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LEARNING ANALYTICS: Effective Use of Student Data Is Essential to Personalizing Learning and Increasing Student Achievement, Finds New Alliance Report

“'Moneyball' changed the way statistics were used in baseball; significant improvement in technology tools and resources, the implementation of the Common Core State Standards, and the focus on personalized learning for all students provide a similar ‘game-changing’ moment for education," said Bob Wise, president of the Alliance and former governor of West Virginia.

A new Alliance for Excellent Education report finds that the effective use of student data can improve teaching and learning by empowering educators to personalize instruction and increase student achievement for all students, especially those in the highest-need schools.

“This is a ‘Moneyball’ moment for education,” said Bob Wise, president of the Alliance for Excellent Education and former governor of West Virginia. “Moneyball changed the way statistics were used in baseball; significant improvement in technology tools and resources, the implementation of the Common Core State Standards, and the focus on personalized learning for all students provide a similar ‘game-changing’ moment for education. But let me be clear—success depends on confronting fast-growing issues of how data is collected while maintaining student privacy and addressing concerns from parents and the public.”

The report, Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice, focuses on “learning analytics,” which is defined as data collection and analysis for the purposes of understanding and optimizing student learning and classroom teaching. It includes student data collected through the administrative process as well as during the teaching and learning experience and permits educators to respond to data in the form of adapting instructional content, intervening with at-risk students, and providing feedback to students on what they have learned.

The effective use of data and learning analytics are both critical components of a digital learning strategy to personalize instruction for students, the report finds. The idea of learning analytics is not new—states and school districts nationwide, including several cited in the report, are moving from being data collectors to being data analyzers. For example, Kentucky linked K–12 and postsecondary data to provide high schools with a clear understanding of their students’ preparedness for and achievement in college. Utica Community Schools in Michigan created a data system that shares assignments, grades, and other information with parents and students. It includes a calling system to inform parents of emergencies and identifies student learning needs.

At the same time, however, the U.S. education system is not close to reaching the full potential of learning analytics to improve instruction for students, the report notes. In some cases, there is an overwhelming quantity of data without an organized approach to using it; in others, useful data is not available in a timely manner. Regardless of the reason, the report says that states, districts, and schools must build and improve capacity to reach the full potential of learning analytics by

  • providing infrastructure and technology that fosters transparency between educators, administrators, parents, and students;
  • shifting to a culture of data-informed decisionmaking by well-trained educators;
  • strengthening human capital at all levels of the education system—states, districts, schools, and classrooms—by training educators and administrators to use and understand data; and
  • supporting teachers through professional learning communities, including data teams, intra-district communication, and social media.

To build capacity for the effective implementation of learning analytics, policymakers and education leaders at all levels must develop a clear understanding of its potential and rationale. The report includes a set of recommendations for federal, state, and district leaders that will help ensure that policies enable the use of data while providing necessary privacy safeguards. For example,

  • federal education leaders should continue to clarify and provide technical assistance on the Family Education Rights and Privacy Act (FERPA) and the Children’s Online Privacy Protection Act (COPPA); work to increase the cap on E-Rate funding; and embed incentives that support learning analytics in the next reauthorization of the Elementary and Secondary Education Act;
  • state education leaders should understand and guide districts on how federal laws, such as FERPA and COPPA, apply to the use of student-level data to improve instructional practice; consider policies that leverage the Common Core State Standards and college- and career-ready standards; develop policies so that state longitudinal data systems can interact with district data systems; consider policies to include learning analytics as a required aspect of teacher certification, preparation, and evaluation; and
  • local education leaders should provide administrators, educators, and parents with a succinct explanation of how the district’s implementation protects a student’s privacy and elevate learning analytics as an essential component of professional development.

The report also stresses the importance of funding models to support learning analytics and conducting research to support the capacity building policies critical for learning analytics.

An executive summary of Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice, as well as the full report is available at


Data and Privacy


  1. photo
    John Harris Loflin
    Posted 7 years ago

    Mr. Wise

    Speaking of analysis, please critically analyze this approach to children. As an urban educator, I suggest authentic relationships not positivism and instrumentalism as a guide. Using this so-called Learning Analytics approach to enabling good test takers belongs in a factory and misses the whole point of learning.

    Education-Community Action Team

  2. photo
    Tom Murray
    Posted 7 years ago

    Your point about ‘enabling good test takers’ is not the premise of personalized learning or data analytics as we see it. We believe that teachers should be given the tools so that they can utilize assessment data (both formative and summative) to drive instruction for kids so that each child’s needs are met. No longer can we afford the ‘one size fits all’ model of years ago, and no longer is it relevant from what we know about brain research and how students learn. When data is utilized properly, teachers have very specific information with which to instruct students. When the instruction is personalized, students will have a better opportunity to maximize their potential. Data analytics isn’t about bubbles on a sheet; it’s about meeting the needs of every student and giving teachers the tools to be able to do just that.

    Tom Murray
    State and District Digital Learning Director
    Alliance for Excellent Education | @thomascmurray

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