Mining the Data in Teacher Candidate Assessment and in Professional Development Schools

1. Mining the Data in Teacher Candidate Assessment and in Professional Development Schools Linda Oliva Assistant Clinical Professor oliva@umbc.edu

2. Turning lens on ourselves.How are we using data to design and revise our program? • NCATE • Reflection on data provides basis for program improvement • Driven by FELT NEED

3. Performance Assessment Five-Benchmark Model Courtesy Dr. Yi-Ping Huang, Director of Assessment

5. Sample rationale from teacher candidate’s website TESOL Professional Standard

7. The Data Drive Electronic Portfolio Development Project • Goals • To provide workshops and resources that support pre-service teacher candidates and mentor teachers to demonstrate their competencies various standards through the construction of electronic portfolios. • To provide training to pre-service candidates and mentor teachers on principles of student assessment, data analysis and the effective use of data in instructional processes. • To conduct ongoing evaluation and improvement of technology competencies, instructional processes and student achievement through the use of the Information and Assessment Systems. Sponsored from grant from MSDE

8. The value of ESSENTIAL data to Professional Development Schools • Supplements teachers’ observations of students • Facilitates clarity and specificity about students’ performance • Gives clear focus for effective problem solving and decision making • Facilitates collaboration and action research

9. The value of ESSENTIAL data to Professional Development Schools • DRIVES instructions • Provides reason for trip, map, road signs, crew members, mile markers, basis for correction when there are detours, micro and macro management, and destination.

10. Data Reflections MeetingsDiscussion Questions for Team Using your team summary data comparing Quarter 2 and Quarter 3, please answer the following questions: • Is the Quarter 3 AGL, OGL, BGL what you expected for: • Girls Boys • African American girl African American boys • Caucasian girls Caucasian boys • Asian girls Asian boys

11. Discussion Questions for Team What patterns, trends, or gaps do you see in the AGL, OGL, BGL data when you compare Qtr. 2 and Qtr. 3 data summaries? • If you have achievement gaps in any of your subgroup data, what strategies or interventions can we brainstorm to try to eliminate the gaps? • Using the team database information for reading (BMBL, Cluster 2 Score,CTBS-R), which students in your team are not achieving as you would expect?

12. For each child, how will you change his/her instruction or grouping, based on this information?Using the team database information for math (Unit tests, CTBS-M), which students in your team are not achieving as you would expect? For each child, how will you change his/her instruction or grouping, based on this information?Used with permission from Ms. Cynthia Hankin, Principal, Thunder Hill Elementary Discussion Questions for Team

13. Completing the Data Cycle with theStudent in the Center Instructional practices that optimize student achievement Have important questions that need information that can become knowledge Continuous Assessment Community of Inquiry School priorities and resources aligned Teachers and students engaged in the processes Technology tools that effectively analyze, display and disseminate data School personnel are comfortable with research methodologies and statistical concepts Multiple Sources of Trusted Data

14. Completing the Data Cycle with theStudent in the Center Instructional practices that optimize student achievement Have important questions that need information that can become knowledge Continuous Assessment Community of Inquiry School priorities and resources aligned Teachers and students engaged in the processes Technology tools that effectively analyze, display and disseminate data School personnel are comfortable with research methodologies and statistical concepts Multiple Sources of Trusted Data