Research
I have participated in projects that have provided me with interdisciplinary experiences. Overall I design technological environments for teaching and learning.
01
DataViz
As data visualizations are now regularly encountered in the news and social media, knowing how to read and communicate information from data visualizations becomes as important as the ability to read and write text. As data visualizations permeate our daily lives, there are not many opportunities to gain an understanding of data visualizations outside of higher ed. DataViz is a learner-centered tool developed for users to interact, explore, and learn about data through data visualizations. Data Diamonds is an enrichment program designed to test user experience with DataViz.
02
STEM Education
Applying user experience (UX) design principles within education contexts, particularly science, technology, engineering, and mathematics (STEM) education settings. This work aims to improve curricular design and pedagogy practices, provide results to inform the design of online repositories and library interfaces, and provide results from data gathered to inform the design of formal and/or informal learning tools.
03
Data In Motion
Data in Motion creates a technology environment for participants that merges data analytics, computational thinking, and physical activity. The objective is to design a learning experience around different types of wearable technologies and data visualization. Activities are designed to provide participants the opportunity to explore statistical questions that are meaningful to them and allow them to become creators of data science artifacts.
04
Earthquake Detective
Engaging users in an experiment to test if human ears can replace the process of a professional seismologist in identifying dynamically triggered seismic events. Ordinarily, this process involves interactive data processing and visualization efforts on a volume of earthquake recordings (seismograms). Users are asked to listen to relevant sections of seismograms that are accelerated to audible frequencies and identify if signals are in one of four categories: earthquake, tremor, noise, or none of the above. User-submitted results are collected, cleaned, studied, and analyze if citizens can help detect and classify dynamically triggered seismic activity.
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