Student data: How using data analysis can help your students succeed 

Student data analysis is key to making instruction and intervention decisions that enable all students to succeed in learning. But organizing data and understanding how to analyze it to support your students can be an overwhelming task.

Are you looking for a streamlined solution to student data that can give a clear picture of how to best help your students succeed in learning?

This guide can help.

What is student data?

Student data is information gathered about individual students to form a full picture of student learning and needs.

There are multiple facets to student data tracking, including:

• Academic data
• Social-emotional behavior (SEB) data
• School climate and culture data

Why do teachers collect student data?

Teachers collect student data in order to:

• Ensure students are meeting learning objectives
• Identify the most effective instructional methods
• Respond appropriately to students’ whole child needs

Why is student data so important?

Student data tracking is important because it helps educators to create optimal learning experiences for each student. Educators are using data to improve instruction for students with a wide variety of needs, including those who may be experiencing dyslexia, mental health concerns, ADHD, or trauma.

Studies have shown that data-driven education has a high success rate because of its ability to:

• Allow students to place their focus on the areas of instruction where they need it the most
• Give teachers, administrators, schools, and districts the tools they need to adapt their instructional techniques to best meet their students’ needs

3 categories of student data analysis

Holistic student data is a necessary component for educators who truly desire to understand the “whole student.”

How so? Consider the following example:

Cheyenne is in grade 8 and, until last semester, had always been a straight-A student. But recently she has started to receive Cs and Ds.

Although her schoolwork is suffering, the problem’s cause may have absolutely nothing to do with academics. Ultimately, the root of Cheyenne’s struggle may be found in:

• Family or home issues, such as divorce or a parent’s job loss
• Bullying
• Illness or the death of someone close to her
• Feeling uncomfortable or unsafe at school
• Food insecurity
• Problems with her vision

Having access to Cheyenne’s holistic student data can help to pinpoint the cause of such a dramatic change—and can help to identify the individualized help she needs to get back on track in both her daily life and academically.

As noted earlier, there are multiple types of student data. Without visibility into each of these components, it can be easy to overlook the data needed to understand exactly how to help an individual student properly.

Let’s take a look at each data type.

#1: Academic data

Academic data is a collection of information that reflects how students are doing in their academic studies, such as math and reading.

It is composed of student assessment data from tools such as:

• Universal screening
• Progress monitoring
• Interim and formative assessments
• Summative assessments

Academic data also includes:

• Courses the student has completed
• Grades the student has earned
• Completion of academic requirements

Examples of academic student data

Academic student data is gathered from:

• Assessments
• Class grades
• Performance tasks

Specific examples of this type of student data include:

• Report card summaries
• GPAs
• Lists of students who are receiving failing grades in a class
• State achievement test scores
• Standardized test results

#2: Social-emotional behavior (SEB) data

The field of social-emotional learning and mental health has shifted toward a dual-factor model in which practitioners and educators are encouraged to look at both social-emotional problems and social-emotional competencies.

SEB competence is defined by the presence of social-emotional and academic enabling skills that help students learn and relate to others, such as:

• Self-awareness
• Social awareness
• Self-management
• Relationship skills
• Responsible decision making
• Motivation
• Academic achievement

It also refers to the absence of problem behaviors that prohibit learning and healthy relationships, like:

• Aggression
• Noncompliance
• Disruption
• Worry or fear
• Withdrawal or avoidance

To properly evaluate a student’s social-emotional behavior, educators need to take into account two types of data:

1. Quantitative data: This includes anything that can be numerically quantified, such as SEB screening results, attendance data, classroom grades, or assessment scores.
2. Qualitative data: This incorporates data that describes qualities or observations and includes journal entries, personal interviews, hallway conversations, and teacher intuition.

Without high-quality SEB data, it will be difficult to identify and act upon any relational problems a student may be exhibiting.

Examples of social-emotional behavior (SEB) student data

SEB data can be broken into several broad categories, as outlined below.

Student attendance data:

• Tardies
• Full and partial absences
• Extended periods of absence

Behavior incident data:

• Classroom-managed issues—These are the smaller problems that a teacher generally handles in class without assistance from the school office, such as:
  • Disruptive behavior
  • In-class phone usage
  • Late or incomplete assignments
  • Excessive talking
  • Cheating
• Office-managed issues—These generally require administrators’ intervention, such as:
  • Bullying or physically injuring another student
  • Bringing a weapon to school
  • Possession of illegal substances
  • Detentions
  • Expulsions
  • Counselor referrals

Social-emotional competencies:

• Has a positive attitude
• Gets along with others
• Can work independently
• Is able to take turns

Without access to the proper tools, student data tracking for SEB can be an overwhelming task.

#3: School climate and culture data

The whole student data picture also includes information relating to both the climate and culture of the school.

School climate and culture data take into account factors such as:

• How does the student feel while they are at school? Do they feel safe?
• Does the student have friends?
• What is the student’s level of confidence in academic and social situations?
• What is the overriding atmosphere of the school?
• Is there racial or gender division?
• Do students feel like they have support from adults in the school?

A deficit in any of these areas can have a major impact on the student’s life and their ability to learn.

Examples of school climate and culture data

Some examples of data about school climate and culture include:

• Student surveys
• Parent/guardian surveys
• Teacher surveys
• Open forums
• Town hall-type meetings

One measure of this data that was developed by the US Department of Education is a collection of school climate survey questionnaires. Many surveys like this can be found online and utilized to gather data about your school community.

When given the option to submit a survey anonymously, most students are willing to openly and honestly share their feelings about their time at school. For this reason, survey data often exists at the “summary” level instead of the individual student level.

School climate and culture data can be broken down into five broad categories:

1. Teaching and learning
2. Interpersonal relationships
3. Institutional environment
4. Safety
5. Social media use/perceptions

Depending on survey results, schools and districts may want to focus on different categories at different times to reach their data-driven goals.

How can schools prepare for data exploration?

It’s up to school and district leaders to create a culture that values student data collection and use. Here are two concrete ways they might do this.

#1: Promote a data-friendly culture

In a school with a pro-data climate, teachers and administrators look for reliable information to guide their decisions regarding curriculum and instruction. Creating this atmosphere may be a gradual process, especially if you have a lot of staff members who are used to doing things the old-fashioned way.

Two ways you can foster this data-friendly culture are:

1. Identifying goals for school improvement as they relate to the data.
2. Encouraging school staff to think about how they make important decisions.

#2: Create a data exploration team

Data exploration should be done by a larger group if possible. A team effort is critical to the success of data-driven decision making because:

• It’s too much work for one person or even a few people to do themselves
• A broad plan creates buy-in and consensus, which leads to more support for decisions that are made

Serving on this team can be a lot of work, so you may want to rotate the group members on a regular basis.

How can you best analyze student data?

To effectively analyze student assessment data, you should:

1. Compile all student data in a single platform.
2. Analyze data at the universal tier (by district, by school, by grade, or by class).
3. Analyze data from different groups of students.
4. Analyze data for individual students.

As a teacher, you are well aware that the right data used in the right way at the right time can empower teams with the information they need to make the very best decisions for enhancing student success. But in some cases, the details of understanding and implementing student data can be a barrier in the process. How do you find the time to compile, organize, and analyze data?

In these cases, Renaissance can help.

Use student data to drive instruction: the Renaissance solution

Our comprehensive assessment solution provides a complete set of assessment tools to understand each student’s academic and social-emotional behavioral (SEB) learning and needs in terms of both standards and skills.

Data from these assessments—along with all other whole child data—are then integrated for easy analysis in our eduCLIMBER platform. eduCLIMBER provides whole child data visualizations and tools for essential MTSS processes, including need identification, intervention tracking, effectiveness reporting, and collaboration.

In fact, eduCLIMBER is a single hub for all student data sources, including:

• Academic data
• SEB data
• Intervention data
• Behavior incident data
• Attendance
• Qualitative data, such as student observations

As educator Patti Wilson put it:

“It’s one thing to look at grades and see a student is struggling across most content areas. But when you’re able to piece together additional pieces of that data story—that they lack readiness skills or have difficulty with sustained attention and initial engagement—that informs the type of intervention needed.”

Data collection helps you to more deeply understand why your students are performing the way they are and what you should do about it. Connect with an expert today to learn more about eduCLIMBER and other Renaissance solutions.