CTAP4 Data Assessment

Researcher Ken Futernick (2007), after surveying 2,000 current and former teachers in California,concluded that teachers felt greater personal satisfaction when they believed in their own efficacy, were involved in decision making, and established strong collegial relationships.

School leaders who foster collaboration among novice and veteran teachers can improve teacher retention and teacher satisfaction, according to studies conducted by Susan Kardos and Susan Moore Johnson.

n Tennessee, school performance coaches receive specialized training to facilitate improvements in low-performing schools and districts. Helping teachers collaborate in meaningful ways is part of the work.

A Way of Thinking in a Professional Learning Community: Four Principles Begin with Building a Guiding Coalition

Both formative assessment and assessment for learning are intended to provide information early enough in the decision-making process to influence student learning. As traditionally conceived, formative assessment helps teachers group students more effectively and select appropriate instructional interventions. The teacher uses the assessment information. However, the litmus test of an effective assessment for learning is that it informs students about their own learning, helping them focus their learning energies where they are likely to be most effective. So formative assessment enlightens the teacher, while assessment for learning enlightens the student

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."

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.