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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."

As school districts embark on the change process, they face many barriers to the adoption of data-driven decision making. School district leaders have not embraced continuous improvement. Priorities are not clear and goals are not tied to measurable objectives. Data is not collected uniformly between organizations and over time. Outdated technology cannot be used effectively. Educators lack training to define data requirements and apply data. Stakeholders do not trust the data collected or how it will be used.

Reports need to be timely, tied to objectives, and available to people with the responsibility and ability to act on them. Data reports that show data in different ways such as tables, charts, graphs, and trends enable more people to access and understand the information. Some of the decisions that might be made with data reports include: Tracking student achievement for diagnosis and placement Changing beliefs and attitudes that all students can learn Guiding teacher professional development Linking interventions to results Using data to create school improvement plans and assess progress Allocating district resources

The IT infrastructure underpinning most data-driven decision making systems requires a significant investment in hardware, software, implementation, and maintenance.

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.