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Cultivating “Data Literacy” to Accelerate Math Achievement

A guide to transforming data into effective instructional strategies to meet the individual needs of all students

A school district can have a vision for data use and the technology infrastructure to support data-driven decision-making, but if the staff lacks “data literacy”—the knowledge, skills, and capacity to use data effectively—the whole enterprise will be working in an inefficient reactive mode. The most effective districts and schools know that it is essential to build the capacity of staff around a systemic process for using data and technology.

While teachers and administrators need not be experts in psychometrics, they should achieve some degree of data literacy. However, because most educators are not trained in testing and measurement, they are not proficient in assessment literacy. Data literacy and assessment literacy do differ. Data literacy, or pedagogical data literacy, involves transforming data into effective instructional strategies to meet the individual needs of all students.

Data use changes teacher practice, and data-driven decision-making leads to improvement in math achievement and student performance, which is one of the main reasons educators are trying to use data to inform their practices.

There is both a rich array of data available to teachers and leaders within their schools and a multitude of ways in which those data can be used for instructional guidance, institutional planning, organizational support, and cultural influence. Most of the data available to schools goes unexamined or used in ways it was never intended. Yet, data can provide a solid foundation from which to base well-considered courses of action and to test whether past decisions have paid off.

What Should Educators Know About Data?

One of the potentially powerful resources for informing instructional and school improvement—school-wide data—is grossly underutilized. The distinguishing characteristics of school-wide data are that they are:

  • Frequently and systematically collected across a grade level or content area about important student outcomes
  • Quickly aggregated and examined for patterns that can help inform next steps
  • Nimble numbers that provide meaningful guidance for finer-grained adjustments
  • Incorporate the formative qualities of assessment by providing opportunities for the examination of data patterns both within and across classrooms.
  • The results should be used for organizational decision-making and for refinement.

Educators must understand how to use data analysis protocols in terms of how to apply data skills in examining student-learning mathematics data, increase rigor in instruction, and apply blended on-demand professional development. Although having a data coach is essential, it is also important for all educators to gain skills and knowledge about data and how to use educational technology to personalize learning for the diverse needs of all students. Data and technology use should become part of every teacher’s regular repertoire of tools to teach for deeper mathematics understanding of concepts and skills.

It has been my experience in working with under-performing schools that they do not think consistently about the relationships between instructional practice and student outcomes. Teachers tend to examine data on a case-by-case basis rather than look for trends and patterns class-wise or grade-wise due to a lack of supporting expertise, professional development, and structured time to collaborate.

While it is important for educators to understand the principles of assessment and statistics, it is also important for educators to understand the technology that can support the use of data, assessment, and statistics. Teachers and administrators find problems in the interface with data systems, the quality and use of the data, or lack of time to collaborate, available resources, district-wide vision, and the expertise needed to make use of the data.

Research has identified five components of data use and examined teachers’ ability to use these skills. They identified:

  • Data location
  • Data comprehension
  • Data interpretation
  • Instructional decision-making
  •  Questions posing “developing hypotheses”

In studies, researchers found that teachers were able to locate data and knew how to differentiate instruction, but had difficulty with data comprehension skills, data interpretation, and posing questions. Teachers struggle to manipulate numbers in data displays and tend to focus on the extremes of student performance. They also had limited knowledge of validity, reliability, and measurement error—all concepts within assessment literacy.

Professional Development is Key

There is a pressing need for on-demand blended learning professional development courses to help educators build capacity to use data and educational technology to help educators interpret and translate data into effective instructional or administrative practices to achieve continuous school improvement.

If schools, districts, and states are going to rise to the challenge of helping all students meet rigorous State Learning Standards, there must be high-quality professional development for teachers and administrators in the using of multiple sources of assessment data to verify the causes of student learning problems and improve student performance outcomes. Not only must practitioners be trained to use data, they must also understand how to translate multiple measures of assessment data into actionable instructional practices and interventions.

Data use changes teacher practice, and data-driven decision-making leads to improvement in math achievement and student performance, which is one of the main reasons educators are trying to use data to inform their practices.

Teachers should adopt a systematic process for using data and data analysis in order to bring evidence to bear on their instructional decisions and improve their ability to meet students’ learning needs. They should also empower students to examine their own data and set learning goals.

If human capacity around data-driven decision-making is going to improve, data-driven concepts must be integrated with existing courses, and it is essential that formal pre-service courses along with in-service professional development and continuing education opportunities are available.

What We Mean When We Talk About Assessment

The reality is that standardized tests—“assessments of learning”—alone can measure only a few of the important skills and knowledge points that students will learn. Teachers need access to a variety of formative assessment data—“assessments for learning”—for analysis that can improve instructional practices through targeted professional development. The effective use of data in schools offers data-driven personalized pathways with a focus on accelerating student learning toward mastery that is central to college and career preparedness.

What profession, other than K–12 teaching, spends more time performing than preparing for performance? Systematic data analysis is about carefully preparing for performance. Implicit to this idea is that systematic analysis of how teaching produces learning is at the core of teachers’ inquiry into how to continually improve their practice. Yet the structures and opportunities to engage in these inquiries are virtually absent in the education system. Only when this occurs on a widespread basis can education come closer to reaching its goal of improving the learning outcomes of all students.

Enhancing teachers’ and administrators’ data analysis knowledge and skills—“data literacy”—requires systemic processes for collecting data about student learning, interpreting data to develop hypotheses about how to improve instruction, and modifying instruction to increase student learning. Districts and schools need to provide structured time for data team collaboration, continuous blended on-demand professional development learning opportunities, coaching support, easy access to data systems, adaptive learning tools, and dashboards, and technological tools to enhance learning.

The Takeaway

Why is data literacy important and why now? Teachers need credible evidence to inform their practice, and they need hard evidence to provide directions to help them solve pressing problems. The time is right for sustained institutionalization and transformation to data cultures. We now have the opportunity to utilize innovative data-driven tools for personalized learning. We must seize this chance. Carpe diem!

Want to learn more about using data to increase student outcomes? Read my white paper, Using Actionable Data to Personalize Math Learning.

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