We live in a world where we are able to collect vast amounts of data because of the educational technology we use in our classrooms and schools. Think about it – every time a student logs onto an edtech tool you use in your classroom, data is logged of their interaction with the software tool. What this means is that when we have our students engage in lessons where edtech tools are a mechanism to deliver instruction and to assess student learning, we are collecting A LOT of student data. Unfortunately, this data is not always used to make instructional decisions. Now, with the advent of edtech being in the majority of all classrooms over the last five to ten years, teachers now have the opportunity to learn how to collect and analyze the data to help them monitor and adjust their instruction to make instructional decisions to meet their students where they are at in regard to their learning.
Unfortunately, the data literacy of teachers and school leaders to do this is not where it needs to be. Data literacy is one’s ability to collect, compile, and clean data in addition to conducting a statistical analysis to derive new knowledge from in order to make a decision (Mandinach, 2012; Mandinach & Gummer, 2016). My research has shown me the efficacy to utilize various data practices is high, but in reality, the true ability to use data to drive instructional decisions is low (Rhoads, 2019). Therefore, one of my major goals is to teach data literacy so teachers and school leaders can make data-driven decisions to improve instruction and student outcomes. Since there’s such a need in K-12 education to learn data literacy skills, I am going to create a four part blog series where I am going to show teachers and school leaders how to build their data literacy skills so they can make data-driven decisions on consistent basis.
For Part 1 of this blog series on data literacy and data driven decision-making, I am going to go step by step to show you how you can collect data from various edtech tools teachers use everyday in their classrooms. In addition, I am going to briefly go through the process of exporting the data to an Microsoft Excel or Google Sheets spreadsheet. Part 2 of the blog series will cover how to clean and organize the data on a spreadsheet. Then, Part 3 will cover how to conduct basic descriptive statistics on the cleaned data to gain newfound knowledge. Finally, Part 4 of this series will cover how we can use this new knowledge to monitor and adjust and drive instruction so we can make strategic and powerful data-driven decisions.
Part 1 begins today with collecting and exporting data. Let’s get started!
Collecting Data in a Classroom
The first step is collecting student data. We collect data as teachers all of the time. First, we collect data to assess student learning. We also collect data to see our students strengths and areas of improvement. In addition to student learning data, we also collect data on their social-emotional status’s to see how we can best support our students socially emotionally. Ultimately, with this collected data, we use this data to determine how we monitor and adjust our instruction and supports as a teacher to put our students in the best positions to succeed.
Note: There are many data types that I did not mention here that we collect in schools. For the purposes of this blog, I am only discussing some of the major types of data we can collect in classrooms.
Luckily, collecting data is not difficult. Even if you do not use edtech tech, you are collecting data when you input grades into a grade book. When utilizing any edtech tool, student data is collect by using the tool. The data is logged and stored within the program when a student interacts with the software. On many edtech tools such as Pear Deck, GoFormative, Google Forms, b.socrativ, and MobyMax, collected student data can be easily collected and then exported onto a Microsoft Excel or Google Sheets spreadsheet. All teachers must do is build an assessment, lesson, or a set of tasks student must complete on an edtech tool in order to collect the data. Below are two examples of how teachers can build mechanisms to collect data using Google Forms and Pear Deck. Once the infrastructure of these edtech tools is developed, student data can be easily collected once students begin working with the tool.
Exporting the Data
Now, once the data is collected, something has to be done with it in order to begin making it useful. This brings us to exporting the student data. Exporting the data takes several steps. Generally, in many edtech tools, there are areas within the teacher interface where they can access the visualization of data in the form of graphs and tables to evaluate the activity of their students on the edtech tool. For example, when using a Google Form as an assessment or GoFormative, once the assessment is completed, there is an interface teachers can view which shows them the visualization of how students did on an assessment. In regard to an edtech tool like Pear Deck, teachers can review the Pear Deck presentation and see the student responses to the questions posed. This is another example of a data visualization within an edtech tool. After seeing the visualization of the collected student data, there is an option in many edtech tools to export the data onto a spreadsheet. Once this option is selected, a Sheet or Excel spreadsheet is downloaded for you to view and then interact with.
Each edtech tool you use in a classroom collects student data. This data is collected and should be used to help improve instructional decision-making for K-12 teachers and school leaders. While exporting the data to Excel or Sheet spreadsheets may vary among various edtech tools, the option is available on most of the tools you will encounter. Many may say to just stay with the data visualization interface teachers are have the ability to interact with on these tool. This is a good start – but it is not enough. You will see if you take the time to go through the process of collecting, cleaning, conducting statistical analysis, and then using that new knowledge to make a decision, you will catch all of various nuances in the data that the data visualization features miss. In addition, there is so much more you can do when you can conduct your own statistics on the data you collect. You will see this soon!
Stay tuned for next week’s edition of this blog series as we look at cleaning and organizing the data you collect and export from your edtech tools. See you then!
Rhoads, M. (2019). Educational leadership efficacy: The relationship between data use, data use confidence, leadership efficacy, and student achievement. Available from ProQuest Dissertations and Theses Global database. (Accession Order No. ATT 22624797).
Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational psychologist, 47(2), 71-85.
Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37.http://dx.doi.org/10.3102/0013189X12459803
Mandinach, E.B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education. http://dx.doi.org/10.1016/j.tate.2016.07.011