We will focus on asking high-leverage questions, then using data mining, canned reports and extracts, and other options to pull relevant data from Skyward and Homeroom to answer these questions.
Effective data use begins with a good question. This establishes a purpose for using the data and making predictions to enhance learning. A few sample questions are below, but you can also view more ideas, if needed.
- What is the relationship between student attendance and achievement on the Smarter Balanced ELA assessment for my students?
- Do Kindergartners who ride the bus have fewer absences...and if so, what are the most effective supports for increasing attendance with our other students?
- What are the characteristics of students who do not complete high school in our district?
Use this guide to frame your inquiry process. It's okay if your question isn't perfect, or if you end up starting over. This process is rarely a straight line.
You can access a variety of supporting documents on the district web site. You may also log in to the Help Center on the School Data Solutions web site to search for answers to specific questions, view tutorials, or suggest improvements. We also put together a summary of quick tips and tricks on student groups and data extracts.
There are many places to find additional data sets for education (and other areas!). Here are a few to get you started:
- You can download assessment, attendance, demographic, and graduation data for our state from the Data Files area of the OSPI web site. Some files are also available as performance indicators. For even more data, including fiscal, human resources, and transportation, visit Data Administration.
- The Educational Research Data Center (ERDC) has built a longitudinal data system that includes information on Washington students across multiple sectors. These sectors include early learning, K-12, post-secondary, and workforce sectors. These data are shared with ERDC by partnering agencies and institutions across the state. In this way, ERDC acts as a kind of “central hub,” where partnering agencies, institutions, and organizations can pool their data and seek answers to questions that none of them have the resources to answer by themselves.
- Visit the National Center for Educational Statistics is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. You may also be interested in the Civil Rights Data Collection which collects and publishes educational data related to civil rights and equity.
- Many public organizations have open data collections (and, if not, you can obtain information through the public disclosure process). Here are some links to additional collections, including wikipedia edits, air quality, Pokemon, and more.
- Head over to Data USA, a "comprehensive website and visualization engine of public US Government data" to find, map, compare, or download all manner of data related to living in the United States.
Feinberg, Melanie. (2017). A Design Perspective on Data. 2952-2963. 10.1145/3025453.3025837.
What do we do about missing data...and why is it missing in the first place? Mimi Onuoha has started a repo to explore this topic. At the 2016 Tapestry Conference, Eva Galanes-Rosenbaum shares that the plural of anecdote is not "data"...except when it is as a way to show how we can mitigate the effects of missing data. You may also be interested in this recent post on four methods to deal with missing data. Andrea Rossi teaches about methodologies for surveying hidden, marginal, or excluded populations. You can view a sample syllabus for ideas and links to relevant research in this area.
It's always important to be aware of the biases we bring to our work. Use this infographic codex to view all possible cognitive biases in one spot.
Alan Smith of the Financial Times has talked about the need to become competent critics: "people who are capable of deconstructing the role of chance, spotting the potential for using visual methods [in reporting]." Part of that work includes thinking about what is or is not helpful about data stories. You can watch Alan talk about their process in this short story from the 2016 Tapestry Conference.
During our work in this session, we looked at two very different types of visuals.
- The first collection was four charts developed by students of Enrico Bertini. Visit this thread on Twitter to review the charts and the idea exchange.
- We also looked at some examples made by Nicholas Rougeux that represent different pieces of classical music. You can learn more about his process, as well as the various iterations of ideas he had. Sometimes, as data designers, you have to kill your darlings.