie, Lucas Colusso, Sean A. Munson and Gary Hsieh. 2016. Plansourcing: Generating behavior change plans with friends and crowds. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 119--133. http://doi.acm.org/10.1145/2818048.2819943Google Scholar
- Lisa R. Anderson and Charles A. Holt. 2008. Information cascade experiments. Handbook of Experimental Economics Results, 335--343. http://dx.doi.org/10.1016/S1574-0722(07)00039-X Google ScholarCross Ref
- American Dietetic Association. 2011. Nutrition and you: Trends 2011 survey, public opinion on food and nutrition: 20 years of insights. Retrieved September 15, 2016 from http://www.eatright.org/nutritiontrendsGoogle Scholar
- American Public Health Association. 2016. What is Public Health? Retrieved September 15, 2016 from www.apha.orgGoogle Scholar
- Albert Bandura Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1: 1--26. Google ScholarCross Ref
- Albert Bandura, Richard H. Walters. 1977. Social learning theory. Prentice Hall, Oxford, England.Google Scholar
- Nancy D. Berkman, Stacey L. Sheridan, Katrina E. Donahue, David J. Halpern and Karen Crotty Low. 2011. health literacy and health outcomes: An updated systematic review. Annals of Internal Medicine, 155, 2: 97--107. Google ScholarCross Ref
- Benjamin Samuel Bloom. 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals. NY: D. McKay Co., Inc.Google Scholar
- Elena T. Carbone and Jamie M. Zoellner. 2012. Nutrition and health literacy: A systematic review to inform nutrition research and practice. Journal of the Academy of Nutrition and Dietetics, 112, 2: 254--265. Google ScholarCross Ref
- Yubo Chen, Qi Wang and Jinhong Xie. 2011. Online social interactions: A natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48, 2: 238--254. Google ScholarCross Ref
- Isobel R. Contento. 2007. Nutrition Education: Linking Research, Theory, and Practice. Burlington, MA: Jones & Bartlett Learning.Google Scholar
- Edward L. Deci and Richard M. Ryan. 2010. Intrinsic motivation. In Corsini Encyclopedia of Psychology (4th ed.), Irving B. Weiner and W. Edward Craighead (eds.). Hoboken, NJ: John Wiley and Sons, 1--2. Google ScholarCross Ref
- Allison E. Doub, Aron Levin, Charles Edward Heath and Kristie LeVangie. 2015. Mobile app-etite: Consumer attitudes towards and use of mobile technology in the context of eating behaviour. Journal of Direct, Data and Digital Marketing Practice, 17, 2: 114--129. Google ScholarCross Ref
- Ryan Drapeau, Lydia B. Chilton, Jonathan Bragg and Daniel S. Weld. 2016. Microtalk: Using argumentation to improve crowdsourcing accuracy. In Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing (HCOMP).Google Scholar
- Daniel A. Epstein, Felicia Cordeiro, James Fogarty, Gary Hsieh and Sean A. Munson. 2016. Crumbs: Lightweight daily food challenges to promote engagement and mindfulness. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI). Google ScholarDigital Library
- Feng Gao, Enrico Costanza and mc schraefel. 2012. Honey= sugar means unhealthy: Investigating how people apply knowledge to rate food's healthiness. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 71--80. http://doi.acm.org/10.1145/2370216.2370228Google Scholar
- Laura Germine, Ken Nakayama, Bradley C. Duchaine, Christopher F. Chabris, Garga Chatterjee and Jeremy B. Wilmer. 2012. Is the web as good as the lab? Comparable performance from web and lab in cognitive/perceptual experiments. Psychonomic Bulletin & Review, 19, 5: 847--857. Google ScholarCross Ref
- Laura Germine, Ken Nakayama, Eric Loken, Bradley Duchaine, Christopher Chabris, Garga Chatterjee and Jeremy Wilmer. 2010. Downloadable science: Comparing data from internet and lab-based psychology experiments. Journal of Vision, 10, 7: 682682.Google ScholarCross Ref
- Heather Gibbs and Karen Chapman-Novakofski. 2012. A review of health literacy and its relationship to nutrition education. Topics in Clinical Nutrition, 27, 4: 325--333. Google ScholarCross Ref
- Ralph Haefner. 1932. Casual learning of word meanings. The Journal of Educational Research, 25, 45: 267--277. Google ScholarCross Ref
- Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics: 65--70.Google Scholar
- Yuheng Hu, Lydia Manikonda and Subbarao Kambhampati. 2014. What we instagram: A first analysis of instagram photo content and user types. In Proceedings of the International AAAI Conference on Web and Social Media.Google ScholarCross Ref
- Angela A. Hung and Charles R. Plott. 2001. Information cascades: Replication and an extension to majority rule and conformity-rewarding institutions. The American economic review, 91, 5: 1508--1520. Google ScholarCross Ref
- Jean-Fabrice Lebraty and Katia Lobre-Lebraty. The dangers of crowdsourcing. 2013. The dangers of Crowdsourcing. In Crowdsourcing. Hoboken, NJ: John Wiley & Sons, 97--100. Google ScholarCross Ref
- Lena Mamykina, Andrew D. Miller, Yevgeniy Medynsky, Catherine Grevet, Patricia R. Davidson, Michael A. Terry, Elizabeth D. Mynatt. 2011. Examining the Impact of Social Tagging on Sensemaking in Nutrition Management, in the Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2011 http://doi.acm.org/10.1145/1978942.1979037Google Scholar
- Lena Mamykina, Thomas N. Smyth, Jill P. Dimond and Krzysztof Z. Gajos. 2016. Learning from the crowd: Observational learning in crowdsourcing communities. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2635--2644. http://doi.acm.org/10.1145/2858036.2858560Google Scholar
- Michael K. Paasche-Orlow and Michael S. Wolf. 2007. The causal pathways linking health literacy to health outcomes. American Journal of Health Behavior, 31, Supplement 1: S19-S26. Google ScholarCross Ref
- Katharina Reinecke and Krzysztof Z. Gajos. Labinthewild: Conducting large-scale online experiments with uncompensated samples. In Proceedings of the Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1364--1378. http://doi.acm.org/10.1145/2675133.2675246Google Scholar
- S. C. Ratzan and R. M. Parker. 2000. Introduction. In National Library of Medicine Current Bibliographies in Medicine: Health Literacy, National Institutes of Health, U.S. Department of Health and Human Services., Bethesda, MD.Google Scholar
- D. Schacter, D. Gilbert, D. Wegner and M.K. Nock. 2014. Psychology. NY: Worth Publishers.Google Scholar
- Tracy L. Scott, Julie A. Gazmararian, Mark V. Williams and David W. Baker. 2002. Health literacy and preventive health care use among medicare enrollees in a managed care organization. Medical Care, 40, 5: 395--404. Google ScholarCross Ref
- Sanket Sharma and Munmun De Choudhury. 2015. Detecting and characterizing nutritional information of food and ingestion content in instagram. In Proceedings of the 24th International Conference on World Wide Web, 115--116. http://doi.acm.org/10.1145/2740908.2742754Google ScholarDigital Library
- Inge Spronk, Charina Kullen, Catriona Burdon and Helen O'Connor. 2014. Relationship between nutrition knowledge and dietary intake. British Journal of Nutrition, 111, 10: 1713--1726. Google ScholarCross Ref
- Roumen Vesselinov and John Grego. 2012. Duolingo effectiveness study. City University of NY,Google Scholar
- Luis von Ahn. 2013. Duolingo: Learn a language for free while helping to translate the web. In Proceedings of the 2013 international conference on Intelligent user interfaces, 1--2. http://doi.acm.org/10.1145/2449396.2449398Google ScholarDigital Library
- Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi and Thomas W. Malone. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science, 330, 6004: 686--688. Google ScholarCross Ref
- Keiji Yanai and Yoshiyuki Kawano. 2014. Twitter food photo mining and analysis for one hundred kinds of foods. In Advances in Multimedia Information Processing -- PCM 2014: 15th Pacific-Rim Conference on Multimedia, Kuching, Malaysia, December 1--4, 2014, Proceedings, 22--32. http://dx.doi.org/10.1007/978--3--319--13168--9_3Google ScholarCross Ref
- Juanjuan Zhang and Peng Liu. 2012. Rational herding in microloan markets. Management science, 58, 5: 892912.Google Scholar
- Jamie Zoellner, Carol Connell, Wendy Bounds, LaShaundrea Crook and Kathy Yadrick. 2009. Peer reviewed: Nutrition literacy status and preferred nutrition communication channels among adults in the lower mississippi delta. Preventing Chronic Disease, 6, 4: A128.Google Scholar
Recommendations
Learning From the Crowd: Observational Learning in Crowdsourcing Communities
CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing SystemsCrowd work provides solutions to complex problems effectively, efficiently, and at low cost. Previous research showed that feedback, particularly correctness feedback can help crowd workers improve their performance; yet such feedback, particularly when ...
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
IUI '22: Proceedings of the 27th International Conference on Intelligent User InterfacesWhen people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the ...
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing SystemsSelf-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key ...
Comments