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III. Data Literacy
The focus of this session is to be able to identify data sources that can assist districts/schools in making data-driven decisions. Data-driven decision-making is defined as the “process of collecting, analyzing, reporting, and using data for school improvement” (Dahlkemper, 2002). We will discuss what factors need to be in place for using data to inform decisions, and example data sources that can be used.
What is Data Literacy?
Data literacy refers to one's level of understanding of how to find, evaluate, and use data to inform instruction.
What is Data Literate?
A data literate person possesses the knowledge to gather, analyze, and graphically convey information and data to support decision-making.
Schools are often "data rich and information poor" (DRIP). Our schools are full of spreadsheets, reports, grade books, surveys, and databases that all hold “data” that might be important for our work. But how do we know whether our students are learning? What can we do? Educators must become data literate to answer these questions.
Becoming data literate means developing skills that help to ask significant questions, devise sensible and efficient ways to answer these questions, and then respond to the answers with changes to learning environments and instructional practices. A data literate person considers relevant data when making important decisions. This process is often called data-driven decision making and refers to teachers, principals, and administrators systematically collecting and analyzing various types of data to guide a range of decisions with the aim of helping to improve the success of students and schools.
What is Data Driven Decision Making D3M?
Data Driven Decision Making (D3M) is about collecting appropriate data, analyzing the data, getting the data to the people who need it, using the data to increase school efficiencies and improve students achievement and communicating those decisions to key stakeholders. Data is a powerful tool for districts to use, it can narrow achievement gaps, improve teacher quality, improve curriculum development, locate problems, share best practices, communicate needs, motivate students and increase parental involvement. Today’s educational leaders face an environment that requires real-time decisions and accurate, reliable and timely data. As a result of this, educational leaders face a growing need to gather, analyze and monitor more data than ever before in their oversight of schools (Mills, 2011). In order to utilize data properly, we must establish strong correlations between data and the decision made. Data-driven decisions must be based on data, not on personal opinion or belief. Data can be used to assess instructional practices, teacher effectiveness, student progress and organizational needs.
Aspects of Data Use
In the U.S. Department of Education report Teachers' Ability to Use Data to Inform Instruction: Challenges and Supports(2011), the research team identified five skills that cover the different aspects of data use that the experts thought teachers need to master if they are to use student data to improve instruction. These skills, which can be applied to all educators, are the abilities to
find the relevant pieces of data in the data system or display available to them (data location),
understand what the data signify (data comprehension),
figure out what the data mean (data interpretation),
select an instructional approach that addresses the situation identified through the data (instructional decision making), and
frame instructional relevant questions that can be addressed by the data in the system (question posing).
Multiple Uses of Data
Drives decisions and funding
Ensures that you are reaching EVERY student, so EVERY student benefits from your school counseling program
Creates an urgency for change
Creates the energy for change
Serves as a catalyst for focused attention
Challenges existing policies
Engages decision makers, district leaders, school teams in data driven decision making
Surfaces evidence of access or equity issues
Focuses resources where they are most needed
Supports grant writing efforts
Why is it important to have clean Data?
The four basic data types are described below.
Achievement or assessment data is used to determine the level of student achievement in a particular content area (such as performance-based assessments, written exams, or quizzes).
Demographic data is descriptive information about the school community such as enrollment, gender, ethnicity, economic status, student attendance, grade levels, school suspensions, and behavioral problems.
Program data define the programs, instructional strategies, and classroom practices of the teachers. Program data collected may be useful in making informed decisions about future program and curriculum choices.
Perception data tells us what students, parents, staff and others think about the learning environment. They include questionnaires, interviews, surveys, and observations. Collecting and evaluating perception data allows educations to pay attention to the opinions and ideas of the community.
Choose one scenario from the following documents. Work collaboratively to brainstorm data types that could be used to address the issue. Post one per group to the Google Document.
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