Historically, the majority of data training has taken place at the classroom level to help teachers better understand the applications and benefits of data-informed decision making. However, data application does not stop at evaluating teachers’ performance only. It can help school and educational leaders at district or national levels develop holistic frameworks for decision-making, instead of being overreliant on random requests or anecdotal evidence.
For teachers, understanding students (from mental records, family background to their physical health) has a tremendous impact on identifying student needs and planning effective supports. For administrators, understanding the strengths and weaknesses across schools helps them to not only identify student needs at an aggregate level, but also how they can plan their resources accordingly. In this segment, we will talk about three areas of school that can be significantly improved through effective use of analytical data: curriculum review, teacher professional development and resource allocation.
Typically, school leaders only use students’ mid-year and end-of-year assessment scores as the only valid data points to measure whether there has been collective growth in student performance. While this is a good start to using data, it is still quite minimal.
In a school that effectively uses data-informed decision making, leaders will look at many different types and levels of student data such as social scores, mental wellbeing scores, or if progress has been made only in certain classes or in a specific subject.
As educational leaders, you have to critically ask these questions: “Is there any lesson or part of the curriculum where my students are failing, or getting lower than median scores? Is there anything that teachers can do better regarding the curriculum?” If the answer is yes, take a look at data from previous years. Do you see a pattern among the last few years? If there is a pattern, there must be a schoolwide effort to adjust the current instructions for that specific unit.
Try to see if there is any improvement that can be done to the lesson plan: for example, you can provide students with more learning scaffold, make that unit optional, integrate different learning methods like PBL, etc. An annual curriculum review like that is a critical opportunity to evaluate the effectiveness of the national curriculum and determine the impact, positive or negative, on student achievement.
Teacher Professional Development
Determining teacher learning goals for professional development is driven by identifying school instructional goals. To determine these goals, all available data sources should be reviewed, including summative and interim assessments, behavior records, and curriculum maps. Using data-informed decision making ensures professional development resources target these exact areas of improvement.
One common professional development mistake is assuming all educators need the same kind or same level of professional development.
Data helps administrators determine which teachers should participate in which sessions (i.e., all of them, a certain grade, specialists, etc.). Data also help divide educators into various learning levels (e.g., beginner, intermediate, advanced, etc.) based on their current evaluation scores. In this regard, educators, just like students, greatly benefit from differentiated learning.
When administrators allocate resources, they are tasked with determining the ways in which their time and money will address educational goals. On a district level, an administrator may allocate an equal amount of resources to every school in the district. If that is the case, some schools might find that amount inadequate to sustain operations, while other schools only need a fraction of that budget to maintain educational equity for their students. With the help of data, district administrators can direct resources to where it yields the most impact.
On a school level, school leaders also need to make judgment calls on how much money to invest in maintaining current facilities, obtaining novel technologies, building infrastructures, etc. In this decision-making process, leaders can utilize data on facilities to determine what types of investment should be prioritized, and what can wait.