Predicting Proficiency… How MAP Predicts State Test Performance
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The document outlines how Vancouver and Highline Public Schools utilize alignment studies to predict student performance on state tests using the MAP assessment. It details various regression studies, cut scores, and intervention strategies that identify students at risk of not meeting proficiency standards. The findings show the predictive validity of MAP scores, emphasizing the importance of early intervention based on these assessments.
Predicting Proficiency… How MAP Predicts State Test Performance
- 1. How Does MAP Predict State Test Performance? Understanding, Conducting, and Using Alignment Studies June 27, 2012 11:15 am Vancouver Public Schools and Highline Public Schools in Washington State Presenters: Paul Stern Paul.Stern@vansd.org Sarah Johnson Sarah.Johnson@highlineschools.org
- 2. Overview • Background/The Value of Alignment Studies • Highline’s Regression Study • NWEA’s Linking Study • Multi-District Regression Study • Conclusions • Applying the Results
- 3. Learning Objectives • Learn how to define proficiency using MAP cut scores. • Understand the alignment of MAP to Washington’s State Assessments. • Learn how alignment studies can be conducted and used to inform instruction.
- 4. Value of Alignment Studies Researchers align scales for one of two purposes: • Use results from measure “X” to predict the value of a harder-to-observe measure or outcome “Y”. • Use results from measure “X” to predict the value of a future measure or outcome “Y”. In our case, faculty and administration are interested in identifying students who are likely to struggle on future state performance measures. By intervening early, we can target resources to students who may not meet “proficiency”.
- 6. About Vancouver Public Schools • About 22,000 enrolled students • 6 High Schools (4 comprehensive, 1 magnet, 1 alternative) • 18% of students speak a language other than English at home • 49% eligible for free or reduced price lunch • The district serves half of the city of Vancouver, WA (across the river from Portland)
- 7. About Highline Public Schools • About 18,000 enrolled students • 15 High Schools (2 comprehensive, 6 small learning community, 1 magnet, 5 alternative, 1 skills center) • 43% of students speak a language other than English at home - 21% are ELL. • 67% eligible for free or reduced price lunch • The district serves neighborhoods of White Center, Burien, Des Moines, SeaTac and Normandy Park just south of Seattle.
- 8. Washington’s State Assessments • Measures of Student Progress (MSP) is given in grades 3-8 in math and reading. • High School Proficiency Exam (HSPE) is given in grade 10 in math. There is not a 9th grade test. • End of Course Exam (EOC1 and EOC2) given at the end of Algebra and Geometry courses regardless of the student’s grade. (Some middle school students take both the math MSP and an EOC). • The Writing and Science MSP and HSPE were not included in any of the following analyses. • A score of 400 is proficient in Reading & Math/EOC.
- 9. Highline’s Regression Study • In 2007, School and District Administration had been requesting ways to interpret student MAP scores in context of (then) WASL testing. One concern in particular was that students had been above average on the national norms, but yet were not meeting standard on the state assessment. • School staff also requested a way to quickly identify if a student was on track or not.
- 10. Highline’s Regression Study • Decided to do a regression analysis to predict WASL performance. • Ran correlations on multiple variables, and found that “HiMap” (max of last 3 test administrations) had a higher correlation with WASL than a single MAP score. • Weeds out test “bombs” and missing data
- 11. “HIMAP” Variable Defined Fall Winter SPRING HIMAP HIMAP HIMAP 5th Grade 5th Grade 5th Grade
- 12. Highline’s Regression Study • Rather than make a straight out prediction of whether a student will meet/not meet standard, we wanted to emphasize the possible prediction error. • Decided to find a cut on the MAP assessment to predict 400 on WASL, and then generate an error band around that where students would be considered “too close to call” • Used 4 points as a generous estimate of the standard error of the assessment (usually between 3-3.5)
- 13. Intervention Categories: 3 “Bands” • “Above Benchmark” students were those who performed more than 4 RIT points above the cut score. These students are considered on track to meet standard. • “Strategic” students were those who performed within 4 RIT points of the cut. These students are “too close to call” and should receive strategic intervention to meet standard. • “Intensive” students were those who performed more than 4 RIT points below the cut score. These students are unlikely to meet standard without intensive intervention.
- 15. Cuts for Fall, Winter and Spring • When the study was first done in 2008, regression analyses were performed using Spring MAP scores and WASL. • Growth norms were utilized to back track to get cuts for Fall and Winter • Cut scores and ranges were disseminated to teachers and administrators, along with an explanation of the scores. • Excel files for schools began including MAP scores, along with each students’ “BSI Indicator”, color coded in Red, Yellow and Green.
- 18. Predictive Validity • When a student’s indicator is compared to their actual performance: • Approximately 90% of students identified as “Above Benchmark” actually met standard. • Approximately 50% of students identified as “Strategic” actually met standard. • Approximately 10% of students identified as “Intensive” actually met standard. • These were generally true within about 10 percentage points
- 20. 2010 MSP • The analysis was re-run in 2010 following the first year of transition from WASL to MSP. • During the second analysis, regressions were run on each test window individually in each grade level, finding individual cuts, rather than using growth norms. • District budget cuts made high school MAP testing optional, and therefore High School was excluded.
- 25. NWEA’s Linking Study • Most recently updated in Feb, 2011 • Based on a sample of 271 schools in the Spring of 2010 • NWEA uses an Equi-percentile method to equate test results
- 26. Equipercentile Method of Alignment • NWEA used a sample of students from 271 schools taking the 2010 spring assessment in WA. • For each grade and subject, identify the percentage of students in the study sample that met standard. • For each grade and subject, identify the RIT associated with the equivalent percentile from within the study sample. “If 40% of the study population in grade 3 math performed below the proficient level on the state test, we would find the RIT score that would be equivalent to the 40th percentile for the study population”
- 31. Multi-District Regression Study • Included 7 districts including Seattle, Bellingham, Vancouver, Highline, Sumner, Auburn, and Clover Park • Data covered the 2009-10 and 2010-11 academic years • The “cut score” for proficiency was consistent across both years at each grade level, so data from both years was pooled • Overall N of approximately 80,000
- 32. Independent Variables Created • Math Spring RIT (Winter and Fall as well) • Math Spring HIMAP (Winter and Fall as well) • Combined Spring HIMAP (sum of Read & Math) (Winter and Fall as well) • Math Winter HIMAP + Math MSP • Math Fall HIMAP + Math MSP (Comparable variables were also created for Reading)
- 33. Quality of Correlation Best: (Corr: 0.78) • Spring RIT (but no predictive value, so Spring indicators will be ignored) Next Best: (Corr: 0.73-0.75) • Winter RIT • Winter HIMAP + MSP scale score (275-500) • Winter HIMAP Third Best: (Corr: 0.70) • Read Winter HIMAP + Math Winter HIMAP
- 34. Rationale for Selecting Winter HIMAP • Spring MAP test window overlaps MSP/HSPE test window. • Prior Year MSP scores not available for grades 3 and 10. • New students in district are missing MSP scores. • Not all students perform to their best ability on every test. • Many students do not take the Winter MAP.
- 35. Rationale for Selecting Winter HIMAP Winter HIMAP … • Is not very different in the quality of the correlation as compared to other options, • Maximizes the number of students for whom it can be applied, and • Is relatively easy to explain
- 38. Predictive Validity, using Multi-Dist Model Fourth Grade Reading Red circles on students that were Failed MSP Passed MSP Total predicted Predicted Would Fail 3,699 790 4,489 accurately Predicted Would Pass 1,036 7,298 8,334 Blue circle on students that were Total 4,735 8,088 12,823 “under-estimated” Purple circle on Failed MSP Passed MSP Total students that were “over-estimated” Predicted Would Fail 29% 6% Predicted Would Pass 8% 57% Total 100%
- 39. Predictive Validity of Winter Score READING Multiple Districts NWEA Highline Over-Est. Under-Est. Over-Est. Under-Est. Over-Est. Under-Est. Accurate State Perf State Perf Accurate State Perf State Perf Accurate State Perf State Perf Grade 3 85% 7% 8% 84% 10% 6% 84% 10% 6% Grade 4 85% 6% 9% 85% 7% 8% 84% 10% 6% Grade 5 84% 6% 10% 84% 7% 9% 83% 10% 7% Grade 6 84% 7% 9% 84% 10% 7% 82% 12% 6% Grade 7 82% 10% 9% 82% 7% 12% 82% 10% 9% Grade 8 84% 8% 8% 84% 7% 9% 82% 13% 6% Grade 10 86% 4% 10% 85% 9% 6% n/a n/a n/a MATH Multiple Districts NWEA Highline Over-Est. Under-Est. Over-Est. Under-Est. Over-Est. Under-Est. Accurate State Perf State Perf Accurate State Perf State Perf Accurate State Perf State Perf Grade 3 83% 7% 9% 83% 10% 7% 83% 10% 7% Grade 4 84% 7% 10% 83% 12% 5% 84% 10% 6% Grade 5 85% 7% 8% 83% 13% 4% 83% 10% 7% Grade 6 86% 6% 9% 85% 10% 5% 86% 7% 7% Grade 7 85% 5% 10% 86% 9% 6% 84% 11% 4% Grade 8 85% 6% 9% 85% 8% 7% 85% 10% 5%
- 40. Predictive Validity: Percent of Students Meeting Standard by Band Multi- 10%-20% of READING District NWEA Highline “Likely Not Likely Not Proficient 15% 17% 20% Proficient” Students Met At Risk 55% 59% 65% Standard. Likely Proficient 92% 93% 94% 50%-65% of “At Risk Students Met Multi- Standard. MATH District NWEA Highline 90%-95% of Likely Not Proficient 10% 14% 14% “Likely Proficient” At Risk 50% 62% 61% Students Met Likely Proficient 92% 95% 95% Standard.
- 41. Pro and Con of Do-It-Yourself Pro: • Data are based on “our kids” (this is an emotional argument, not a statistical one). • Winter and prior spring estimates can be computed rather than estimated. Con: • It is a lot of work. • Controlling for test windows is complex. • NWEA results are very similar to DIY results. • Teachers who encounter the NWEA linking study will be confused about why our cut points are different. • … and did I say it was a lot of work?
- 42. Vancouver’s Plan to Move Forward • Use NWEA-published linking study to identify cut-point targets in each grade/ testing window. • Identify students as likely to meet standard, at risk, and not likely to meet standard based on their HIMAP RIT for that period and a 4 point band around the NWEA targets. • Estimate winter values based on the mid point between fall and spring. Estimate prior spring equal to subsequent fall value (no summer drop- off).
- 43. Applying the Results: Vancouver • Teacher tables with color coding to identify which students are likely to meet or not meet standard on the MSP/HSPE • Predictions of the number of students the district might expect to meet standard if no changes are made to the pace of student learning during the year. • Maintain a higher priority in the use of MAP to identify individual student learning needs and target instruction (using DesCartes)
- 47. Read Met MSP MSP Math Met MSP MSP Growth Read 2013 2013 Read Growth Math 2013 2013 Math LAST FIRST Target Read Fall Read Read Spring Target Math Fall Math Math Spring ID# NAME NAME Grade 2012 Fall RIT Pctile Categ. Odds Target 2012 Fall RIT Pctile Categ. Odds Target 10944 5 Yes 202 34 46% 207 No 196 12 17% 203 13455 5 No 202 34 46% 207 Yes 208 40 36% 216 13980 5 Yes 215 73 75% 219 No 228 88 81% 235 17713 5 No 217 78 80% 221 No 215 61 52% 223 17716 5 No 192 14 24% 199 Yes 184 3 5% 192 17719 5 No 206 45 54% 211 No 204 29 27% 212 17728 5 Yes 211 61 66% 215 Yes 208 40 36% 216 17732 5 Yes 213 67 70% 217 Yes 208 40 36% 216 17736 5 Yes 216 76 78% 220 Yes 227 87 80% 234 17804 5 Yes 203 36 48% 208 Yes 217 66 56% 224 18312 5 Yes 205 42 52% 210 Yes 201 21 22% 208 18328 5 Yes 212 64 68% 216 Yes 202 24 24% 210 18578 5 No 203 36 48% 208 Yes 201 21 22% 208 18624 5 Yes 216 76 78% 220 Yes 206 34 32% 214 19057 5 No 225 93 89% 228 Yes 212 52 46% 220 19128 5 Yes 217 78 80% 221 Yes 222 78 68% 229 21036 5 No 176 2 5% 186 24125 5 No 215 73 75% 219 Yes 210 46 40% 218 26414 5 No 194 17 27% 201 Yes 209 43 38% 217 27807 5 No 180 4 8% 189 Yes 185 3 5% 193 30737 5 No 205 42 52% 210 No 209 43 38% 217 36075 5 No 201 31 43% 206 No 185 3 5% 193 36376 5 Yes 171 1 3% 182 191 7 9% 199 41166 5 No 184 6 12% 192 No 197 14 18% 204 43584 5 No 230 97 93% 233 Yes 224 82 72% 231 46978 5 Yes 197 22 34% 203 Yes 197 14 18% 204
- 48. How Does MAP Predict State Test Performance? Understanding, Conducting, and Using Alignment Studies Vancouver Public Schools and Highline Public Schools in Washington State Presenters: Paul Stern Paul.Stern@vansd.org Sarah Johnson Sarah.Johnson@highlineschools.org