The No Child Left Behind Act of 2001 changed the way schools looked at data. New accountability mandates required that test scores be segmented by specific classes of students. Low-performing sub-groups could no longer be hidden in school-wide averages, hence the No Child Left Behind title.
With the 2015 reauthorization—the Every Student Succeeds Act—the federal government rolled back many NCLB provisions and allowed states to develop their own accountability standards. Rather than using data primarily to measure yearly progress, state education departments and individual districts began to focus on using data to drive instruction.
Data informs decision-making by identifying weaknesses and strengths in curriculum design, teaching methods, and student groupings. Data sets also provide evidence of program effectiveness or ineffectiveness, making it possible for all stakeholders to understand and get on board with changes. New technologies make it possible to easily collect and interpret large volumes of information. The questions become, “What data should we collect and how should we use it?”
Types of Data
Standardized Test Scores
Aggregated district and school-wide standardized test scores remain the primary measure of a school’s performance. States collect this data to inform accountability, allocate resources and inform the public with School Report Cards. For the school principal, school-wide data provides an overall perspective on student achievement, but its usefulness in driving instruction is limited.
To determine if specific content standards are being met, to understand the effectiveness of particular instructional strategies, and to identify students that require intervention, school leaders must analyze data at the grade and classroom-levels. And it goes without saying, individual student scores, along with observations and other qualitative measures, are critical data points for teachers as they create lesson plans, set the pace in their classrooms, and differentiate instruction to accommodate individual needs.
Solid social-emotional skills and positive behaviors contribute to a student’s ability to be academically successful. If students are to strive, they must feel capable and part of the larger community. Measuring these soft skills and creating SEL programs to address weaknesses must be part of an overall school improvement initiative.
Chronic absenteeism puts students behind academically and increases the risk they will drop out. Widespread absenteeism negatively impacts a school’s climate by fracturing the sense of community necessary for an optimal learning environment. A system that tracks attendance data and red flags rising issues will rein in problems early and send a message to the school community that daily attendance is expected.
Steps to Using Data-Driven Instruction
The National Association of Elementary School Principals breaks down the process of using data to support instruction into four phases:
When identifying which data to collect, think beyond test scores. Interim quizzes and drills along with classroom observations give educators a snapshot of where students are in their understanding of concepts. Long-term, multi-step projects offer insights into students’ skills in teamwork and time management.
Data is most informative, better able to illustrate the “big picture,” when compiled in one place. Disparate systems create information silos, which can be difficult to penetrate. While a student’s attendance may have a strong bearing on his grades, this may not be evident when systems are insulated from each other. Using multiple programs tends to frustrate data collection as administrators, educators and counselors must become skilled in each system and develop the ability to switch between programs. This produces separate reports that must then be manually combined to serve any useful purpose.
Data analysis is a collaborative endeavor with team configurations determining the analysis lens. For example, a team of teachers and teacher-leaders/coaches will look at data to gain awareness and understanding of instructional needs. They will identify the effectiveness and ineffectiveness of particular teaching methods and strategies. Collaboration between teachers and administrators seek to reveal the needs and challenges faced by educators and develop an understanding of the resources needed to address educator challenges.
Follow data analysis with goal setting. What are the desired outcomes going forward? Develop strategies to address weaknesses and build on strengths. Strategies may include adjusting the pacing of instruction, reviewing or reteaching particular concepts and skills, or regrouping wavering students for interventions in and outside of class. Define success.
What measurements will you use to determine if changes have been effective? You may determine your data is incomplete. The assessments and other measures aren’t providing the information you need and you will need to develop additional methods of tracking the school’s performance.
The final step in using data to drive instruction is to produce more data. While teachers generally will recognize whether a new strategy is working or falling flat, solid statistics on the district, school, and classroom levels serve as apples-to-apples comparisons to earlier data. Assessment results then cycle to become the first step in using data to drive instruction.
Using Data to Drive Instruction – 7 Best Practices
1. Start small – volumes of data can be overwhelming. Begin with a single classroom or learning unit and develop routines for data collection and analysis. Gradually scale-up using the methods you have found useful.
2. Use formative assessments – continually integrate data from quick quizzes and short assignments into the process to assess the effectiveness of new strategies. These interim measures may suggest adjustments are needed before a full collect-analyze-take action-assess cycle is completed.
3. Use data to group students – design interim assessments to identify each student’s level of content mastery to expedite differentiated instruction. One method is to create rubrics based on Blooms Taxonomy (remembering, understanding, applying, analyzing, evaluation, creating) to determine groupings. Another approach is to test for learning styles as defined in Gardner’s theory of multiple intelligences and group accordingly.
4. Determine support levels – the ability of educators to effectively scaffold instruction is essential to student-centered learning programs. Data drawn from formative assessments and teacher observations identify the level of support students need.
5. Engage students in the process – Introduce students to performance measures and ask them to develop goals and monitor their own progress. This exercise will provide teachers with insight into student needs, perceptions, and motivations.
6. Include parents in the conversation – keep parents involved in goal setting and regularly update them on their child’s progress. Parents will provide insight into their child’s behavior and provide support at home to help their child reach his or her goals.
7. Utilize and integrate technology –Information spread across separate programs makes for an unwieldy system. Advances in technology have streamlined the process of collecting, storing and analyzing data. Integrating IT systems will facilitate data-driven instruction initiatives by making SIS, administrative functions, attendance and behavior management systems available in one space. With advanced systems, administrators, educators, and counselors have the ability to share information for cohesive planning.
Research confirms that using data to drive instruction improves a schools performance, but the Age of Information has educators swimming in data. For it to be useful, districts need systems and routines to collect, sort and analyze information.
ScholarChip offers a solution called Alternative Behavior Educator (ABE). This innovative program enables counselors to identify, monitor, and improve student behavior throughout a student’s career while giving administrators and teachers powerful data-driven reports that quickly flag at-risk students, help monitor and chronicle progress, and support decision-making tasks.
The ScholarChip system incorporates the complete spectrum of behavior and integrates student rewards, interventions, and tracking with PowerSchool®, Infinite Campus, and other popular SIS platforms.
To learn how ScholarChip can help keep your schools safer and more secure learn more about the many solutions ScholarChip provides or to get free recommendations, feel free to schedule a 1-on1 with one of our specialists!