CTAP4 Data Assessment

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.

Big Idea #1: Ensuring That Students Learn The professional learning community model flows from the assumption that the core mission of formal education is not simply to ensure that students are taught but to ensure that they learn. This simple shift—from a focus on teaching to a focus on learning—has profound implications for schools.

Big Idea #2: A Culture of Collaboration Educators who are building a professional learning community recognize that they must work together to achieve their collective purpose of learning for all. Therefore, they create structures to promote a collaborative culture.

Big Idea #3: A Focus on Results Professional learning communities judge their effectiveness on the basis of results. Working together to improve student achievement becomes the routine work of everyone in the school. Every teacher team participates in an ongoing process of identifying the current level of student achievement, establishing a goal to improve the current level, working together to achieve that goal, and providing periodic evidence of progress. The focus of team goals shifts. Such goals as "We will adopt the Junior Great Books program" or "We will create three new labs for our science course" give way to "We will increase the percentage of students who meet the state standard in language arts from 83 percent to 90 percent" or "We will reduce the failure rate in our course by 50 percent."

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.

1. If you're not using data to make decisions, you're flying blind.

2. This is all about a process, not a specific technology.

4. You will be spending more money, not less.

In this post I hope to share the essence of some of the main ideas communicated in the speech. The format is: words from the slide followed by a short narrative on the core message of the slide. Hope you find it useful.

The biggest challenge in our current environment is that it is trivial to implement a tool, it takes five minutes. But tools are limiting and can just give us data. What compounds the challenge is that we all have this deep tendency to make decisions that come from who we are influenced from our life experiences. Based on my humble experience of the last few years here are seven common sense recommendations for creating a data driven company culture……

# 6 Reporting is not Analysis

The popular term for what's going on in the Richmond School District and other school systems throughout the region is data-driven decision making. How that plays out varies from school district to school district, from weekly meetings and annual data retreats to regular standardized assessments of student performance. What it means is educators are getting more scientific in how they approach teaching and learning in today's schools.

Use of the data for instruction is still in its infancy, according to Laura Maly, a math instructional coach who works with teachers at Bradley Tech and Pulaski high schools on applying the benchmark assessments to their classroom work. But she's optimistic that the more teachers learn about what information is available to them on their students, the greater impact it will have.

One of the main obstacles that schools say they face in taking advantage of the plethora of information available to them in the technological age is finding time for teachers to study their students' academic performance on objective measures and plan ways to address any shortcomings. In the Oconomowoc School District, each school has held a "data day" for staff before the start of school for the last four years. The Wauwatosa School District is experimenting in several schools with having teachers gather to figure out how to take information from the MAP test and apply it in their classrooms.

The five assessment areas are: student achievement, which could include success on state tests and graduation rates; school operations, which could include teacher vacancies, class sizes and serious incidents; constituent satisfaction, which will look at results of student, parent and teacher surveys; school-selected indicators, which could include the percentage of students passing advanced classes, for example; and extra credit, which would be improvement in areas identified as challenging, such as increasing the number of students in the advanced category on the state's math and reading test.

Harnessing more powerful data tools and greater amounts of data.

Granting teacher, parent and community access to data

Planning and utilizing interoperability standards