Mobilize is an NSF grant that was written to bring computational thinking to high school math and science classrooms through participatory data collection (using a smartphone app that allows students to collect data on themselves and their communities) and "data science."

As a graduate student, I worked on the grant writing curriculum, working on an R package, and leading professional development sessions for in-service teachers from the Los Angeles Unified School District. I've written a blog post about the curriculum we developed, a year-long Introduction to Data Science curriculum at the high school level.



During my time on the grant we tried several computational tools for the Mobilize activities. Ultimately, we decided to use R and RStudio.

R is a programming language for statistics and data analysis used by many professionals. It is an open-source programming language, which means both that it is free (good for school districts with limited budgets) and it can be easily extended (so we could write our own extensions for Mobilize).

Through my experience with Mobilize, I became convinced that the best way to teach R was with an extremely small number of functions, handpicked for the syntactic consistency.

Many of the tasks we wanted teachers and students to be able to perform were already available in a consistent syntax through the mosiac package, lattice graphics, and the formula syntax more generally. However, for some tasks the syntax was not consistent or the function was not available, so the mobilizr package was born.


R and RStudio documentation

Recently I've been working a lot on documentation for R and RStudio. One major place this can be found is the Mobilize wiki. The wiki has content on almost everything Mobilize-related, but I can take credit for the R and RStudio documentation.

Then, because some students and teachers are more visual learners, I've produced a series of introductory videos, which are hosted on the Mobilize YouTube. Again, I can only take credit for the R and RStudio videos.

PD group

Professional development

Many of the teachers in our program are initially unfamiliar with exploratory data analysis, so the professional development sessions I teach encourage creative exploration and plot creation. "In-service" means current teachers, not those who are still in the process of becoming licensed. Because we work with current high school teachers, our professional development sessions are typically a day or two (if they take place during the school year) or an intensive week (over the summer).

Below are some examples of motivating presentations I use to introduce the ideas in these sessions.

Data are cool
Participatory sensing
Critical cartography

Permuted graphics

During the summer of 2012, we also explored some of the ideas of Heike Hofmann, trying to learn to "calibrate" our eyes to recognize true patterns in data, as opposed to patterns which might have appeared due to chance. The activity was so popular that we've repeated it many times with teachers and students.