3 Tips for Making Data-Based Decisions like a Pro!

By Jennifer Bessette, Director of Professional Services

Data. Some people love it. For others, it can be a real four-letter word. Whether you love it or hate it, data is here to stay! It is CRUCIAL to student success. Without data, we have no way to objectively analyze student progress. However, simply collecting data without pausing to reflect & make decisions gets us nowhere. Check out these great tips to ensure a successful data-driven school year!

1. Collect a Baseline
Prior to teaching a new goal to your students, it is a great best-practice to begin by collecting baseline data. You can think of baseline data as a pre-test: we’re simply testing the student to see what he already knows before we begin teaching. Imagine you’re planning on teaching a student to identify community helpers. Without collecting a baseline, you likely don’t know if the student knows some community helpers, all community helpers, or no community helpers at all. This would make it difficult to know where to begin. Once you collect some baseline data, you may discover that the student can already identify the police offer, the fire fighter, and the doctor, but cannot identify the postal worker, the construction worker, or the teacher. You now know where to begin your lesson!

Baseline data also documents the awesome work you’re doing teaching your students. Without baseline data, someone might wonder if you actually taught this skill to the student or if he already had this skill to begin with. If your baseline data shows the student cannot accurately and reliably perform the skill, but after you begin teaching, he now has the skill, you are showing that it is your teaching that made the difference!

2. Be Specific
Analyzing data can be quite difficult if you were not specific during data collection. Take the example of teaching your student identify objects. If the student is not mastering this skill, why not? Take a look at the following graph:

What do you know about this student by looking at this graph? You can see student has not been able to perform this skill with more than 66% accuracy. Why? Honestly, it’s difficult to know the full story looking at this graph. Let’s take this same data, and present it in a different way:

Looking at this graph, you can now see the student is able to identify the ball and the car, but is struggling to identify the block, book, and doll. Now you have a much better sense of the student’s strengths & challenges, and know where you should focus your efforts moving forward!

3. Check for Pre-Requisite Skills
Sometimes students struggle to make progress on all aspects of the goal no matter how hard you try. This can be frustrating for educators, parents, and the student. Imagine a student has been learning to tell time, and has shown very little growth on this skill.

One question you might ask at this time is: Does the student know all of her numbers? If not (or maybe she’s forgotten), telling time would be a very difficult skill for her! This would be a great time to stop & test for the pre-requisite skill of Identifying Numbers.

Clearly, you can see the student did not have this pre-requisite skill. However, after pausing to teach this skill, it looks like the student can now identify all numbers 0-12. Let’s return to the Telling Time lesson.

There! That did the trick. Now that your student can identify all numbers 0-12 and she is able to tell time to the whole hour. Success!

What are your favorite tips for making data-based decisions? Let us know in the comments below!

Using Baseline Data to Inform Instruction

It’s no secret that data can be daunting. For some of us, the word “data” means pile-high clipboards and stacks of complex data sheets or binders of reports. Data can also feel like never-ending lists of impersonal statistics that are difficult to difficult to comprehend and cumbersome to analyze.

When we look at data through this lens, it becomes impossible to see how data can improve student performance and enhance holistic education practices, but it can. So let’s break it down.

In it’s simplest form, data is a collection of facts and statistics that can be used for planning or analysis. All educators are charged with ensuring they use data to inform instruction, so students benefit from evidence-based techniques and approaches to education. Data can also be helpful in monitoring student progress and identifying areas of need through assessments.

Pre-tests, homework, attendance, grades and test scores are all data sources that help inform classroom, school and district decisions.

What is baseline data and how is it collected?

Baseline data is a measurement that is collected prior to intervention or teaching starting. It can be collected through various measures including: percent accuracy, frequency, duration, rate and intervals. When selecting a measure of baseline data collection, it is important to consider how intervention or instructional data will be collected to ensure consistency.

Percent accuracy is collected by calculating the number of target responses divided by the total number of opportunities. Some examples of this measure of data collection include:

  • Percentage of spelling words spelled correctly
  • Percentage of correct math problems
  • Percentage of items correctly identified

You can record frequency by tracking the number of each instance of a behavior. Collect frequency data with counters, tallies or a similar technique. Some examples of this measure of data collection include:

  • Number of words read
  • Number of times a student gets out of their seat
  • Number of times students raise their hands

Duration is measured by tracking how long a behavior occurs. To record duration, start a timer when the behavior commences and stop the timer when the behavior ceases. Some examples of this measure of data collection include:

  • How long a child engages in tantrum behavior
  • How long a child engages in peer interactions
  • How long it takes to complete lesson plans for one week

Rate is calculated by recording the number of behaviors per unit of time. Some examples of this measure of data collection include:

  • Number of words read per minute
  • Number of math problems completed per minute
  • Number of tantrums per hour

Interval data can be used when tracking each occurrence of behavior is not possible, or when the start and end time of the behavior is not clear. You can also use interval data to obtain a sampling rather than an exact count. Also you can measure interval data by determining a preset time interval and then marking whether the behavior occurred during that interval. Some examples of this measure of data collection include:

  • The occurrence of body rocking
  • The occurrence of staying in an assigned area
  • The occurrence of off-task behavior

Why is baseline data important and when is it used?

Without baseline data, many educators run the risk of failing to show progress in a number of student populations, such as with at-risk students, English language learners or students with disabilities.

Baseline data should always serve as a starting point for instruction. It justifies the need for behavioral intervention plans and allows for shifts in instruction that help every student achieve progress. Also it aids in proper selection of skill acquisition activities and allows educators to determine appropriate interventions with a degree of accuracy that increases likelihood of student success.

With baseline data, educators can essentially create a roadmap for students to achieve their educational goals and gain the support they need to master skills, lessons and more. Despite growing needs and changes in the educational landscape in America, baseline data continues to be a useful tool for educators to track and analyze student progress across the country. When you use data to guide instruction, there’s no telling what you’ll discover.

But one thing’s certain: You’ll create a dynamic and individualized experience your classroom that will move your students and school forward.