Education for the Future eff.csuchico

1. Using Data to Improve Student Learning Arizona Professional Development Leadership Academy Education for the Future http://eff.csuchico.edu Bradley J. Geise bgeise@csuchico.edu

2. Education for the Future Background • Education for the Future – Non-Profit Initiative • Victoria L. Bernhardt, Exec Director • California State University, Chico • Our Mission • Funded by contracts. • 17 Books, Conferences, Institutes, Workshop. • Manage long-term implementation contracts.

4. Education for the Future Outcomes • Why and what data are important for continuous improvement. • How to use data for continuous improvement. • How to involve allstaff in using data. • How to solve a “problem” using data. • How to create a shared mission and vision. • How to create a quality plan from data. • Resources available for taking the work home.

5. Education for the Future Agenda • WHY data analysis/continuous school improvement? • WHAT data should we engage as part of school improvement? • HOW do we take the work back and implement with our staff? • Tools and resources available.

6. Education for the Future Building Buy In and Perspective Why Data Analysis? Why Continuous School Improvement?

7. Education for the Future Why Data Analysis? Mapping Your History of School Improvement. Gather according to the year you started your career in education. What was going on in the name of school improvement when you first started?

8. Education for the Future Why Data Analysis? Mapping Your History of School Improvement. Inyour experience: How was an initiative determined to be effective? How were new initiatives deemed necessary?

10. Mission Vision Continuous Improvement Cycle

18. Education for the Future A Process of Evidence and Engagement • Evidence: • Data to inform and drive a logical progression of next steps. • Engagement: • Bringing staff together to inform change through the use of data to move from personality driven to systemic and systematic.

21. Education for the Future Where are we now? Comprehensive Data Analysis Using demographic, perception (climate, environment), school process, and student learning data to identify strengths, challenges, and implications for the plan.

22. Education for the Future Where are we now? Comprehensive Data Analysis Evidence

26. Education for the Future Demographics • Enrollment • Gender • Ethnicity / Race • Attendance (Absences) • Expulsions • Suspensions

27. Education for the Future Demographics • Language Proficiency • Indicators of Poverty • Special Needs/Exceptionality • IEP (Yes/No) • Drop-Out/Graduation Rates • Program Enrollment

28. School and Teaching Assignment Qualifications Years of Service Gender Additional Professional Development Education for the Future Staff Demographics

29. Education for the Future Demographics What student demographicdata elements change when leadership changes?

33. Help us understand whatstudents, teachers, and parents are perceiving about the learning environment. We cannot act different from what we value, believe, perceive. Education for the Future Perceptions

34. Student, Staff, Parents,Alumni Questionnaires Observations Focus Groups Education for the Future Perceptions

35. Education for the Future Perceptions What do you suppose students say is the #1 “thing” that hasto be in place in order for them to learn?

39. Education for the Future Student Learning • Diagnostic Assessments(Universal Screeners) • Classroom Assessments • Formative Assessments(Progress Monitoring) • Summative Assessments(High Stakes Tests, End of Course)

44. Education for the Future School Processes Schools are perfectly designed to get the results they are getting now. If schools want different results, they must measure and then change their processes to create the results they really want.

45. Education for the Future School Processes Processes include… • Actions, changes, functions that bring about a desired result • Curriculum, instructional strategies, assessment, programs, interventions … • The way we work.

46. Education for the Future School Processes • The missing link in improving K-12 education • The missing link in meeting NCLB requirements

47. Input Data elements that describe what learning organizations start with. The “givens” are usually beyond our immediate control. Process Elements that describe the actions learning organizations plan for and implement to get the outcomes they are striving to achieve, given the input. Outcome The data elements that describe the results of a learning organization’s processes.