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Advancing Assessment Literacy. Data Analysis II: Examining &amp; Interpreting Data. Predicting. All data are meaningless until we attach meaning through interpretation. From http://www.canadiantestcentre.com/Teachers/InformationForTeacher.asp#result. Cut Scores.

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1. Advancing Assessment Literacy Data Analysis II: Examining & Interpreting Data

2. Predicting All data are meaningless until we attach meaning through interpretation. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

4. Cut Scores • On page 4 of the detailed reports you will find the cut scores detailing the percentage correct required for students to be classified at one of two levels: • Threshold of adequacy • Threshold of proficiency • Reader response and Math challenge scores are presented on a five-level scale (1-low to 5-high). Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

5. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

6. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

7. Locating Cut Scoresin the Report • Turn to pg. 4 in the detailed report for your grade level. • You will need to refer to these scores during the following prediction activity. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

8. Predicting Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

9. AFL Math Percentage of Students who met the Adequate Standard set by Saskatchewan Educators Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

10. Shade in your prediction on the supplied prediction chart. AFL Math Percentage of Students who met the Adequate Standard set by Saskatchewan Educators Wellman, B. & Lipton, L. (2004). Data driven dialogue. Mira Via, LLC.

11. Based on your predictions, create a set of hypotheses for some or all of them. As you create each hypothesis, identify the underlying assumptions. Hypothesis – “X” will contain the highest scores. Assumption – we created a common assessment for “X” in 2005. Hypothesis – students will report higher on digging in and starting reading than looking at the front and back covers to get an idea of what the book is about. Assumption – students expect to discover what the book is about in the first few pages. Write each hypothesis and its accompanying assumption on the cards provided. Please write legibly. Hypothesesand Assumptions Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

12. Sharing • Gather the cards together at your table and either pass them to the next table or shuffle them to discuss as a group. • When you receive a set of cards, discuss the hypotheses and assumptions. • Do you see any patterns? • Are any similar or different? • Why might this be? Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

13. Comparisons The completed bar graphs have been supplied to your table. • What are you noticing about the data? • What surprised you? • What are the benefits of approaching data in this manner? • What other data would you like to see to better inform the results you’ve seen so far? Wellman, B. & Lipton, L. (2004). Data-driven dialogue. Mira Via, LLC. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

14. Designing Interventions • Assumptions must be unpacked, because our interventions will be based on them. • We must strive to correctly identify the causal factors. • Don’t fall in love with the theory until you have other data. • Use a strength-based approach to interventions. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

15. Data Displays • Create groups of three – appoint a recorder, materials manager, and facilitator. • On the wall is a piece of chart paper and: • AFL – The test questions with results by question • Package with objectives to supplement. • CAT3 – Criterion Referenced Scores • Observe the data silently. Wellman, B. & Lipton, L. (2004). Data-driven dialogue. Mira Via, LLC. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

16. Data Displays • After 5-8 minutes share your observations, questions or comments about the data. • What is unique? Unexpected? • Don’t interpret, just look for what pops out. • Please record your comments and questions on the chart paper provided. Wellman, B. & Lipton, L. (2004). Data-driven dialogue. Mira Via, LLC. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

17. Gallery Tour • Leave a representative behind to answer questions. • Members of other groups may now circulate and look at the other groups’ findings. • Return to your original display. Discuss what you saw on your tour. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

18. Team Action Plan • At your data display, complete the Team Action Plan sheet. • Write one observed strength and one opportunity for improvement in student learning on the post-it notes provided and post them on the chart for your school at the front of the room. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

19. Fishbone Tool • At your table, analyze one strength and one area for improvement. Consider all possible causes. • When you are done, compare and contrast the two diagrams. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

20. Creating a Statementof Success • From your analysis of what is working, develop a statement of success. Write it in the space provided on this worksheet. • What elements from your statement of success might we include in the beginning stages of an action plan? • This process will be continued and expanded upon in the next module. Advancing Assessment Literacy Modules: Data Analysis II (February 2008)

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