Yi-Chen Wu, Kristi Liu, Martha Thurlow , & S heryl Lazarus

1. The Characteristics of Non-Proficient Special Education and Non-Special Education Students on Large-Scale Assessments Yi-Chen Wu, Kristi Liu, Martha Thurlow, & Sheryl Lazarus National Center on Educational Outcomes University of Minnesota This paper was developed, in part, with support from the U.S. Department of Education, Office of Special Education Programs grants (#H373X070021). Opinions expressed herein do not necessarily reflect those of the U.S. Department of Education or Offices within it.

2. NCEO Web site(http://www.cehd.umn.edu/nceo/)

3. Outline • Background • Alternate Assessment based on Modified Academic Achievement Standards (AA-MAS) • Questions • Method • Data source • Analytical Techniques • Results • Conclusions

4. AA-MAS • States may count up to 2% of students participating in an AA-MAS for annual yearly progress (AYP). • Students with IEP • AA-MAS is phasing out • on August 23, 2013, the U.S. Department of Education published a proposed rollback of regulation that allowed the AA-MAS (NCEO, 2014). • The assessment may be going away, but struggling learners with disabilities still exist.

5. Candidates for AA-MAS • Students with low performing • belief that low performance on the assessment indicates a need for students to have a different type of assessment in order to demonstrate their knowledge and skills in a content area. • Students below proficiency level • Federal regulations state that eligible AA-MAS participants should be “not proficient” on grade-level content within the year of their IEP

6. Previous study on low performing students • Wu, Lazarus, & Thurlow, 2010 • males, students of color and students from low-income backgrounds, regardless of whether they have a disability=>LP • If low performing students with these demographic characteristics also have a disability, they are much more likely to remain in the bottom 10th percentile across multiple years of the assessment • AA-MAS participants were significantly more likely to be from minority racial or ethnic backgrounds (Shaftel & Rutt, 2012)

7. Is proficiency more reasonable? • Individual states set score cut-points for proficiency in different places, depending on the rigor of the state assessment and related standards. • It may be that the group of non-proficient students with disabilities, as stated in federal regulations, is more representative of the characteristics of the total population in a state.

8. Questions • How does the percent of NP students who receive SPED services compare to the percent of NP Non-SPED students? • How do the demographic characteristics of PNP SPED students compare to the demographic characteristics of PNP Non-SPED students?

9. Method • Data source

10. Method-Definition • Non-Proficient Students • at or below the cut-off score for proficiency • Persistent Non-Proficient Students (PNP) • students who were in the non-proficient group all three years of our analyses. • Demographic variables • Gender • White vs. non-white • Low income (free/reduced lunch)

11. Results—RQ1 • How does the percent of NP students who receive SPED services compare to the percent of NP Non-SPED students?

12. Number of students—Reading 10% SPED 90% Non-SPED

13. Number of students—Math 10% SPED 90% Non-SPED

14. Proportion—NP Reading Non-SPED>SPED Students in SPED are more likely to be NP

15. Proportion—NP Math Non-SPED>SPED Students in SPED are more likely to be NP

16. Proportion—PNP Reading # of NPs No pattern across all 4 states

17. Proportion—PNP Math # of NPs Students in SPED are more likely to be PNP (70 vs. 20; 15 vs.14) More than 60% of NP became PNP in state 2

18. Proportion—PNP • Reading: no pattern found • NP students in SPED were more likely to remain NP in each of the three years compared to their SPED peers for State 4. • Math • NP students in SPED were more likely to remain NP in each of the three years compared to their Non-SPED peers.

19. Results—RQ2 • How do the demographic characteristics of PNP SPED students compare to the demographic characteristics of PNP Non-SPED students?

20. Gender—Reading (State 1) Figure 1. Percentage of State 1’s male and female students in the persistently non-proficient, and total, populations on the state reading assessment by special education status 1a. G5R 1b. G8R

21. Gender—Math (State 1) Figure 2. Percentage of State 1’s male and female students in the persistently non-proficient, and total, populations on the state math assessment by special education status 2b. G8M 2a. G5M

22. Gender—Reading (State 4) Figure 1-1.Percentage of State 4’s male and female students in the persistently non-proficient, and total, populations on the state reading assessment by special education status 1b. G8R 1a. G5R

23. Gender—Math (State 4) Figure 2-1.Percentage of State 4’s male and female students in the persistently non-proficient, and total, populations on the state math assessment by special education status 2b. G8M 2a. G5M

24. Gender—Across states • Similarity • PNP are more likely to be males • More than 50% of SPED population are males • Differences • The difference between SPED and non-SPED is quite different between states • Difference between males and females are not the same (the gap is bigger on state 1, not on state4)

25. Gender—Within a state • Within a state, the pattern is consistent across grades • Most of PNP students who received SPED are more likely to be males • Within a state, the pattern is not consistent across content areas • The gap is smaller on PNP male between SPED and non-SPED on Reading, but the gap is bigger on math • Most of PNP students who did not receive SPED are more likely to be females (True for state 4 math, not for reading).

26. Ethnicity—Reading (State 1) Figure 3.Percentage of State 1’s white and non-white students in the persistently non-proficient, and total, populations on the state reading assessment by special education status 3b. G8R 3a. G5R

27. Ethnicity—Math (State 1) Figure 4.Percentage of State 1’s white and non-white students in the persistently non-proficient, and total, populations on the state Math assessment by special education status 4b. G8M 4a. G5M

28. Ethnicity—Reading (State 4) Figure 3-1.Percentage of State 4’s white and non-white students in the persistently non-proficient, and total, populations on the state reading assessment by special education status 3b. G8R 3a. G5R

29. Ethnicity—Math (State 4) Figure 4-1.Percentage of State 4’s white and non-white students in the persistently non-proficient, and total, populations on the state Math assessment by special education status 4b. G8M 4a. G5M

30. Ethnicity—Across states • Similarity • The proportion of the PNP is NOT similar to the whole population • Differences • The proportion on SPED PNP population is about 50-50 for state 1 across grades and content areas, but not for state 4. • Most PNP students with SPED are more likely to be White (for state 4; state 1 is 50-50) • The difference between SPED and non-SPED is quite different across states (gap is smaller on state 1) • Most of PNP students who receive SPED are more likely to be non-white (True for state 4, not for state 1).

31. Ethnicity—Within a state • The pattern is consistent across grades and content areas for state 1, but not for state 4. • The pattern is not consistent across content areas • The gap between SPED and non-SPED is bigger on Reading than on math across grades for state 4. • The gap between SPED and non-SPED is bigger on Grade 8 than on grade 5 for both content areas.

32. Income Level—Reading (State 3) Figure 5. Percentage of State 3’s low income and non-low income fifth and eighth grades students in the persistently non-proficient and total population on the state reading assessment by special education status 5b. G8R 5a. G5R

33. Income Level—Math (State 3) Figure 6.Percentage of State 3’s low income and non-low income fifth and eighth grades students in the persistently non-proficient and total population on the state math assessment by special education status 6b. G8M 6a. G5M

34. Income Level—Reading (State 4) Figure 5-1.Percentage of State 4’s low income and non-low income fifth and eighth grades students in the persistently non-proficient and total population on the state reading assessment by special education status 5b. G8R 5a. G5R

35. Income Level—Math (State 4) Figure 6.Percentage of State 4’s low income and non-low income fifth and eighth grades students in the persistently non-proficient and total population on the state math assessment by special education status 6b. G8M 6a. G5M

36. Low Income—Across states • Similarities • The proportion of the PNP is different from the whole population • Most PNP students are more likely to be from low income regardless the disability status • Differences • The difference between SPED and non-SPED is quite different across states (the gap in grade 5 is bigger than grade 8 for state 1; However, the gap is bigger in grade 8 than grade 5 for state 4.)

37. Low Income—Within a state • Within a state, the pattern is consistent across grades and content areas for state 1, but not for state 4. • Within a state, the pattern is not consistent across content areas • The gap between SPED and non-SPED is bigger on Reading than on Math across grades for state 4. • The gap between SPED and non-SPED is bigger on Grade 8 than on grade 5 for both content areas.

38. Conclusion • Not exactly same as the findings in Wu et al.’s low performing study (Wu et al, 2012). • The possible reason might be due to the cut score for the proficiency level is quite different among states. • Even though some of the characteristics were similar across states (e.g., low-income level), the differences between the SPED and Non-SPED population were not the same across states. • Not the same pattern across the two content areas of reading and math.

39. Conclusions • There were some similarities in the characteristics of PNP students, such as male, non-white and low-income. • The percentages of PNP students for one state’s content assessments were stable for SPED and non-SPED populations in one of the characteristics, but the same was not the case for other states. • For example, in state 1, on the math assessment there were different patterns for gender and for race/ethnicity. • There were relatively stable percentages of male versus female students in the PNP SPED and Non-SPED groups compared to the total population tested.

40. Final Comments • AA-MAS is going away, but these students are not going away • The results provide important information • about a group of kids who will be in the next generation assessments • it is important to continue to analyze data to see how this population is doing over time.