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Using Common Assessment Data to Predict High Stakes Performance:

Using Common Assessment Data to Predict High Stakes Performance:. An Efficient Teacher-Referent Process. Bethany Silver Colleen Palmer Frances DiFiore Capitol Region Education Council Hartford, Connecticut. Purpose .

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Using Common Assessment Data to Predict High Stakes Performance:

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  1. Using Common Assessment Data to Predict High Stakes Performance: An Efficient Teacher-Referent Process Bethany Silver Colleen Palmer Frances DiFiore Capitol Region Education Council Hartford, Connecticut

  2. Purpose School districts across the nation routinely commit resources and instructional time to the creation, implementation, scoring and data entry of locally constructed assessments. This paper describes the process employed by a small Northeastern school district to examine the relationship between student performance on common district benchmark assessments and high stakes achievement.

  3. Research • Common Assessments • Reeves (2000, 2002a, 2002b) • Ainsworth and Viegut (2006) • Functions of a Test • Thomas (2005) “present day high-stakes programs are also often used for predicting pupils’ future academic success” (p.81) • Teacher perceptions • DuPaul, Volpe, Jitendra, Lutz, Lorah & Gruber (2004)

  4. Research Questions The work by DuPaul, Volpe, Jitendra, Lutz, Lorah & Gruber (2004) found teacher perceptions of academic skills to be the strongest predictor of achievement test scores. Thomas (2005) philosophically reinforces this. Research Question 1: How well are the locally constructed district-wide common assessment scores related to high stakes assessment performances? Research Question 2: What processes can we use to make more efficient use of district-wide common assessment data in understanding the relationship between common assessment scores and high stakes assessment scores?

  5. Methodology, Question 1 Research Question 1: How well are the locally constructed district-wide common assessment scores related to high stakes assessment performances? Sample Drawn from a school district with approximately 3400 urban and suburban learners in eight magnet schools. 61% of the students are minority, non-white 27% qualify for free or reduced price lunches Existing data from approximately 1200 grade three through eight students enrolled in five magnet schools were used for this research.

  6. Methods, Cont. Assessment Tools • Existing data sets for 1200 students, grades 3-8: • Local common assessment data from 2005-6 school year • Two Instances: Fall and Mid-Year (January) • Math Assessment • Language Arts Assessment • One or Two instances of Degrees of Reading Power • High stakes data from Spring 2006 administration

  7. Findings: Math

  8. Findings: Reading Comprehension

  9. Findings: Degrees of Reading Power

  10. Methodology, Question 2 Research Question 2: What processes can we use to make more efficient use of district-wide common assessment data in understanding the relationship between common assessment scores and high stakes assessment scores?

  11. Process Steps 1, 2, and 3 Mathematics Scale Score

  12. Step 4: Teacher Validation Reports

  13. How was this Process Meaningful?

  14. Discussion RQ1:Correlation as just the beginning - Instructional Benefit - Blue Print Alignment RQ2: Prediction and Teacher Validation - Cultural Adoption of a systematic accountability process

  15. Resources • Ainsworth, L. & Viegut, D.J. (2006) Common Formative Assessments: How to connect standards-based instruction and assessment. Thousand Oaks, California: Corwin Press, Inc. • Bureau of Student Assessment, Connecticut State Department of Education, (2007). CMT-4 technical bulletin, Calculation of scale scores for the 2007 CMT-4 administration (Form P’). Retrieved January 3, 2007, from http://www.csde.state.ct.us/public/cedar/assessment/cmt/resources/misc_cmt/cmt_technical_bulletin_2007.pdf • DuPaul, G.J., Volpe, R.J., Jitendra, A.K., Lutz, J.G., Lorah, K.S., & Gruber, R. (2004) Elementary school students with AD/HD: predictors of academic achievement. Journal of School Psychology, 42, 285-301. • Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahway, NJ: Lawrence Erlbaum. • Reeves, D. B. (2000). Accountability in action: A blueprint for learning organizations. Denver, Colorado: Advanced Learning Press. • Reeves, D. B. (2002a). Holisticaccountability: Serving students, schools, and community. Thousand Oaks, California: Corwin Press, Inc. • Reeves, D. B. (2002b). The leader's guide to standards. San Francisco: Jossey-Bass. • Schmoker, M (1999). Results: The key to continuous school improvement, 2nd edition. Alexandria, VA: Association for Supervision and Curriculum Development.  • Stiggins, R.J. (2005). Student Involved Assessment for Learning, 4th ed. Upper Saddle River, NJ: Merrill/Prentice Hall. • Thomas, R.M. (2005) High Stakes Assessment: Coping with collateral damage. Mahway, NJ: Lawrence Erlbaum. • Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics, 5th ed. Boston: Allyn and Bacon.

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