Data-Driven Decision Making in Education: 11 Tips | American University (2024)

Teachers and school systems have long collected and used data in some form or another, whether recording scores in a grade book to track progress and calculate final grades or examining standardized test scores to measure district-wide achievement. However, today’s technology has greatly increased educators’ opportunities to use data and analytics to improve teaching. Teachers can now use tools that track their students’ understanding in real time throughout the delivery of a lesson or provide them with the results of assigned homework before planning their next lessons.

As a case in point, EdPuzzle allows teachers to create or select videos accompanied by questions they can assign for homework. The platform’s data analytics feature tracks which students watched the video and what their scores were. This allows teachers to tailor their lessons according to the results, informing how they group students, scaffold learning activities, and differentiate instruction. It also gives them useful data about who might need extra help and which students might best lead class discussions. Data-driven decision making in education can transform classrooms—dramatically improving teacher responsiveness to students, saving teachers time, and ensuring instruction is relevant.

Data and analytics also play a vital role in addressing inequalities in education. Significant achievement gaps persist between students from low-income households and those from high-income households, and with an increasing wealth disparity, the number of children deprived of equal opportunities in education grows. By analyzing data, educational researchers can identify how factors such as nutrition, pre-K programs, and parental involvement impact achievement gaps. They can also assess whether interventions and policies positively or negatively affect outcomes such as graduation rates and the achievement of key learning milestones. This vital information arms educational leaders with what they need to implement the most promising strategies and to advocate for policies that will most effectively close achievement gaps. For example, the Economic Policy Institute analyzed data taken from the National Center for Education Statistics and focused on two kindergarten classes separated by ten years. The study examined students’ reading and math skills in both groups to see how they measured up against appropriate skill levels for kindergartners. It also looked at achievement gaps between the two groups in light of pre-K participation and “whole child” educational approaches. In this way, authors of the study uncovered important insights about strategies that can improve readiness among low-income students and reduce achievement gaps.

People make data-driven decisions in education in a variety of ways. Teachers use data as part of their reflective teaching practice. Each school day, they observe, make inferences, and then adjust their teaching accordingly. Sometimes the adjustments are on-the-spot fixes. For example, a teacher offers a student a simplified reading assignment customized to her abilities after observation reveals that the student needs extra support in reading. On the other hand, sometimes the adjustments are long-term shifts in instructional methods. For example, a teacher incorporates new student-centered activities after receiving feedback from a supervisor.

Schools use collaborative approaches to make data work for them. Administrators and teachers may study standardized test scores, attendance data, and behavior data to make decisions for their schools. Processes like these can catch students falling through the cracks, identify gaps in curriculum coverage, and better align curriculums across departments and grades. Additionally, in the article “The Collaborative Advantage,” American University professor Dr. Jennifer L. Steele explains, “A collaborative approach to data promotes that sense of shared responsibility by helping teachers see their instruction as part of a larger effort to serve students more effectively.” American University offers a Master of Arts in Teaching that explores innovative approaches to using data that can help graduates provide an equitable education to all students.

Tips on How to Use Data in Education

Data can be an immensely helpful tool for educators and administrators if they keep the following tips in mind.

Use data to find out what happened in the past

Educators should look to past data to get a baseline understanding of students. Past data indicates what skills students have learned and in what areas breakdowns occurred. This information serves as an important road map for teachers as they plan what skills to teach next and in what areas students might need extra help. In many districts, standardized tests will show whether a student is advanced, proficient, basic, or below basic. With this knowledge, teachers gain insights about why one class might not be advancing as fast as another at the same grade level. This data also helps teachers set up accommodations for students. For example, teachers may move students at the below basic level to the front of the classroom so they have easy access to extra support. Or, they may provide advanced students with alternative activities that offer them a greater challenge.

Use a mix of quantitative and qualitative data

Educators should use a mix of data types to evaluate student performance. Using data obtained from an end-of-unit exam alone misses many opportunities to get useful information about students’ strengths, weaknesses, and preferences. Simple formative assessments such as thumbs up/thumbs down check-ins can help teachers get a quick reading on student comprehension and can provide information about student engagement. Observing students’ interpersonal and social successes can give teachers insights into which activities students enjoy and with whom they tend to work best — valuable information when grouping students for collaborative work or lesson planning.

Understand what data can and can’t tell you

Teachers must assess what data can be used for. Certain data can help answer one question but not another. For example, a teacher may examine data that reveals a particular group of students come from disadvantaged backgrounds. That may help explain why those students struggle academically in general; however, it cannot explain why a student did poorly on an exam. Understanding the ways in which data can and can’t be used allows teachers to diagnose problems more accurately and then respond to them. For example, a teacher who simply attributes students’ poor performance on a test to information about their background may miss the actual reason for the results and therefore not be able to supply needed interventions.

Keep an eye out for unexpected trends

Many factors outside of a teacher’s control come into play that can influence student success. For example, students might be responsible for getting their younger siblings dressed and off to school before showing up to their own classes. Any troubles along the way could mean those students arrive to class late and miss a quiz. When teachers are aware of such issues, they can find ways to help students work around them. Rather than refusing to allow the student to make up the missed quiz, the teacher can make accommodations for the student. Sometimes, asking simple questions such as “Why do you arrive late every day?” can provide the information teachers need to make personalized adjustments.

Moreover, observant teachers keep their eyes open for patterns in student behavior and performance. For example, an English teacher evaluating test scores from the past few months may unexpectedly find data that shows students perform better if the test is on a Monday rather than a Friday. After polling students, the teacher may learn the students always take math tests on Fridays, which means they had to study for two tests the night before, cutting down on their time to prepare well for either one. With this data in hand, the teacher can coordinate exam days with colleagues.

Use a variety of data tools

Data-driven decision making in education has never been easier, with the advent of new technology. A variety of data tools, many of them free, can now reveal hidden patterns and insights or simply help teachers organize data and keep it accessible for analysis. Traditionally, the grade book has served as a teacher’s record-keeping tool. Today’s electronic grade books, however, offer bells and whistles such as individual and class statistics, the ability to attach standards to assignments—making it easier to assess a student’s level in core areas—and analysis of student accomplishments. Apps such as Geddit and Plicker provide formative assessments to teachers and give them instant feedback on student learning. Geddit allows students to “check in” throughout a class, and it then provides the teacher with immediate information about individual students. Plicket lets teachers instantly deliver and grade low-stakes assessments like exit tickets (an answered question or completed exercise students must hand in before leaving class) and mini quizzes, which saves time and provides useful data for planning differentiated instruction. Microsoft Excel, Google Forms, and Google Docs are also helpful for organizing and analyzing information about class performance year after year.

Devise new lessons based on data

All types of data can help guide teachers in their lesson planning. Teachers must consider skill deficiencies; how many students are proficient, basic, or below basic; and what their specific students enjoy. For example, teachers may query their students at the beginning of the school year and throughout the semester about their favorite subjects and activities. Based on the responses, teachers can develop lessons that focus on the subjects students like best and incorporate their preferred learning activities into the curriculum.

Discover how to use data to improve student performance

Analysis of the right data allows teachers to identify contributors to student success and failure. Once those contributors are located, teachers can devise solutions to address them. For example, by analyzing a struggling student’s homework grades and test scores, a teacher may gather important insights about where a deficiency in understanding exists or, perhaps, which question formats pose challenges. Once such discoveries are made, a teacher can design exercises and activities for students in their trouble areas that help improve their performance.

Turn to multiple sources of data

To make well-informed decisions about instruction, teachers should pull from several sources of data, and according to Dr. Steele, “Teachers need time to engage regularly in conversations with colleagues about a wide range of data sources.” Some of those sources might include student writing samples, group projects, homework and test grades, as well as student surveys and reflections written by students about their own learning. With this data, teachers can make effective choices about skills to incorporate into a unit plan, texts and materials to use, and activities to include.

For example, a science teacher devising a new curriculum may look over multiple homework reports from the previous unit and learn that students can identify cell parts but still need work understanding each part’s functions. So the teacher includes lessons covering cell function. While reading over students end-of-unit reflections, the teacher notices that students reported finding visual models particularly helpful, so the teacher ensures the new unit incorporates more visual models to explain concepts. Finally, the teacher reflects on and analyzes which learning activities yielded the best results and then includes the most effective ones and pulls back on those that did not work as well. Drawing information from different sources offers teachers a variety of insights that can help them craft the best coursework for their students.

Understand when data may not be suitable

Data has limits. Teachers need to keep in mind that not all data analysis can be applied in the same way. Even when situations seem similar, factors that are not obvious at first may result in different outcomes. For example, a ninth grade English teacher may look over data showing how the introduction of a new peer-editing method improved student essays in a colleague’s 10th grade English class. The ninth grade teacher may assume that by implementing the same peer-editing method, ninth grade student essays will also improve. However, that assumption may be short-sighted. For instance, the peer-editing method may rely on analytical skills the ninth-graders have not mastered. The 10th grade class might have several advanced students capable of leading the peer-editing activity that the ninth grade class lacks. Any number of factors can impact student learning, which means teachers must be mindful of how they interpret data and consider the many things that can affect results, including class structure, class size, and student ages and backgrounds.

Prepare visualizations based on data

Teachers often need to share data. This can help a teaching community identify and understand trends within a school. To share data in the most accessible way, teachers can create visual models. For example, a teacher may select different data points relevant to colleagues teaching the same grade level or the same subject. Using pie charts, graphs, or PowerPoint presentations, teachers can show how students performed in different subject areas and help department teams gain insights into the strengths and weaknesses of incoming students.

Use data to plan for the future

Data is key when a teacher prepares to navigate the twists and turns inevitable in education. For example, a teacher may evaluate past data about classroom budgets and notice that the budget has decreased by 5 percent every year. Reasoning that the budget will likely decrease by 5 percent again for the upcoming year, the teacher can choose what supplies to purchase for the school year with that budget decrease in mind. The teacher may also choose to apply for grants from organizations such as Donors Choose, which provides funding for teacher projects their school budgets cannot cover.

The Future of Data-Driven Decision Making in Education

Both teachers and students stand to benefit from the continuing evolution of data and analytics. Teachers can work toward incorporating data-driven instruction into their work and make use of the many new tools designed to help them use data to work efficiently and improve student performance. Learning how to harness the power of data and cultivating a deep understanding of the complex social justice issues many students face will prove vital in helping educators develop solutions that level the playing field for disenfranchised students.

Using cutting-edge research, American University’s award-winning faculty, including renowned experts like Dr. Steele, trains students in its Master of Arts in Teaching program to tackle the greatest challenges in education. Discover how the right education can prepare teachers to become agents of change.

DonorsChoose, About Us

Economic Policy Institute, “Reducing and Averting Achievement Gaps”

Educational Leadership, “The Collaborative Approach”

EdSurge, “Not Just Numbers: How Educators Are Using Data in the Classroom”

Educational Leadership, “Why Teachers Must Be Data Experts”

Edutopia, “3 Ways Student Data Can Inform Your Teaching”

ESchool News, “6 Steps for Using Data to Improve Instruction”

Forbes, “Data-Driven Decision Making: 10 Simple Steps for Any Business”

My eLearning World, “Top 10 Online Gradebooks to Make Teachers’ Life Easier”

ScienceSoft, “4 Types of Data Analytics to Improve Decision-Making”

SEDL, Using Data to Guide Instruction and Improve Student Learning

TeachThought, 5 Innovative Tools for Data-Based Teaching

U.S. Department of Education, “Research Review: Data-Driven Decision Making in Education Agencies”

ZDNet, “Business Analytics: The Essentials of Data-Driven Decision-Making”

Data-Driven Decision Making in Education: 11 Tips | American University (2024)


What is an example of data-driven decision making in education? ›

An example of data-driven decision making in higher education could involve analyzing enrollment trends for a particular department. If you find that enrollments have been on a steady decline for sociology courses, a data-driven decision would be to re-evaluate the curriculum and merge or eliminate certain classes.

What are the best data to collect and use in data-driven decision making in schools? ›

Academic Achievement Data
  • benchmark assessments.
  • diagnostic assessments.
  • formative and summative assessments.
  • common grade-level assessments.
  • students' class averages.
  • progress monitoring data.
  • student work samples.
  • portfolios.
Feb 17, 2020

What is data-driven decision making in special education? ›

What is data-based decision making in education? Data-based or data-driven decision making is a system of procedures that teachers use to identify why a student is struggling. Many years ago, we tended to assume that when a student had difficulty in school, it was always because of a disability.

What is an example of data-driven decision making? ›

An example of data-driven decision-making is using digital intelligence tools to look at existing demand in a market for a specific product or service before deciding to enter it. Another example of DDDM is using competitive intelligence to look at specific keywords to target in a PPC campaign before investing.

What does it mean to be data-driven in education? ›

More specifically, when a teacher uses data-driven instruction (or DDI), that teacher regularly gathers and analyzes data from both formative and summative assessments to glean insights into how well their students are understanding and mastering the material.

What are examples of data in education? ›

Data includes disciplinary records, report cards, and behavioral assessments. Teachers record student behavior. National assessments, state high-stakes tests, district level assessments, SAT and ACT scores, etc.

What are the 5 levels of use in data-driven decision making? ›

How to Become More Data-Driven in 5 Steps
  • Step 1: Strategy. Data-driven decision making starts with the all-important strategy. ...
  • Step 2: Identify key areas. ...
  • Step 3: Data targeting. ...
  • Step 4: Collecting and analyzing data. ...
  • Step 5: Turning insights into action.

What are the 3 most important sources of data for effective decision making? ›

Sources of Data
  • Observation Method.
  • Survey Method.
  • Experimental Method.

Why are data driven decisions important in education? ›

Use data to find out what happened in the past

Past data indicates what skills students have learned and in what areas breakdowns occurred. This information serves as an important road map for teachers as they plan what skills to teach next and in what areas students might need extra help.

What are the benefits of data driven decision-making? ›

5 Benefits of Data-Driven Decision Making
  • Increased accountability.
  • Better efficiency.
  • Alignment on company-wide goals.
  • A sense of ownership at every level.
  • Transparency.

What is the purpose of data driven decision-making? ›

Data driven decision making (DDDM) is the process of using data to make informed and verified decisions to drive business growth. By using the right KPIs and tools, companies can overcome biases and make the best managerial rulings that are aligned with their strategies.

What are data-driven decision-making issues? ›

Issues such as duplications, inaccuracies, lack of consistency, and a lack of completeness can all affect the quality of your data. Data that's not up-to-date or that's obsolete can cause serious problems as well. Inaccurate or irrelevant data will often directly lead to poor decision making.

What are the types of data used in decision-making? ›

The four types of data analysis are:
  • Descriptive Analysis.
  • Diagnostic Analysis.
  • Predictive Analysis.
  • Prescriptive Analysis.
May 20, 2022

What is data-driven program example? ›

Data-driven programming is a programming model characterized by program statements that describe the data instead of a sequence of actions. For example, an email filtering system may be programmed to block emails from malicious email addresses.

What are the four attributes of a data driven school? ›

Overall, the four components needed to build a data-driven education system are personalization, evidence-based learning, school efficiency, and continuous innovation.

How does a teacher use data to drive instruction? ›

How Teachers Use Student Data to Improve Instruction
  1. Standardized tests gauge overall learning and identify knowledge gaps. ...
  2. Individual assessments reveal each student's needs. ...
  3. Summative assessments catch learning roadblocks. ...
  4. Summative assessment also informs curriculum and instruction.

Why is data important in a university? ›

Data can help predict patterns and trends that could become problems if not addressed immediately. Universities can make proactive decisions before major issues arise and avoid costly mistakes.

What is an example of a data driven question? ›

Interview questions to assess being data driven

How did data help guide certain decisions you made in your previous role? How do you navigate decision-making in the absence of quantitative data? Tell me about a time when you had a measurable impact on a job or organization.

What tools are used for data driven decision-making? ›

Data analysis tools

To make data-driven decisions, you need to collect, process, and analyze your data. There are many tools available to help with this, such as Excel, Google Analytics, SQL, Python, and R.

What is another name for data driven decision-making? ›

Data-driven decision making is also known as data-driven decision management or data-directed decision making.

What are the 4 key models of decision-making? ›

The four different decision-making models—rational, bounded rationality, intuitive, and creative—vary in terms of how experienced or motivated a decision maker is to make a choice. Choosing the right approach will make you more effective at work and improve your ability to carry out all the P-O-L-C functions.

What is data driven in Six Sigma? ›

Six Sigma uses a data-driven management process used for optimizing and improving business processes. The underlying framework is a strong customer focus and robust use of data and statistics to conclude.

What are the 3 C's of decision making? ›

Clarify= Clearly identify the decision to be made or the problem to be solved. Consider=Think about the possible choices and what would happen for each choice. Think about the positive and negative consequences for each choice. Choose=Choose the best choice!

What are the 6 sources of data? ›

The most commonly used methods are: published literature sources, surveys (email and mail), interviews (telephone, face-to-face or focus group), observations, documents and records, and experiments.

What are the 5 sources of data? ›

Methods of Collecting Primary Data
  • Direct personal investigation.
  • Indirect oral investigation.
  • Information through correspondents.
  • Telephonic interview.
  • Mailed questionnaire.
  • The questionnaire filled by enumerators.
Jan 23, 2021

What is the difference between data driven and data informed education? ›

This is the fundamental difference between being data-driven and being data-informed: Data-driven: You let the data guide your decision-making process. Data-informed: You let data act as a check on your intuition.

What is the conclusion of data driven decision-making? ›

Conclusion. In today's world, data-driven decision making is essential for success. By using the right data in the right way, you can identify trends and patterns that can help you make informed decisions.

Why is data driven mindset important? ›

Having a data-driven mindset encourages crucial components of innovation like collaboration, thinking, and failing. Moreover, it aims to break down silos within an organisation, allowing different departments to understand each other's challenges and opportunities.

What is another word for data driven? ›

The way we should be data driven already has an alternative term – data-informed. Obviously the big difference is that last word – informed.

What are the disadvantages of data driven decision-making in education? ›

Cons of Data Driven Instruction
  • Requires an investment in training. Without sufficient training, teachers may find the program to be frustrating and ineffective. ...
  • Requires an investment in time spent using it. ...
  • Technology is neither infallible nor a silver bullet.
Jan 31, 2019

What are the three key challenges in using data for decision-making? ›

Above, in the first of three videos, Tim McGuire sets out the triple challenge that companies face: deciding which data to use (and where outside your organization to look), handling analytics (and securing the right capabilities to do so), and using the insights you've gained to transform your operations.

How can data driven decision-making be improved? ›

Here are five practical ways to improve your data-driven decision making!
  1. Make data more accessible. In the fast-paced world of data and technology, flexibility and agility are more important than ever. ...
  2. Make data more appealing. ...
  3. Make data more available. ...
  4. Make data more applicable. ...
  5. Make data more agile.

What are the 2 categories of data analysis for data driven decision making? ›

Data-driven decision making is tied most closely to predictive and prescriptive analytics, even though these are the most advanced.

Which type of data is easiest to analyze and use to make decisions? ›

Descriptive Analytics

Descriptive analytics is the simplest type of analytics and the foundation the other types are built on. It allows you to pull trends from raw data and succinctly describe what happened or is currently happening.

What are the four 4 types of analysis? ›

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

What are data-driven strategies? ›

When a company employs a “data-driven” approach, it means it makes strategic decisions based on data analysis and interpretation. A data-driven approach enables companies to examine and organise their data with the goal of better serving their customers and consumers.

What are data-driven skills? ›

Critical thinking : Data-driven decision making involves the ability to think critically about data and discern the complexity of data and weigh in on the pros and cons of the decisions made.

What are data-driven approach methods? ›

In a data-driven approach, decisions are made based on data instead of intuition. Following a data-driven approach offers measurable advantages. That's because a data-driven strategy uses facts and hard information rather than gut instinct. Using a data-driven approach makes it easier to be objective about decisions.

What is data based decision making in school psychology? ›

Data-based decision-making can be defined as teachers' systematic analysis of data sources in order to study and adapt their educational practices for the purpose of maximizing learning results. Teachers must apply the findings from their data use to their personal teaching activities.

What are some key factors when making data-driven decisions? ›

5 steps for making data-driven decisions
  • Know your vision. Before you can make informed decisions, you need to understand your company's vision for the future. ...
  • Find data sources. Once you've identified the goal you're working towards, you can start collecting data. ...
  • Organize your data. ...
  • Perform data analysis. ...
  • Draw conclusions.
Oct 3, 2022

How do you use data to improve student learning? ›

How Teachers Use Student Data to Improve Instruction
  1. Standardized tests gauge overall learning and identify knowledge gaps. ...
  2. Individual assessments reveal each student's needs. ...
  3. Summative assessments catch learning roadblocks. ...
  4. Summative assessment also informs curriculum and instruction.

What are the benefits of data-driven decision making? ›

5 Benefits of Data-Driven Decision Making
  • Increased accountability.
  • Better efficiency.
  • Alignment on company-wide goals.
  • A sense of ownership at every level.
  • Transparency.

What is the purpose of data based decision making? ›

Data based decision making provides businesses with the capabilities to generate real time insights and predictions to optimize their performance. Through this, they can test the success of different strategies and make informed business decisions for sustainable growth.

What is data driven decision making principle? ›

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level.

What are the types of data used in decision making? ›

The four types of data analysis are:
  • Descriptive Analysis.
  • Diagnostic Analysis.
  • Predictive Analysis.
  • Prescriptive Analysis.
May 20, 2022

What are 5 examples of decision-making? ›

Examples Of Decision-Making In Different Scenarios
  • Deciding what to wear.
  • Deciding what to eat for lunch.
  • Choosing which book to read.
  • Deciding what task to do next.
Sep 11, 2020

What are the 5 basic decision-making? ›

5 Steps to Good Decision Making
  • Step 1: Identify Your Goal. One of the most effective decision making strategies is to keep an eye on your goal. ...
  • Step 2: Gather Information for Weighing Your Options. ...
  • Step 3: Consider the Consequences. ...
  • Step 4: Make Your Decision. ...
  • Step 5: Evaluate Your Decision.


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