AACTE Bortner Data Initiatives Sungti Hsu Director State Affiliates and Partnership Support Yupin Bae Manager Professional Education Data System

The Changing Data Landscape New mandates and opportunities Recognizing limits of PEDS Listening to perspectives from the field Suspending PEDS

AACTE Bortner Data Initiatives

Data Aggregation and Reporting Services Common Measures for Benchmarking Sample-Based Data Collection Model Data System Study

–Alex Peiniger “In the end, you should only measure and look at the numbers that drive action.”

Data Aggregation and Reporting Services Current Challenges Duplication in reporting New expectations on types and amounts of data collection Underutilized national data sets to increase comprehensive understanding of the field and continuously improve individual programs

Data Aggregation and Reporting Services Intended Outcomes An accessible and user-friendly central location for several major national data sets. A data dashboard and statistical tools with visual result display accessible to member institutions and non-member institutions with a fee. Periodic statistical update from AACTE. Periodic major statistical report from AACTE and partner(s).

Common Measure for Benchmarking Current Challenges Lack of common measures that would make benchmarking possible Uneven understanding of the definition of the data points collected Limited information collected on other school personnel

Common Measure for Benchmarking Intended Outcome Promote well established and commonly understood measures as benchmarks Supplementary/appendix survey on the advanced programs that prepare other school personnel

Sample-Based Data Collection Current Challenges Duplication of collection Limited scope of available data

Sample-Based Data Collection Public documentation of a variety of issues related to the education workforce Targeted data collection (Sample survey) Teacher Educators Educator Preparation Providers Graduates Employers (LEA/SEA) Parents Other Stakeholders Begin with graduate (teacher and principal programs) and employer satisfaction surveys

Sample-Based Data Collection Intended Outcomes Member institution access to the survey questions and results to duplicate the survey study locally National result as benchmarking tool

–Dan Heath “Data are just summaries of thousands of stories-tell a few of those stories to help make the data meaningful.”

Model Data System Study Current Challenges Integrating data and evidence from disparate sources and data systems into a functioning quality assurance system Developing routine and effective means of engaging key stakeholders in evidence-based discussion with the aim of improving programs and supporting new teachers’ development

Model Data System Study Case Studies with Two Strands of Investigation STRAND ONE: focusing on the extent to which the EPP has succeeded in integrating data from relevant measures and sources into its functional data system, with particular attention to hoped-for developments and challenges or road-blocks to accomplishing them STRAND TWO: focusing on the degree to which the EPP’s interpretation of tis evidence regarding graduates’ competence in two representative areas is consistent with employer and graduate perceptions of that competence.

Model Data System Study Intended Outcomes Reports to campuses identifying strengths and opportunities for improvement in structure and functioning of data systems with discrepancy analysis (noting both areas of agreement and divergence in) in graduate/employer perceptions of preparation Publicly posted case study summaries of individual campus accomplishments and aspirations regarding data use in program improvement and regarding stakeholder engagement Cross-case analysis of data system integration with suggested strategies for optimizing systems for data use Cross-case analysis of stakeholder engagement dynamics suggested recommendations for engagement strategies

–Bob Parsons “Everything that is measured and watched, improves.”

Thank you