Using Data to Drive Instruction: The Power of Data in the Modern Classroom

In a data-driven world, it is increasingly evident that data analytics will influence every aspect of our lives. The implications are especially significant in education, where advanced technologies are now integrated in new and exciting ways. This plays out in efforts to improve technological literacy from the perspective of both students and educators. 

An important piece of the puzzle? Data's influence on instructional design, learning assessments, and differentiated instruction. This is an emerging area of evidence-based learning — and it promises to transform how we support and respond to students. Below, we illustrate the value of data-driven instruction in education, along with key opportunities for developing evidence-backed solutions to optimize the learning process.  

What Is Data-Driven Instruction?

Data-driven instruction brings the advantages of the information age to the world of education. This approach involves the strategic collection of relevant student information, which can be used to assess everything from engagement to learning outcomes. 

While standardized testing comes to mind as the epitome of data-driven instruction, there's far more to this concept than the occasional testing initiative. To be considered truly data-driven, actionable data must be built into every aspect of the learning process, not simply used to occasionally monitor progress.  

Teacher and Administration Involvement

The primary benefits of data-driven instruction involve this strategy's unique ability to inform teachers and administrators, inspiring them to integrate actionable insights into future lessons or initiatives.

Through the extensive collection and dissemination of relevant data, education professionals can gain details about students and everyday practices that might otherwise feel out of reach. These findings can then inform nearly every aspect of modern education, such as: 

• Classroom design or layout 
• Strategies for differentiated instruction 
• Identifying at-risk learners and responding to their unique needs 
• Deciding whether to assign homework (or how much) 
• Determining when to re-teach topics or move on to new subject matter

Because data-driven instruction provides so many valuable takeaways for teachers, it is important to get them involved in the process from the ground up. This begins with extensive training, so teachers feel more comfortable implementing and utilizing data-based systems and strategies. Similarly, administrative professionals need to understand how (and why) this data is collected, as well as how it may play into trends regarding attendance or behavior.

Why Is Data-Driven Instruction Important in Education?

Technically speaking, instruction has always been driven by data. From the beginning, the learning process has been shaped by interactions between educators and students, with the former using a variety of strategies to determine how or whether their approach resonates. Data-driven instruction simply takes this reality to the next level, harnessing the power of advanced technology and new educational frameworks to optimize these efforts. 

The goal? To ensure that teachers and administrators are as informed and empowered as possible. Without these insights, educators could fail to take critical factors into account as they plan their classrooms and lessons. Simply put, proceeding without data is akin to teaching with blinders on. If we have the capacity to gather and synthesize such relevant information, why not make the most of it? 

One of the greatest promises of modern data-driven instruction: the ability to do more with less. With strategic implementation, data-driven systems remove some of the burden of assessment from busy educators. From there, they can use streamlined and highly efficient systems of data collection and analysis to get extensive and accurate insight into how individual students are performing — and how entire classrooms or schools are functioning.  

Online Learning and Big Data

Much of the recent move towards data-driven instruction relates to the proliferation of data-driven strategies in online learning. There, data collection and analysis supplement digital interactions, allowing educators to understand the needs and preferences of their students, even if they are unable to meet in person. 

Known as learning analytics, this concept involves the tech-based analysis of data relating to the learning process. These systems identify and measure key performance indicators as they relate to students and instructors. 

With online learning, these indicators could include login data, assignment submission, or even how often students view feedback from teachers or other students. Test scores are also integrated, but they form a smaller part of the big picture that also draws heavily on indicators of student engagement. 

Now, data-driven instruction aims to bring the most successful analytics initiatives from the world of online learning to classroom instruction. This, in turn, enables teachers to better understand how students’ function within the classroom environment, how engaged they are with the material, and where (or when) they are falling behind.  

How to Use Data to Drive Instruction

Emerging concepts and processes in data-driven instruction hold great promise, but they can feel intimidating to educators who are already struggling to grasp and implement a myriad of other modern technologies. Data should take precedence, as educators are already exposed to it — and rely on it — more than they realize. These best practices should help:  

Collecting Student Data

Any data-driven initiative should begin with collecting the volume of data needed to discern patterns or drive educational strategies. Collecting data is far from new; traditionally, this has involved tests and other formal assessments, which provide insight into student learning and opportunities for improvement. Teachers have also traditionally looked to attendance data and indicators of parent involvement for further insight. 

What is shifting, now, is not whether data is collected or which types, but rather, the techniques used to obtain and analyze larger volumes of information. There is a clear need not only to gather extensive data but find the right data and use it in the most relevant and impactful ways possible. Without a strategic approach, the return on investment can be minimal, as teachers may waste valuable time and effort trying to make sense of data that is of little use for their immediate classroom concerns. 

The key may lie in focusing more on engagement than on occasional test scores. Standardized testing has been a core part of the education system for some time now. While this should still play a role, teachers also need to know whether students connect to the material and are motivated to keep learning. Engagement is obviously easier to measure in an online context, but it is possible to track how students participate in the conventional classroom and whether they are invested in the material. 

Meanwhile, teachers also need to be aware that confirmation bias remains a real risk when using data-driven processes. Hence, the need for regular reflection and the willingness to challenge preconceived notions, especially when analyzing student data.  

SMART Goals and Goal Setting

Students feel far more invested when setting goals that they find personally compelling. Often, however, even the most well-meaning students struggle to follow through. Data-driven strategies provide a much-needed boost, revealing when students are most likely to fall behind on their goals — or when they need an extra boost. Data can also shape the very goals that these students set, providing valuable insight into the types of objectives that are within their grasp. 

SMART goals are at the heart of data-driven goal setting. This helpful acronym has long been a goal-setting must-have, but it gets a further boost when data informs goal selection or helps students follow through. The ideal SMART goal will accomplish these essentials: 

• Specific 
• Measurable 
• Achievable 
• Relevant 
• Time-Sensitive 

Teachers can use data-driven strategies to track students' progress and keep them abreast of it throughout the course of each term. Students are similarly encouraged to examine progress indicators, as these can be deeply motivating — particularly when tackling a difficult subject matter, some students may otherwise not feel as if they are making progress.  

Monitoring Progress and Data

As we've mentioned, standardized testing has formed one of the earlier iterations of data-driven learning and still has a role to play. On a larger scale, standardized tests can provide valuable insight into major educational trends and developments. At the classroom level, however, monitoring progress should take place on a more frequent, but less formal basis. 

Simple formative assessments such as low-stakes quizzes can reveal how much students have gleaned from specific lessons and where further progress is needed. Through these results, instructors can determine which concepts entire classrooms are struggling to grasp — or even reveal when or where potential learning disabilities or executive function concerns come into play.  

Developing a Learning Environment

Student data can reveal a lot about the status of any given learning environment and can also be used to shape that environment. Often, data plays heavily into differentiated instruction, with frequent assessments revealing how students are grouped based on content proficiency and other factors. 

In addition to influencing differentiation, data can drive more effective scaffolding, ensuring that students consistently receive the full support they require. As at-risk students are identified and better understood, teachers and administrators can provide better access to resources or assistance while also keeping parents in the loop. 

Data-driven practices may even influence how classrooms are arranged or designed. For example, insights on student engagement may reveal better participation with a particular type of classroom layout. Similar insights can be gleaned based on how classroom activities are scheduled and structured.  

Assess Impact and Optimize Learning

Teachers have long understood the necessity to adjust their approach based on the unique needs of their students, but data-driven strategies make these needs easier to discern, even as they change over time. By continuously collecting and analyzing data, educators can determine whether their efforts are having the desired impact. As we've discussed, teachers must be aware of the role that confirmation bias can play if not addressed head-on — but data literacy training can go a long way toward combatting this. 

Presented with detailed insights on student progress, teachers should be willing to make targeted adjustments designed to optimize learning both at the individual and classroom levels. These changes could include tweaked lesson plans, adjustments to classroom layout, or greater support for students who have, through data-driven practices, been identified as at-risk learners. 

As these changes are made, teachers should continue to track progress to determine whether they are having the desired impact — or whether further adjustments are needed. Ongoing training will be needed to facilitate and support these adjustments, particularly as new data-collection techniques and evidence-based practices become available.  

Improve Communication with Other Educators

Many educators are most inspired by their relationships with one another, and yet, they often struggle to collaborate in meaningful ways beyond planning the occasional all-school activity. When software solutions facilitate collaborative data-sharing, however, teachers can get a better sense for how their efforts play out on a wider scale. This is important for optimizing instruction centered around students with unique challenges. 

This purpose-driven form of collaboration is known as the professional learning community (PLC). Emphasizing collective inquiry, PLCs allow educators to enhance their practice by drawing on the unique ideas of their fellow instructors — but within a highly purpose-driven context. 

Data-driven strategies are also effective from an administrative standpoint, as insights gathered and analyzed by administrators can be incredibly helpful for teachers — and vice versa. For instance, class-by-class attendance data may reveal when specific courses are not sufficiently engaging or when other cognitive or behavioral concerns keep students performing well in some courses but not others.  

Advance Your Knowledge of Education with Lindenwood Online!

Data-driven instruction provides a wealth of exciting opportunities, but this must be backed by evidence-based solutions that are easy for teachers to navigate and master. This is a huge area of promise within the field of instructional design, which aims to optimize learning through the systematic development of educational materials and systems. 

The instructional design coursework at Lindenwood University Online provides a thorough overview of today's top strategies for using data to drive instruction. We offer multiple graduate degree programs, which equip you with the technological skills and analytical mindset needed to optimize data-driven educational methods. Reach out today to learn more about our Master’s in Instructional Technology and our Master’s in Instructional Design.