Data-driven decision-making has been proven to improve student learning. It requires teachers to study their students’ strengths, needs, and preferences to inform instruction. This way, instruction is tailored to the students rather than forcing students to fit into the molds of a restrictive curriculum.
Because it is highly specific to a given group of students, data-driven instruction encourages teacher creativity in lesson planning and curriculum development, increases student engagement, and refines expected outcomes.
What is data-driven instruction?
Data-driven instruction involves carefully gathering consistent yet diverse points of information about each student to use the data to actively improve learning quality. Teachers can track data on student academic performance, behavior, and learning preferences.
This data is used to make informed decisions about the lesson and assessment design. These decisions address student needs and optimize student learning. For instructional strategies to be considered “data-driven,” they must be justified by data points, trends, and outcomes that have been tracked over time.
Going deeper than the “what”
What kind of data should educators be tracking? Many schools use data-driven learning to create a record of what a student knows. This type of data typically presents itself in numbers through summative assessments such as classroom assessments, standardized test scores, and grade point averages.
While this quantitative data is important, this data only scratches the surface. Student outcome data only lets teachers know where students are in their knowledge and skills at a specific point in time.
Addressing the “hows” and “whys”
True data-driven instruction is not just a numbers game. Collecting qualitative data is necessary for data-driven instruction to work. To change the outcome of future student performance data, teachers also need to track student “hows” and “whys.”
Observation data can answer “how” and “why” students have reached a certain outcome. This includes trends in student engagement, behavior, interests, strengths, and struggles. Observation data may also include formative assessments that are used as checkpoints, such as small quizzes, discussions, and projects.
The best practice for data-driven instruction is to have a healthy balance of quantitative and qualitative data. Here are a few ways data-driven instruction can be used in the classroom:
1. Initiating data-driven instruction
To get started with data-driven instruction, it is a good idea for teachers to first determine what they want the data to help them improve upon. Whether it is to find student gaps in knowledge, increase student engagement, or measure the effectiveness of instructional strategies, each goal will require different forms of data. Then, teachers can analyze the data to support their classroom instruction.
2. Establish a data collection system
Data-driven instruction starts with establishing an effective data collection system. There are countless data collection methods, templates, and tools for teachers to choose from. Educators should experiment with different options so they can find the best system that works for them.
Gathering student data during classroom instruction can be difficult, but some instructional practices can inherently track student progress.
Use EdTech that displays learning progress for students
The modern world of EdTech has revolutionized how teachers engage in data-driven instruction by intertwining student learning with gathering data.
For example, there are applications like Peardeck that allow teachers to monitor student engagement, timeliness, and accuracy with the material in real time and will export all student progress onto a neat spreadsheet upon completion.
This information can be used to determine the pace and complexity of future lessons.
Other applications such as CommonLit and NewsELA store and track student reading comprehension data and adjust content and skill levels to match student needs. All information from these sites can be exported into progress monitoring sheets.
Create checklists for quick student data
Checklists are highly effective in tracking student performance because both students and teachers can use them for quick and efficient information logging. For example, teachers can track student productivity by checking off each time a student is prepared for class with the necessary materials. If the student does not have their materials three out of five times, this will inform the teacher to plan organizational strategies for that particular student.
Let students do the tracking
Especially when working with an older grade level, teachers can use student-produced reflections, conferencing, and checklists as data to drive instruction. This way, students can take ownership of their learning by conducting personal data collection, analyzing their needs and strengths, and advocating for themselves and their needs as learners.
Take observational notes
Although this can be the most tedious method, it is usually the most thorough. Observational notes are great for teachers who are looking to address student behavior, engagement, and productivity. Oftentimes, special education teachers rely on observational data to narrate a student’s social, emotional, and behavioral needs.
There is a wealth of observational note templates online that can guide teachers’ note-taking systems toward the area they are looking to improve.
3. Build a schedule for data analysis
Although some school leadership may establish a standard practice, it is usually up to the teacher to determine a personal data analysis system: including collection methods, materials, conclusion reports, and reflections.
Teachers have minimal time away from students to handle all of the additional, non-instructional responsibilities of their jobs. With this in mind, teachers should build a schedule for data analysis that best works for them.
To foster continuous improvement on a manageable schedule, some teachers prefer to track data on two-week cycles, leaving one day at the end of each cycle for analysis.
Once the analysis is complete, instruction is inherently driven by the data, as these analytical conclusions are kept in mind for future lesson and assessment planning.
4. Analyze student data
To save time, teachers can peruse online resources to find data collection, analysis, and reflection templates for reuse each data cycle. When it comes time to analyze student data, it is important to find a general set of questions or charts that lead data to conclusions to solutions.
Establish reliable baseline data
The first step is to compare the data collected to the baseline data. Baseline data is a set of quantitative data that is taken before any interventions or modifications to instruction. This shows the teacher where students are academically, behaviorally, socially, or emotionally before changes are made to instructional methods.
Baseline data allows teachers to accurately measure student achievement—whether to show growth or a decline. The baseline is the original measurement.
Contextualize data
The purpose of analyzing data is to discover learning and behavior trends among specific students or groups of students. To interpret data, it is important to contextualize data points. Having multiple forms of collected data will help teachers piece together a story about each student, lesson, or assessment.
For example, a teacher may use formative and summative assessments to track how many students are meeting formal learning expectations and how many need extra support.
Adding qualitative data through anecdotal observations will help the teacher understand how and why these gaps came to be.
Possible observation notes that a teacher may use to support a learning gap include struggles with attention, preparedness, productivity, engagement, or participation.
Example anecdotes may include trends in not bringing materials to class, ineffective time management, struggle with group work, etcetera.
The more detailed the notes, the more a teacher can draw from them to determine the source of student growth or struggle.
5. Use data to drive and improve instruction
Data can help determine how students progress in learning. Teachers analyze data to find specific information that informs their instruction and helps students achieve mastery of content and skills.
After contextualizing data points and reviewing data trends, teachers will make instructional decisions to address the results of the data and the needs of individual students.
Data reflection
Teachers reflect on data holistically. This means that all factors playing into data results and context are considered for the best understanding. Some reflective questions teachers may ask themselves as they reflect on data include: does the student have a knowledge gap from prior learning experiences that did not reach their needs? Is the student struggling with attentiveness due to external, home, or social-emotional factors? Would the student have performed better if I changed or offered X about this assessment?
The answers to reflective questions breed solutions.
Data-driven decisions
Upon reflection, educators can improve teaching by implementing their findings. In the classroom, lesson and assessment designs can be continuously adapted for optimal student learning.
Lesson planning
Data can inform lessons and activities consistently. Teachers can track student behavior and understanding as a result of specific instructional strategies. These can be adapted day-by-day and from school year to school year.
For example, if the data shows that students are struggling with note-taking lesson activities but are understanding the reading, the teacher may deduce that students are unfamiliar with effective note-organization skills. The teacher can then adapt their lesson to incorporate a note-taking process review before the activity.
Assessment design
Assessments that are data-informed will allow students to best demonstrate their knowledge and skills. If the data shows that students are performing well on interactive lessons but then proceed to do poorly on a multiple choice assessment, the teacher can reflect and adapt the assessment to mirror how students are used to engaging with the material for better results—or vice versa.
Why is data-driven instruction important?
Data-driven instruction will improve student performance because it is student-centered. Typically, instruction driven by data is customized to student needs rather than adhering to set scopes or sequences of instruction.
Using data in the classroom supports both students and teachers. The use of student-specific data inherently provides teachers with more autonomy in the classroom, as they know their students best. Teachers who incorporate data-driven instruction are free to create lessons and curricula that they feel are most efficient and exciting to them as teachers and to their learners.
Student data also gives teachers, administrators, and families an objective source of knowledge on student performance. Consistent data tracks both student learning trends and teacher problem-solving efforts.
Parents and administrators rarely question why student achievement is high, so it is important to keep this data as a reference for comparison if a student is struggling.
This is why teachers need to track data no matter how well or how poorly students are performing.
Having logged student data is also extremely helpful in identifying students who may need special education services. To find a student eligible for these services, it must be proven that a student needs more than general instructional strategies to be successful in the classroom. Teacher-collected data can be presented as evidence to support a student’s case.
Conclusion
To enhance student academic achievement, educators must first determine a goal. Educators should pinpoint an area of improvement for a specific student, group of students, lesson, instructional method, or assessment to drive their data process.
However, data-driven instruction is not one-size-fits-all. Establishing data collection methods, data analysis schedules, and implementation methods are unique to teacher preferences and student populations.
Analyzing data should begin with comparing collected data to a reliable baseline.
This is where teachers can see a positive or negative change in what a student knows or can do. From there, applying context to data allows teachers to understand the “how” and the “why” behind the new data points.
Lastly, teachers will use their newly identified trends and conclusions to drive instructional solutions.
Lesson and assessment design decisions will directly respond to the analyzed data to tailor instruction to student needs.
However, while many of the data tasks are done by educators, school leaders are responsible for creating an environment for data-driven instruction. In order to establish a data-driven culture, teachers can contact school leaders to encourage professional learning communities or professional development opportunities. With proper supervision and curriculum development, school leadership can transform education to reflect student data.
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