Data-driven decision making (DDDM), applied to student achievement

testing data, is a central focus of many school and district reform

efforts, in part because of federal and state test-based accountability

policies. This paper uses RAND research to show how schools and

districts are analyzing achievement test results and other types of data

to make decisions to improve student success. It examines DDDM

policies and suggests future research in the field. A conceptual

framework, adapted from the literature and used to organize the

discussion, recognizes that multiple data types (input, outcome,

process, and satisfaction data) can inform decisions, and that the

presence of raw data does not ensure its effective use. Research

questions addressed are: what types of data are administrators and

teachers using, and how are they using them; what support is available

to help with the use of the data; and what factors influence the use of

data for decision making? RAND research suggests that most educators

find data useful for informing aspects of their work and that they use

data to improve teaching and learning. The first implication of this work

is that DDDM does not guarantee effective decision making: having

data does not mean that it will be used appropriately or lead to

improvements. Second, practitioners and policymakers should promote

the use of various data types collected at multiple points in time. Third,

equal attention needs to be paid to analyzing data and taking action

based on data. Capacity-building efforts may be needed to achieve this

goal. Fourth, RAND research raises concerns about the consequences

of high-stakes testing and excessive reliance on test data. Fifth,

attaching stakes to data such as local progress tests can lead to the

same negative practices that appear in high-stakes testing systems.

Finally, policymakers seeking to promote educators’ data use should

consider giving teachers flexibility to alter instruction based on data

analyses. More research is needed on the effects of DDDM on

instruction, student achievement, and other outcomes; how the focus

on state test results affects the validity of those tests; and the quality

of data being examined, the analyses educators are undertaking, and

the decisions they are making.

Research conducted by

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.

- See more at: http://www.dreambox.com/blog/adaptive-learning-enables-data-driven-decision-making#sthash.ETxDRf9F.dpuf

Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success.[1] DIDM is used in education communities (where data is used with the goal of helping students) but is also applicable to (and thus also used in) other fields in which data is used to inform decisions. While data based decision making is a more common term, data-informed decision-making is a preferable term since decisions should not be based solely on quantitative data.[1][2] Most educators have access to a data system for the purpose of analyzing student data.[3] These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators’ data-informed decision-making.[4] In Business, fostering and actively supporting DIDM in their firm and among their colleagues could be the main rôle of CIOs (Chief Information Officers) or CDOs (Chief Data Officers).[5]

Apa itu Data-Driven Decision Making?

Data-Driven Decision Making atau sering disingkat DDDM

adalah sebuah metode pengambilan keputusan berdasarkan

data yang ada. Data itu bisa berupa apa aja seperti misalnya

hasil survey, polling, statistik dll. Sebagai contoh, misalnya

Asep (ini nama asal-asalan) ingin mencari ponsel baru tapi

masih bingung mau beli ponsel apa yang cocok dan sesuai

budget. Akhirnya pilihan Asep mengerucut pada iPhone 4S dan

Samsung Galaxy S III. Maka Asep pun mencari data sebanyak-

banyaknya tentang kedua ponsel tersebut, mulai dari

spesifikasinya, kelebihan dan kekurangannya, review dari

pengguna dll. Ketika akhirnya Asep memutuskan untuk

membeli iPhone, maka keputusannya itulah yang disebut

DDDM.

Nah, saya juga memakai metode DDDM ini ketika melakukan

redesain. Dengan melihat statistik browser yang dipakai

pengunjung dari Google Analytics, saya bisa melihat kalau

pengunjung blog saya rata-rata memakai browser modern. Dari

statistik itu, saya putuskan untuk melakukan redesain dengan

meniadakan support untuk browser lama.