Data-driven decision making can improve student learning
For centuries, members of the education community have been looking for ways to improve student learning. In more recent history, data-driven decision making has been used in an effort to fulfill this mission, particularly as many students are being asked to meet more rigorous academic goals via the Common Core State Standards.
The idea of using data as a means to guide decisions made by school districts, administrators and teachers is nothing new. An emphasis was placed on collection of student data as a means of increasing achievement when the No Child Left Behind Act was put into place in 2001.
While schools have been collecting student data for decades, it is only more recently that they have discovered the power of data-driven decision making. Many school districts have used the data collected from various standardized tests that students take in order to improve curricula, boost teacher quality and share best practices among schools and districts.
In fact, research has shown that if curricula and instruction plans at all levels – county, district, classroom, and individual students – are based on information gathered from assessments, the probability that students will attain desired learning outcomes increases.
U.S. Secretary of Education Arne Duncan has been a big proponent of data-driven decision making, explaining in 2010 that “our best teachers today are using real-time data in ways that would have been unimaginable just five years ago. They need to know how well their students are performing. They want to know exactly what to teach and how to teach it.”
But how could teachers be using real-time data to influence their instruction when standardized tests are only give out once a year? For many school districts, the answer has been intelligent adaptive learning systems. While schools routinely gather massive amounts of student data via local, state and national testing, that data is not always accessible and teachers don’t always know how to use it effectively.
Why use adaptive learning programs?
Much has been said about the academic benefits that adaptive learning technology can offer students by constantly reevaluating its approach to instruction in order to help students achieve at the highest levels. Intelligent adaptive learning systems like DreamBox have the ability to produce millions of different individualized learning pathways to meet the unique needs of students. This has been shown to provide a more personalized learning experience for each student that helps them truly internalize and deeply understand new concepts.
However, what hasn’t been discussed at length is how the data constantly being collected by adaptive learning programs can be used by teachers and administrators to track student proficiency and progress and adjust curricula accordingly. When standardized tests are taken at the end of each school year, it’s too late for the data derived from those exams to do anything for the student who took them. While this data can certainly drive decision-making and help teachers and administrators develop more effective approaches to education, it doesn’t do much to help students who are struggling when they need help most.
In order for adaptive learning systems to work, they must constantly monitor and analyze student interactions with the program. In this way, the system can customize lesson pacing and presentation, offering a pedagogical approach that is most appropriate to the individual student’s learning style. Adaptive learning systems simultaneously assess and instruct in real-time, and tracks the millions of data points that it collects so that it can provide real-time reporting for teachers and administrators. This allows educators an immediate and valuable insight into the learning levels of students so that they can deduce where additional instruction may be required.
As a result, adaptive learning technology and the data it collects give students the personalized learning experience that schools would like to provide, but may have difficulty doing as classroom sizes increase and budgets decrease.
How does this apply to data-driven decision making?
While the technology can be an immensely helpful component of improving student learning, what teachers and administrators choose to do with the data that is collected is what truly makes a difference. Data can help school districts notice things they may not otherwise see when that data is examined from all angles. There’s no sense in collecting all of that data if it is not going to be put to use.
With the real-time reporting of student data that comes from the use of adaptive learning technologies, it’s possible to find the root causes of problems schools and students are facing, rather than simply treating the symptoms. Educators can also use the data to develop after-school and summer school programs and to modify programs or approaches that are not working.
Do you use data-driven decision making into your school? Read DreamBox’s free white paper for more information about how data can improve student learning.
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