COOLAPPER

Data-enabled Design
Apr 2022 - Jun 2022
Project Overview
Following the data-enabled design approach,  we designed a micro intelligence called Coolapper, which is a sustainable cooking behavior recommender with a reward system and timely data visualization by electricity tracking.
My Contributions
Team work
Team members: Biliang Wang, Hangcheng Yang, Zun Wang
Taking charge in research prototype realization, experiment designing and conducting, and contributed to research question forming, data collecting and insights concluding.
Design Process
Reducing greenhouse gas emissions and improving energy efficiency have become increasingly important priorities in households. In this study, we targeted cooking activities and used data-enabled design as the framework to explore how to effectively communicate energy consumption data to users during the cooking process to promote sustainable cooking behaviors.

The project was developed in a data-enabled design process that includes two steps: the contextual step and the informed step. In the contextual step, we started with quantitative data research to learn people’s attitudes toward cooking in a general view. After that, a data probe was used to collect detailed data in the user’s home during cooking to generate further insights.

Based on the outcome, we designed Coolapper, a sustainable cooking behavior recommendation system with a gamified reward system and real-time data visualization. By deploying Coolapper into two users’ homes during the informed step, insightful feedback was collected while constructive design suggestions were concluded for future design and research.
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