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Presented at Washington Educational Research Association (WERA) conference. Presenters: Highline Public Schools and Vancouver Public Schools Sarah Johnson [email protected] Paul Stern [email protected] Presentation Overview: - Background/The Value of Alignment Studies - Highline’s Regression Study - NWEA’s Linking Study - Multi-District Regression Study - Conclusions - Applying the Results
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Predicting Student Performance on the MSP-HSPE
Understanding, Conducting, and Using Alignment Studies
Highline Public Schools andVancouver Public Schools
Presenters:Sarah [email protected] Stern [email protected]
Overview•Background/The Value of Alignment Studies•Highline’s Regression Study•NWEA’s Linking Study•Multi-District Regression Study•Conclusions•Applying the Results
About the MAP Assessments• Computerized• Adaptive assessment – increases in difficulty
when answers are correct and decreases in difficulty when answers are incorrect.
• Rasch Units (RIT) Scale• Equal Interval • Vertical scaling• Has the same meaning regardless of grade or
age of the student. • For the purposes of this presentation, we will be
looking at Reading & Math only.
Value of Alignment StudiesResearchers 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”.
http://kingsburycenter.org/gallery
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•53% eligible for free or reduced price lunch•The district serves half of the city of
Vancouver, WA (across the river from Portland)
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% ELL.
•68% 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.
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.
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
SPRINGHIMAP
5th Grade
WinterHIMAP
5th Grade
FallHIMAP
5th Grade
“HIMAP” Variable Defined
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 predictive 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)
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.
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.
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
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.
2012 MSP•Due to online testing for MSP along with other
district initiatives requiring lab time, our Spring window was moved from May to March beginning in Spring 2012. Also, Winter testing became an optional window.
•Therefore, cuts were created one more time in 2012 for Highline. Again, regressions were done between Fall MAP and MSP, and Spring MAP and MSP. Because Winter was optional, the cuts for Winter were determined using the 2/3 point between the Fall and Spring cuts.
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
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”
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
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)
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
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.
Rationale for Selecting Winter HIMAPWinter 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
Predictive Validity: Percent of Students Meeting Standard by BandREADING
Multi-District NWEA Highline
Likely Not Proficient 15% 17% 20%At Risk 55% 59% 65%Likely Proficient 92% 93% 94%
MATHMulti-District NWEA Highline
Likely Not Proficient 10% 14% 14%At Risk 50% 62% 61%Likely Proficient 92% 95% 95%
10%-20% of “Likely Not Proficient” Students Met Standard.
50%-65% of “At Risk Students Met Standard.
90%-95% of “Likely Proficient” Students Met Standard.
Pro and Con of Do-It-YourselfPro:• 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?
ID#LAST NAME
FIRST NAME Grade
Read Met Growth Target 2012
Read Fall RIT
Read Fall
Pctile
MSP 2013 Read Categ.
MSP 2013 Read Odds
Read Spring Target
Math Met Growth Target 2012
Math Fall RIT
Math Fall
Pctile
MSP 2013 Math
Categ.
MSP 2013 Math Odds
Math Spring 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