Definition of Data Management

Administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users.

 Making Sense of Data-Driven Decision Making in Education

Educators proclaim, “We are completely data-driven.” In recent years, the education community has witnessed increased interest in data-driven decision making (DDDM)— making it a mantra of educators from the central office, to the school, to the classroom.

DDDM in education refers to teachers, principals, and administrators systematically collecting and analyzing various types of data, including input, process, outcome and satisfaction data, to guide a range of decisions to help improve the success of students and schools.

Achievement test data, in particular, play a prominent role in federal and state accountability policies. Implicit in these policies and others is a belief that data are important sources of information to guide improvement at all levels of the education system and to hold individuals and groups accountable. New state and local test results are adding to the data on student performance that teachers regularly collect via classroom assessments, observations, and assignments.

As a result, data are becoming more abundant at the state, district, and school levels—some even suggest that educators are “drowning” in too much data (Celio and Harvey, 2005; Ingram, Louis, and Schroeder, 2004). Along with the increased educator interest in DDDM has come increased attention from the research community to understand the processes and effects of DDDM.

Yet there remain many unanswered questions about the interpretation and use of data to inform decisions, and about the ultimate effects of the decisions and resulting actions on student achievement and other educational outcomes. Recent research has begun to address some of the key questions related to DDDM.

 Data-Driven Decision Making Can Improve Student Learning

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.

According to " Making Sense of Data-Driven Decision Making in Education,", " Data-driven decision making can improve student learning," the authors explained the meaning of Data-Driven Decision Making in Education. They explained importance and role DDM in education such as Total Quality Management, Organizational Learning, and Continuous Improvement, which emphasize that organizational improvement is enhanced by responsiveness to various types of data. Then, they shed light on types of data that using by administrators and teachers.

One of the most popular types of student outcome data, are summative most of them are designed to test students’ knowledge on a broad range of skills and topics that should have been learned by the time of the exam. After that, they explained how are administrators and teachers using these data. They also addressed the kinds of support are available to help with data use. Moreover, the articles shed light on factors influence the use of data for decision making such as accessibility of data, quality of data, motivation to use data, timeliness of data, staff capacity and support, and history of state accountability.

In fact, 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 Dream Box 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. In my opinion, Data-driven decision making will be one of the key factors in changing the future of education.

There is so much great work being done with data analysis and data linkage tools for the future of education. Linking K-12 data with college and career data will certainly have a positive, significant impact on student achievement.

  • Data management

    The skills and equipment used to organize, secure, store and retrieve information. Data management technology can refer to a wide range of techniques and database systems used for managing information use and allocating access both within a business and between entities.

  • Maker Movement

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  • Engaging and meaningful use of technologyThe methods of integrating technology in your lesson plans and courses that provide for an engaging experience for teachers and students.

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