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An Alternative Method to Rate Teacher Performance

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An Alternative Method to Rate Teacher Performance …

An Alternative Method to Rate Teacher Performance
Patricio A. Rojas, PH.D. Director of Research, Data & Assessment, Los Lunas, NM
Fusion 2012, the NWEA summer conference in Portland, Oregon

This session will provide participants the opportunity to experience an alternative method of rating teachers, under new regulations of New Mexico. This is an updated version of the work presented last year in FUSION 2011. The alternative method is needed because we do not have growth points in the year 2010-2011 in New Mexico.

Learning outcome:
- Learn easy graphs to analyze growth and how to rate teacher performance without using grown points.

Los Lunas is located 35 miles south from Albuquerque, the district has 9,000 students; 17 schools (3 high schools, 2 middle schools, and 12 elementary schools). The district is one of the few nationally accredited districts in the nation. We have been using MAP as short cycle assessment for the last six years. MAP scores are an important piece of data used to rate both schools and teachers.

Audience:
- Experienced data user
- District leadership
- Curriculum and Instruction


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  • 1. An Alternative Method to RateSchool and Teacher Performance By Patricio A. Rojas, Ph.D. Associate Faculty at University of Phoenix Albuquerque, NM Director of Research, Data, and Assessment Los Lunas Schools, NM June 2012
  • 2. BACKGROUND• Director of Research, Data, and Assessment, Department of Curriculum and Instruction, Los Lunas Schools, Los Lunas – NM.• Member of AAAC (Accountability and Assessment Advisory Committee), nominated by the office of the Education Secretary of State.• New Mexico State Coordinator for the Math EAG (Enhancement Assessment Grant) Project, nominated by the Education Secretary of State.• Associate Professor at University of Phoenix, Albuquerque – NM.• Faculty consulting for: – San Jose State University, San Jose, CA – Universidad de Don Bosco, El Salvador. – Universidad Catolica del Norte, Antofagasta, Chile. 2
  • 3. PRELIMINARIESThe legislation for No Child Left behind Actwas proposed by President George W. Bush onJanuary 23, 2001. It was coauthored byRepresentatives Boehner (R-OH), Miller (D-CA), and Senator Gregg (R-NH). The UnitedStates House of Representatives passed thebill on May 23, 2001 (voting 384–45), and theUnited States Senate passed it on June14, 2001 (voting 91–8). President Bush signedit into law on January 8, 2002. 3
  • 4. No Child Left BehindNo Child Left Behind requires all government-runschools receiving federal funding to administer astate-wide standardized test (all students take thesame test under the same conditions) annually toall students. The students scores are used todetermine whether the school/teacher has taughtthe students well. Schools which receive Title Ifunding through the Elementary and SecondaryEducation Act of 1965 must make Adequate YearlyProgress (AYP) in test scores (e.g. each year, its fifthgraders must do better on standardized tests thanthe previous years fifth graders). 4
  • 5. RESEARCH• Research shows that every year fewer schools are making AYP using SBA-Proficiency model. In 2006, 29% of schools in the nation did not make AYP; last year 2011, 35% did not make AYP, and the forecast for 2012, indicates that the percentage of schools not making AYP will be 45%. Source: CEP (Center on Education Policy, Dec 2011)• In 2006, 54% of districts in New Mexico did not make AYP; last year 2011, 80% did not make AYP, and the forecast for 2012, indicates that the percentage of schools not making AYP will be 85%. Source: CEP (Center on Education Policy, Dec 2011) 5
  • 6. NCLB WAIVERS• Obviously, the “attainment proficiency model“ or SBA-Proficiency model is not working at national/state/ district level neither is the growth model, using either SBA or MAP scores.• With the addition of eight new states, so far, we have 19 states on waivers, while another 17 states and the District of Columbia are under review.• States granted waivers are exempt from the laws’ requirement that all students pass achievement tests by 2014 and make progress toward that goal each year. 6
  • 7. Consequences•Last year we proposed an alternative growthmodel based on the concept of students “ontrack to graduation (OTTG)” if the student iseither proficient or makes expected growth.•The state of New Mexico changed the scalescore making each grade ranging X00-X80with the proficiency level at X40, where Xrepresents the grade, for example scale scoresfor grade 5 ranges 500-580 with proficiencylevel set at 540.•This change eliminated the OTTG model wepresented last year. 7
  • 8. More changes• In 2011, the state released a preliminary report named the School Grading Report 2010-2011, giving schools ratings of A-F using a statistical tool not very well explained to the stakeholders. The state is planning to make this report official for the year 2011-2012, while promising a guide for stakeholders to understand and replicate the scores generated by the statistical tool they are using. 8
  • 9. NEW PROPOSALBecause of this dramatic change indata and regulations, this year weare proposing a combination of theattainment proficiency and growthmodels based on trend analysis torate schools and teachersperformance. 9
  • 10. NEW MODEL• The new model is based on 6 trend analysis for the following parameters: – SBA Math Proficiency (SMP) – SBA Reading Proficiency (SRP) – MAP Math Growth (MMG) – MAP Reading Growth (MRG) – MAP Math Proficiency (MPP) – MAP Reading Proficiency (MRP)• Next year we could add two more parameters: – SBA Math Growth (SMG) – SBA Reading Growth (SRG) 10
  • 11. REGRESSION ANALYSIS • We collected information for the six parameters as shown belowPERCENTAGE OF PROFICIENT 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 STUDENTSSCHOOL A MATH 42.0 43.2 43.2 30.7 36.8 41.1 33.5 34.5SBA PROFICIENCY READING 65.6 58.6 63.7 57.2 59.0 46.2 43.3 38.6SCHOOL A MATH 28.0 47.6 55.2 57.4 68.9 79.6 76.1 72.1MAP PROFICIENCY READING 57.0 60.0 65.5 70.3 74.0 79.1 75.1 71.3SCHOOL A MATH 40.2 42.1 43.9 41.3 43.3 39.9 37.8 39.0MAP GROWTH READING 32.0 27.3 26.4 20.9 29.3 36.9 28.8 24.0 • Next we performed a regression analysis in each of these parameters to find the regression line equation. We used the slope of the lines to create a rank by using the average of the slopes computed for each parameter. 11
  • 12. SCHOOL A PERCENTAGE OF STUDENTS SBA READ PROFICIENCY65.6 y = -3.77x + 70.99 63.7 59.0 57.2 58.6 46.2 43.3 38.6 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 12 READING Linear (READING)
  • 13. SCHOOL A PERCENTAGE OF STUDENTS SBA MATH PROFICIENCY 43.2 y = -1.21x + 43.57 43.242.0 41.1 36.8 34.5 33.5 30.7 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 MATH Linear (MATH) 13
  • 14. SCHOOL A PERCENTAGE OF STUDENTS MAP MATH GROWTH y = -0.48x + 43.09 43.9 43.3 42.1 41.340.2 39.9 39.0 37.82004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 14 MATH Linear (MATH)
  • 15. SCHOOL A PERCENTAGE OFSTUDENTS MAP MATH PROFICIENCY 79.6 76.1 72.1 68.9 y = 6.38x + 31.91 55.2 57.4 47.6 28.02004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 15 MATH Linear (MATH)
  • 16. SCHOOL A PERCENTAGE OF STUDENTS MAP READ PROFICIENCY y = 2.62x + 57.24 79.1 75.1 74.0 71.3 70.3 65.5 60.057.02004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 16 READING Linear (READING)
  • 17. SCHOOL A PERCENTAGE OF STUDENTS MAP READ GROWTH y = -0.10x + 28.64 36.932.0 29.3 28.8 27.3 26.4 24.0 20.92004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 17 READING Linear (READING)
  • 18. SCHOOL A SCHOOL A SCHOOL A y = 6.38x + 31.91 SCHOOL A y = 2.62x + 57.24PERCENT OF STUDENTS y = -3.77x + 70.99 PERCENT OFSTUDENTS PERCENT OF STUDENTSSBA READ PROFICIENCY MAP MATH PROFICIENCY MAP READ PROFICIENCY 65.6 63.7 79.676.1 72.1 79.1 57.2 59.0 74.0 75.1 58.6 68.9 55.2 71.3 47.6 57.4 65.5 70.3 46.2 43.3 38.6 60.0 28.0 57.0 READING Linear (READING) MATH Linear (MATH) READING Linear (READING) SCHOOL A SCHOOL A y = -0.48x + 43.09 PERCENT OF STUDENTS y = -1.21x + 43.57 PERCENT OF STUDENTS SCHOOL A y = -0.10x + 28.64SBA MATH PROFICIENCY MAP MATH GROWTH PERCENT OF STUDENTS 43.2 MAP READ GROWTH 42.0 43.2 41.1 43.9 43.3 42.1 36.9 40.2 41.3 39.9 32.0 36.8 39.0 27.3 26.4 29.3 28.8 37.8 24.0 34.5 30.7 33.5 20.9 MATH Linear (MATH) MATH Linear (MATH) READING Linear (READING) This is a view of all six graphs at once. It is hard to make a decision if this school is performing as desired. We need to find a measure to rate the school, we will use the slopes of the regression lines as explained in the next slide 18
  • 19. MEASURE TO RATE SCHOOLS• The following are the six regression lines – SRP y 3.77 x 70.99 – SMP y 1.21x 43.57 – MMG y 0.48x 43.09 – MMP y 6.38x 31.91 – MRP y 2.62x 57.24 – MRG y 0.10x 28.64• We compute the average of the slopes and we called the rank, in this case RANK = 1.41• Now we need to decide a scale to assign a grade. 19
  • 20. MEASURE TO RATE SCHOOLS• This is a tentative scale that requires some more research RANK GRADE 4+ A 2.00 – 3.99 B 0.00 – 1.99 C -1.99 - -0.01 D -2.00 - F• The school in the example with rank = 1.41 will receive a grade C.• Another example is shown on the next slide 20
  • 21. SCHOOL B SCHOOL B y = -3.7381x + 67.121 SCHOOL B y = -2.35x + 54.175 SCHOOL B y = -2.0845x + 64.948PERCENT OF STUDENTS PERCENT OFSTUDENTS PERCENT OF STUDENTSSBA READ PROFICIENCY MAP MATH PROFICIENCY MAP READ PROFICIENCY 67.6 54.2 63.7 62.5 52.3 52.6 60.6 58.6 58.1 53.7 47.2 45.2 46.2 42.1 42.5 56.9 57.2 39.5 35.2 43.3 38.6 30.4 42.3 45.2 READING Linear (READING) MATH Linear (MATH) READING Linear (READING) SCHOOL B y = 0.0342x + 42.478 SCHOOL B y = -0.3617x + 42.42 SCHOOL B y = -0.3617x + 42.42 PERCENT OF STUDENTS PERCENT OF STUDENTS PERCENT OF STUDENTSSBA MATH PROFICIENCY MAP MATH GROWTH MAP READ GROWTH 45.3 43.2 45.3 43.2 50.1 42.0 42.0 41.1 40.3 42.0 41.1 42.0 36.8 43.5 42.1 36.8 40.3 35.6 42.0 43.2 43.544.1 35.6 32.5 MATH Linear (MATH) MATH Linear (MATH) MATH Linear (MATH) RANK = - 1.45 GRADE = D 21
  • 22. RATING TEACHERS• We could use the same methodology and scale to rate teachers, here are a couple of examples.• Teacher A RANK = 2.81 GRADE = B• Teacher B RANK = 4.19 GRADE = A• Teacher C RANK = - 3.52 GRADE = F 22
  • 23. TEACHER A TEACHER A TEACHER A TEACHER ASBA/READ/PROF MAP/READ/GROWTH MAP/MATH/PROF 65.0 84.5 60.4 48.5 45.0 52.6 68.4 52.6 68.4 46.2 65.0 34.7 35.0 36.2 25.3 2009 2010 2011 2012 2009 2010 2011 2012 2013 2008 2009 2010 2011 2012 2013 TEACHER A TEACHER A TEACHER ASBA/MATH/PROF MAP/MATH/GROWTH MAP/READ/PROF 53.9 45.0 44.7 47.4 37.9 42.7 42.1 45.0 35.0 42.3 33.3 32.6 38.1 28.6 26.3 2009 2010 2011 2012 2008 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 RANK = 2.81 – GRADE = B
  • 24. TEACHER B TEACHER B TEACHER B TEACHER BSBA/READ/PROF MAP/READ/GROWTH MAP/READ/PROF 88.8 86.0 88.8 86.9 44.4 75.0 42.1 52.1 52.1 35.6 36.1 45.0 30.0 31.2 2009 2010 2011 2012 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 TEACHER B TEACHER B TEACHER BSBA/MATH/PROF MAP/MATH/GROWTH MAP/MATH/PROF 56.5 61.1 50.8 48.3 54.3 43.5 47.5 47.4 37.5 27.8 25.0 42.4 10.0 10.0 2009 2010 2011 2012 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 RANK = 4.19 - GRADE = A 24
  • 25. TEACHER C TEACHER C TEACHER C TEACHER CSBA/READ/PROF 91.7 66.6 86.6 MAP/READ/GROWTH 76.0 MAP/READ/PROF 80.0 70.0 80.8 64.0 55.6 52.7 45.4 46.0 38.8 38.4 33.2 45.9 28.5 TEACHER C TEACHER C TEACHER CSBA/MATH/PROF MAP/MATH/GROWTH MAP/MATH/PROF 88.0 76.0 77.6 79.2 86.9 79.2 76.0 62.9 74.1 72.2 38.8 38.4 59.3 38.0 57.7 64.0 16.4 RANK = -3.52 - GRADE = F 25
  • 26. EXTRA VALUE OF THE MODEL• Using forecasting methodologies we could design an expected growth/proficiency for both teachers and schools.• For School A the forecasted values are: PERCENTAGE OF PROFICIENT 2011-2012 FORECAST STUDENTS GOALSCHOOL A SBA MATH 44.1 42.8 44.1PROFICIENCY READING 38.6 33.5 38.6SCHOOL A MAP MATH 35.2 33.0 35.2PROFICIENCY READING 45.2 46.2 46.2SCHOOL A MAP MATH 42.0 39.2 42.0GROWTH 35.2 35.9 READING 35.9 26
  • 27. CONCLUSIONS1. The new model presented seems to be 1. Fair 2. Easy to use and replicate 3. Easy to explain to families2. We could easily introduce the concept of “added value” for any other parameter: College readiness, extracurricular activities, sports, etc.3. Any new parameter should be measured by percentage of students, parents, or teachers participating.4. We tested six schools and ten teachers. We requested input from principals and administrators, over 90% agreed with the grades assigned by this model. 27
  • 28. •QUESTIONS? Patricio A. Rojas, Ph.D. projas@llschools.net (505)866-8226 THANK YOU 28

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