Abstract
Educational leaders and teacher educators face a very real dilemma within the current teacher education policy context, as there currently exist numerous accreditation and accountability mandates for which teacher education programs are responsible. Many of these accountability pressures require teacher educators and teacher education programs to provide evidence of consistent data use for continuous program improvement. Teacher education programs are also increasingly responsible for providing evidence-based measures of the quality of their teacher candidates. These data-driven expectations can create additional pressures, stresses, and workload responsibilities for teacher educators and teacher education program leaders. By drawing on the lessons learned from three exemplary teacher education programs, this chapter will discuss the benefits and the challenges related to organizing collaborative data use efforts within teacher education programs for the purpose of fostering teacher candidate development.
TopEast Carolina University
East Carolina University is a large public university in rural eastern North Carolina, and the College of Education within the university is the primary preparer of teachers in the region. In recent years, East Carolina University was prompted to respond to the requirement in the state of North Carolina that all teacher education programs within a given university must supply three evidences of teacher candidate outcomes and that all programs within a given university must use the same evidences. In addition to this, the state also began to use student standardized achievement test data as an outcome measure of program success. Thus, the accountability pressure for the teacher education program at East Carolina University significantly increased. The case of this program, therefore, represents an illustrative case related to moving teacher education programs towards increased data use. Three themes from this program’s transition will be highlighted: responding to faculty resistance, encouraging experimentation, and the development of a central storage system for the data.
Key Terms in this Chapter
Data Use: Purposeful use of data reports for program improvement, including formal discussion and analysis of information gathered as a result of coursework and/or observations of teacher candidates.
Program Improvement: The continuous ongoing effort to achieve measurable outcomes in program performance by making strategic, data-driven revisions to program design. These revisions can include revisions in curriculum reform, revisions to rubrics and assessment tools, and revisions of criteria for and indicators of high-quality teaching.
Program Personnel: Individuals who are responsible for and active in the development of teacher candidates and also who are responsible for the process of data collection and data analysis. Program personnel can include faculty, administrative leaders, field supervisors, mentor teachers, data management staff and specialists, and district stakeholders.
Performance Assessment: An educational assessment that asks teacher candidates to demonstrate their knowledge and skills in a performance-related task, such as facilitating a whole-group classroom discussion. These assessments ask teacher candidates to apply what they know and to demonstrate their mastery of specific practices in authentic settings.
Data Systems: Institutionalized, centralized systems that collect, store, and analyze data collected across the teacher education program.