1. Advancing Assessment Literacy Data Gathering III: Identifying & Valuing Different Types of Data

2. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 2 32° What might the above piece of data mean? While 32° is data, the meanings you provided were interpretation. All data is meaningless until interpreted. Wellman, B. & Lipton, L. (2004). Data-driven dialogue. Mira Via, LLC.

3. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 3 A Data-Rich Environment Wellman & Lipton (2004) state: Schools and school districts are rich in data. It is important that the data a group explores are broad enough to offer a rich and deep view of the present state, but not so complex that the process becomes overwhelming and unmanageable. Wellman, B. & Lipton, L. (2004). Data-driven dialogue. Mira Via, LLC.

4. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 4 Types of Data QuantitativeQualitative Numerical in form Efficient to analyze Objective Not numerical in form Can be more than words or text – pictures, artifacts, etc. More complex to analyze Subjective

5. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 5 International Data Sources Programme for International Student Assessment (PISA) What utility can we gain from this data? What is its impact on classrooms? http://snes.eas.cornell.edu/Graphics/earth%20white%20background.JPG

6. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 6 National Data Sources Pan-Canadian Achievement Program (PCAP) Canadian Test of Basic Skills (CTBS) Canadian Achievement Tests (CAT3) What utility can we gain from this data? What is its impact on classrooms? http://www.recyclage.rncan.gc.ca/images/canada_map.jpg

7. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 7 Provincial Data Sources Assessment for Learning (AFL) Departmentals What utility can we gain from this data? What is its impact on classrooms? http://regina.foundlocally.com/Images/Saskatchewan.jpg

8. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 8 Division Data Sources Division level rubrics Division benchmark assessments What utility can we gain from this data? What is its impact on classrooms? http://www.sasked.gov.sk.ca/branches/ed_finance/north_east_sd200.shtml

9. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 9 Local Data Sources Teacher designed assessments Portfolios Routine assessment data What utility can we gain from this data? What is its impact on classrooms?

10. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 10 Nature of Assessment Data From Understanding the numbers, Saskatchewan Learning DefinitiveIndicative Individual Classroom SchoolDivision Provincial National International Student EvaluationsSystem Evaluations

11. 11. Marazano, 1996 Depth and Specificity of Knowledge From Understanding the numbers, Saskatchewan Learning Little knowledge of specific students In-depth knowledge of specific students IndividualNationalSchoolClassroomInternationalDivisionProvincial Assessments In-depth knowledge of systems

12. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 12 Using a Variety of Data Sources Thinking about the data sources available, their nature and the depth of knowledge they provide, how might the information in each impact decisions affecting classroom instruction? As a table group, create a statement or scenario which documents the journey of response and decision making based on different levels of data.

13. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 13 Local Level Sources of Data While international, national and provincial sources of data can provide direction for system or school initiatives, the data collected at the local level is what provides the most detailed information regarding the students in classrooms.

14. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 14 Four Major Categories of Data Demographics –Descriptive information such as enrollment, attendance, ethnicity, grade level, etc. –Can disaggregate other data by demographic variables. Perceptions –Provides information regarding what students, parents, staff, and community think about school programs and processes. –This data is important because people act in congruence with what they believe. Student Learning –Describes outcomes in terms of standardized test results, grade averages, etc. School Processes –What the system and teachers are doing to get the results they are getting. –Includes programs, assessments, instructional strategies, and classroom practices. Bernhardt, V. L. (2004). Data analysis for continuous school improvement, 2 nd Edition. Larchmont, NY: Eye on Education.

15. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 15 What Data Are Useful & Available? Refer to the goals and the questions you created in the previous workshop. Using the supplied template, begin to catalogue the data you already have and the data you need to gather in order to answer the questions raised concerning the goals you have set. An example follows on the next slide.

16. Questions What data do you have, or need, to answer the questions? What other data do you have, or need, to gather? Demographics Attendance Enrollment by gender Projections of future enrollment Perceptions Student profiles School Community Council feedback Student Learning AFL and CAT3 results Would like school- specific benchmark assessment data School Processes Guided reading program data Impact of study hall Effect of resource room attendance Bernhardt, V. L. (2004). Data analysis for continuous school improvement, 2 nd Edition. Larchmont, NY: Eye on Education.

17. Advancing Assessment Literacy Modules: Data Gathering III (February 2008) 17 Next Steps More time may be needed to complete the data sources template. If so, set another time to meet or set a deadline for their submission. The next module will focus on ways that data can be analyzed.