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Elrod Darnel

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Anne Bubnic

Improving Teaching and Learning with Data-Based Decisions - 0 views

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    "Data-based decisions"-the phrase has become a buzzword in education over the last few years. However, it does make sense that using information to help clarify issues, identify alternative solutions to problems, and target resources more effectively will lead to better decisions. The real question should not be whether to integrate the use of data in decision making, but how.Finding good data and using it effectively is actually a complex process-one that many schools and districts are just beginning to address. One specific type of data-based decision making that shows promise for helping schools dramatically increase student achievement is the use of assessment data to drive instructional improvement
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Taking data to new depths [Nancy Love] - 0 views

  • While collaborative inquiry is appropriate for any content area, it is particularly relevant for mathematics and science because the process mirrors for the adults what students experience in our best mathematics and science classrooms. Data teams investigate not scientific phenomena or mathematics problems, but how to improve teaching and learning. They raise questions, examine student learning and other data, test their hypotheses, and share findings with their colleagues.
  • Typically, one or two teachers, one administrator, and one NSF project staff member become data facilitators for a school. They then convene school-based data teams to focus on improving mathematics and science. Sometimes team members are from the mathematics or science department or are existing grade-level teams. Other times, the team is schoolwide.
  • If data facilitators have only one source of data on student learning, they collect additional data such as local assessments or common grade-level and course assessments for the next data facilitator session. The process emphasizes triangulating data, using three different sources of student learning data before identifying the student learning problem. By triangulating, data facilitators guide data teams to test hunches with other data instead of drawing conclusions from a single measure.
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  • In their data facilitator workshops, data facilitators use the "go visual" principle, first developed by nonverbal communications expert Michael Grinder (1997). Grinder revealed the power of large, visually vibrant and color-coded displays of data in fostering group ownership and engagement. Data facilitators work with the team on one data report at a time to avoid overload and confusion. For each report, they create a colorful newsprint-sized graph displaying the results and post it on their "data wall." Then they record their observations and inferences on additional pieces of newsprint that they post under their chart. As they work with additional data, they add more graphs and more observations and inferences to their data wall.
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    There's a ton of data being collected. The trick is to know how to use it effectively.
Anne Bubnic

On the Road to DDDM [Dian Schaffhauser] - 0 views

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    April 2008 : THE Journal. As each district progresses, it will face new challenges discerning what data is relevant, addressing tolerance for change among users, and figuring out how to respond now that data is driving its decision-making.
    \nStage 1: Define the Outcomes
    \nStage 2: Define the Questions
    \nStage 3: Collect and Sort
    \nStage 4: Extract Meaning
    \nStage 5: Take Action
    \nStage 6: Evaluate Outcomes, Modify as Needed

Anne Bubnic

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    This book is a step by step recipe book for using data in your schools. "It answers the who, what, when, where, and how. The book is not a complicated read and it is an excellent book for a book study with the entire staff.This book will help staffs to understand how data can help to improve the climate and culture in a school, instruction and the academic outcome of all students."
Anne Bubnic

Data-Driven Decision Making Gone Wild: How Do We Know What Data to Trust to Inform Deci... - 0 views

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    It's not as easy as it looks to determine which schools are doing better than others. Two different criteria are relevant: is the difference in performance between two schools large enough to matter, which is sometimes termed educational significance or practical significance; and is the difference in performance between two schools real, or could it just be due to chance, which is typically described as statistical significance. Ideally, we are interested in differences that are both practically and statistically significant. But a difference could be large, but not statistically significant (which is often the case when we have a small sample of information about performance), or statistically significant, but very small (in which we are pretty sure that the difference is real, but it's just not very important). (Yes, statistical significance does matter!)
Anne Bubnic

Analyzing and Using the Data - 0 views

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    A major purpose of the monitoring plan is to put in place a system for the ongoing collection of data aligned with the content standard indicators/objectives you are responsible for teaching. You can't analyze data you don't have. And it is not useful to analyze data that is not aligned with your learning outcomes. Once you have the data, you are ready to analyze it.
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Looking Back, Looking Forward / A Focus on Assessment - 1 views

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    During the last 65 years, ASCD publications have charted the education profession's perennial quest to design and use assessments wisely. ASCD and its members have consistently urged clarity about the purposes of evaluation and advocated the use of assessments that are appropriate for their specific purposes. A thread that runs through six decades of writing about assessment is the belief that assessments should answer two questions: How are we doing? How can we do it better?
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Data done right - 0 views

  • This is the NCLB model. Schools are expected to collect data once a year, slice and dice them in various ways, set some goals based on the analyses, do some things differently, and then wait another whole year to see if their efforts were successful. Somehow, this model is supposed to get schools to 100% proficiency on key learning outcomes.
  • he key difference in this model is an emphasis on ongoing progress monitoring and continuous, useful data flow to teachers
  • Under this approach, schools have good baseline data available to them, which means that the data are useful for diagnostic purposes in the classroom and thus relevant to instruction. The data also are timely, meaning that teachers rarely have to wait more than a few days to get results. In an effective data-driven school, educators also are very clear about what essential instructional outcomes they are trying to achieve (this is actually much rarer than one would suppose) and set both short- and long-term measurable instructional goals from their data.
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  • It is this middle part of the model that often is missing in school organizations. When it is in place and functioning well, schools are much more likely to achieve their short- and long-term instructional goals and students are much more likely to achieve proficiency on accountability-oriented standardized tests. Teachers in schools that have this part of the model mastered rarely, if ever, complain about assessment because the data they are getting are helpful to their classroom practice.
  • When done right, data-driven decision-making is about helping educators make informed decisions to benefit students. It is about helping schools know whether what they are doing is working or not
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    Thoughtful analysis from Scott McLeod. In his work with numerous school organizations in multiple states, he has seen the power of data firsthand. When done right, data-driven education can have powerful impacts on the learning outcomes of students. Unfortunately, most school districts still are struggling with their data-driven practice. Much of this is because they continue to think about using data from a compliance mindset rather than using data for meaningful school improvement
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