Using Data to Make Data-Driven Instructional Decisions: Part 4 of 4 – Taking Newfound Knowledge from our Data and Making a Decision

Over the course of the last four week in the "Using Data to Make Data-Driven Instructional Decisions" series, we have outlined how to collect data, clean/organize data, and conduct univariate and multivariate statistical analysis on the data to transform it into newfound knowledge that can be used to make a decision. While this sounds like … Continue reading Using Data to Make Data-Driven Instructional Decisions: Part 4 of 4 – Taking Newfound Knowledge from our Data and Making a Decision

Using Data to Make Data-Driven Instructional Decisions: Part 3 of 4 – Using Statistics to Transform Data into Knowledge

Welcome to Part 3 of 4 of the Using Data to Make Data-Driven Instructional Decisions Blog Series! Today, in Part 3, we are going to focus on using statistics to transform our data into knowledge. Statistical analysis on a data set allows educators to essentially mine information from the data. What this does is provide … Continue reading Using Data to Make Data-Driven Instructional Decisions: Part 3 of 4 – Using Statistics to Transform Data into Knowledge

The Power of Analyzing Statewide Education Data: Self-Affirming Conclusions that Prompt Us to Dig Deeper Into the Data

Data is powerful, especially in the realm of education. At times, it may be self-affirming while on the other hand, it makes you question your current practices and policies because the data identifies further ramifications that make us have to dig deeper to determine what’s going on as well as devising solutions to the problems we face as educators. But, most importantly, it tells a story about the students we serve, which then we are called to do something about it as teachers and administrators. Over the last few days, I have been able to analyze several data sets that have been collected from the California Department of Education. The data sets I analyzed encompass all of the 2018 California K-12 School demographics, state testing scores, attendance rates, suspension rates, and funding mechanisms. My goal was to transform the data into several self-affirming stories of what the data is telling us as well as how we dig deeper into the stories the data is illustrating to determine new insights into we how to solve the problems we have identified.