Panel Report 3

Jeff Hare: Stanford Dissertation Browser

Jeff Hare is interested in the interaction between different study fields. An aspect of his project that stood out to me was what began his research. He was interested in how we foster high-impact research and the effectiveness of research sharing between institutes. This is a topic that my pre-med friends and I have discussed before.

Hare surveyed many university-majors and PhD dissertations to compare departmental similarities of text. He showed us the different types of data visualizations techniques he used and discussed their pros and cons. The two most important things I learned were how you can change models with different algorithms and some data visualizations may be easy to interpret but may lead to a wrong conclusion.

Kristine Kohlhepp: EMAR Project

Kristine created two prototypes to access mental health of a student in real time. The initial prototype would record and store data from multiple students in real time, ask them questions based on those answers, move around to engage with students, and analyze and share the data. What was most interesting to me about this prototype was that she made it with common household items. Her explanation was sometimes you do not need high-fidelity items for a prototype. This goes against what I am use to; I am a perfectionist. Even though it is a prototype, I would want to make it the best I could. Another thing that interested me was how running off an Arduino was hindering her goals for the project.

Reflection on Sprint Deliverable

Using the advice of Jeff Hare, I believe I could have created a wider variety of data visualizations for the Tableau sprint. In the future, I will analyze if the user will easily understand the data I am trying to present. The data visualizations that I created may contain errors and lead my user to wrong conclusions about the safety in certain areas. With the information from Kristine, I create more functionality to my loud music solving prototype. This would allow me to receive more feedback, during testing, to figure out which features users like best and potentially improvements that could be made.