Understanding and Correcting Inaccurate Calorie Estimations on Amazon Mechanical Turk
Autor: | Lillio Mok, Joseph Jay Williams, Stephen Gou, Brenna Li |
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Rok vydání: | 2019 |
Předmět: |
Estimation
Educational method Calorie Amazon rainforest Computer science 05 social sciences Applied psychology Contrast (statistics) 020207 software engineering 02 engineering and technology Consumer knowledge 0202 electrical engineering electronic engineering information engineering Food energy 0501 psychology and cognitive sciences 050107 human factors Learning at scale |
Zdroj: | CHI Extended Abstracts |
Popis: | Current research on technology for fitness is often focused on tracking and encouraging healthy lifestyles. In contrast, we adopt an approach based on improving consumer knowledge of food energy. An interactive survey was distributed on Amazon Mechanical Turk to assess how well crowdworkers can judge the calories in a series of foods. Our subjects yielded results comparable to traditional participants, exhibiting well-known phenomena such as underestimating the energy contained in foods perceived to be healthy. Several techniques from the online education literature, such as prompts for reflection, were also investigated for their efficacy at increasing estimation accuracy. Although calories were more accurately judged after applying these methods on aggregate, the effects of individual techniques on our participants were inconclusive. A more thorough investigation is thus needed into effective educational methods for correcting calorie estimations on the Web. |
Databáze: | OpenAIRE |
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