Assessment data can give educators crucial information to help guide instructional decisions. However, knowing which data to use and understanding how to interpret historical trends in student data can be challenging.
Simply looking at one source of student data isn’t enough.
Identifying historical trends in student data and then using the data to make intervention decisions is key to student success.
We’ll discuss what historical trends in student data are and how educators can use them to drive student instruction.
Table of Contents
- What Are Historical Trends in Student Data?
- Trends vs. Patterns in Student Data: What’s the Difference?
- How to Identify Historical Trends in Student Data
- 4 Reasons Why It Is Important to Identify Historical Trends In Student Data
- Illuminate Education: Using Student Data to Drive Instruction
What Are Historical Trends in Student Data?
Historical trends in student data are the lows and highs of a student’s performance over time.
It’s important that multiple scores from a single measure be compared to one another over a period of time (e.g., scores from fall, winter, and spring). Both short-term and long-term data should be considered, but long-term data will be more helpful in finding trends and patterns in a student’s skills.
Historical data help identify where the student is succeeding and where deficits are still present.
Trends vs. Patterns in Student Data: What’s the Difference?
Let’s start with general definitions of trends and patterns and then see how we can apply those definitions specifically to student assessment data.
Trends refer to the general direction of data over time for an individual student.
For example, if you are watching the stock market, you may see one of these three trends:
- Downwards; or
When considering upward and downward trends, remember that the movement isn’t always up or down. A stock with an upward trend may experience some intermittent downward movement, but the overall movement over a period of time is upward.
The same is true with downward trends. Though there may be some short stints of upward movement, the downward trend is identified with lower peaks over time.
Sideway trends are characterized by movement that stays level over the long haul.
Though trends and patterns are similar, they aren’t quite the same thing.
A pattern refers to data that repeat in a noticeable way, across multiple students
If we go back to our stock example, patterns may be found when considering price, sale volume, or closing price. Patterns can be found in upward or downward trends and may mark the start of a new trend.
Patterns in Student Data
In the educational assessment arena, “Patterns are common results in data for a group of students.”
Patterns should be examined specifically, allowing the instruction to target the student’s needs.
Looking at specific learning objectives is important in order to gain the information necessary develop instruction that will help the student succeed.
How to Identify Historical Trends in Student Data
Assessing students is just the first step. After the assessment is over, educators are equipped with data to help in decision-making.
But how are those data used?
Below we’ll look at data analysis procedures that help us take apart and inspect the assessment data for beneficial use.
Data Analysis Procedures
When data are organized and laid out in a particular way, they help educators see patterns in student performance that are helpful in the analysis of the data and decision-making. Patterns should be specific, so the instruction can be applied where it’s most appropriate.
Looking at patterns and needs separately can be most helpful.
For example, a pattern in assessment data may look like this:
Students averaged 85% on an assessment, but more than 70% of the students missed the same three questions.
Needs are the particular factors that affect a student’s performance. Need should not necessarily be equated with weakness, as a need for enrichment may be identified in an area where a student is strong.
Areas of need can fall into two categories:
Going back to our pattern example above, we may find that the three questions the students missed all dealt with the same particular content standard.
Looking for patterns of need in clusters of students, while being more efficient, also helps educators operate from a big-picture perspective.
Identifying patterns of need across students gives educators the opportunity to:
- Understand trends in students’ strengths and challenges; and
- Work together around strategies that further encourage student strengths and improve other areas of instruction
Identifying the pattern of need first is crucial to finding the right solution. The next process, root cause analysis, keeps educators from simply addressing symptoms only to find the issue returns. Root cause analysis helps us get to the actual problem and avoid repeat recurrences.
Root cause analysis helps educators determine:
- What has happened and why it happened; and
- A strategy to remove the root cause
Root cause analysis is used best as a proactive tool to help solve problems before they start and helps teachers and administrators know where to spend the most time and energy to get the desired results.
4 Reasons Why It Is Important to Identify Historical Trends in Student Data
Historical trends in data give us a fuller picture of a student’s current situation, whereas isolated data only show a small piece of the whole picture.
When we have the full picture, it aids us in:
- Identifying ongoing issues
- Avoiding unnecessary intervention
- Avoiding repeat intervention; and
- Measuring the effectiveness of previous interventions
Let’s look at these four reasons along with examples to better understand why identifying historical trends in student data is so important.
#1: Confirm an Ongoing Issue
When we look at a single data point over a short period, it’s easy to miss struggles and issues that have been compounding over time.
Alternatively, looking at long-term data can help us see patterns that have been developing over time. Once those patterns are identified, we can use the data to create the right plan and strategy to help the student improve in the area when a deficit has been seen.
Focusing on trends and patterns lets us identify specific skill needs, and the greater the need, the more time we should be taking to align the intervention with the patterns and trends discovered.
For example, you may look at one data point of a recent assessment and determine the student is deficient in a particular skill. But before scheduling intervention, you look at the student’s historical data for that same skill and note that the student’s prior scores were stronger.
Perhaps the most recent score was an isolated event, and more digging and research may be necessary to see if intervention in this skill level is actually required.
#2: Avoid Unnecessary Intervention
Looking at a single data point over a short time may result in hastily scheduling intervention, which is a drain on resources.
Instead, some time should be well spent looking at several data points over a longer period of time. If there seems to be a pattern over a longer period of time with a particular skill, there is stronger evidence that intervention is necessary.
Can unnecessary intervention be harmful?
In the journal article above, Zhao says, “Educational interventions do not only have linear effects. Their impact and outcomes must be assessed in a differentiated way by relating them to competing goals, taking unwanted side effects into account, and considering the distribution of the effects depending on the context.”
So, according to Zhao, unnecessary intervention may have negative effects because it takes time away from other instruction and requires resources that could be used in a different way
Correctly matching a student’s skill need with the best intervention benefits that student and also ensures that the resources and specialists are available to other students when intervention is necessary.
#3: Avoid Repeating an Intervention
Looking at historical data can also give some information about what interventions have been prescribed before and what the results have been. Without looking at the historical patterns and trends, educators may unknowingly be repeating interventions that weren’t effective.
In that case, the information and data educators unlock when looking deeper into past interventions help them determine new interventions and strategies with great success.
When looking at past interventions, we need to ask questions like:
- What other interventions have already been put in place?
- Was the intervention implemented correctly?
- What was the result of the intervention?
Rather than repeating interventions, we need to identify what worked and what didn’t and then make decisions armed with that information.
#4: Find Out if the Student Was on Track in Previous Interventions
Student interventions can be ineffective for a variety of reasons.
In determining which interventions are appropriate, it’s essential to understand what particular factors make a previous intervention ineffective.
One factor contributing to an intervention’s effectiveness is whether it was implemented with fidelity.
Fidelity in intervention means the instruction was implemented as it was intended. Fidelity is accomplished when:
- The original plan was followed
- The intervention was done at the recommended frequency
- The plan lasted for the recommended duration
- Progress was regularly and correctly monitored
- Student data were compared with benchmarks
If intervention fidelity wasn’t achieved, it’s important to understand why. Some reasons for unfulfilled interventions include:
- Inaccurately implemented instruction – either regarding frequency, duration, or how the instruction was implemented
- Frequent student absences
- Lack of student involvement
- Faulty progress assessment administration
Looking carefully at each of these points is integral to understanding why a prior intervention succeeded or failed — and also provides insight into which future interventions will help the student move forward.
Illuminate Education: Data that Informs Instruction
If you’re unsure how to collect the data or how to arrange the data to be useful, Illuminate Education can help. We have the tools to help you make data-informed decisions about students’ academic and social-emotional behavior (SEB) needs so that resources are allocated effectively and efficiently.
Contact us today or schedule a demo to learn more.
Illuminate Education equips educators to take a data-driven approach to serving the whole child. By combining comprehensive assessment and MTSS management and collaboration tools, the Illuminate Solution enables educators to accurately assess learning, identify needs, align whole child supports, drive system-level improvements, and equitably accelerate growth for every learner.
Ready to discover your one-stop shop for your district’s educational needs? Let’s talk.