Special Education Teacher Shortages – Building the Data Systems and Predictive Models to...

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Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University of New York, College at Cortland Rorie Fitzpatrick, Nevada Department of Education William Lange, Lange Research and Evaluation Sharon Schumacher, Alaska Department of Ed. and Early Development

Transcript of Special Education Teacher Shortages – Building the Data Systems and Predictive Models to...

Page 1: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand

the Scope of the Problem

Edward Caffarella, State University of New York, College at CortlandRorie Fitzpatrick, Nevada Department of Education

William Lange, Lange Research and EvaluationSharon Schumacher, Alaska Department of Ed. and Early Development

Page 2: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Today’s Objectives• Share Nevada’s & Alaska’s Experiences

– Tracking Teacher Attrition– Predicting Supply & Demand

• Students and Teachers• Discuss the Issues, Using Real Data• Share Some Troubling Trends• Contemplate the Impacts and Next Steps

Page 3: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Teacher Attrition in

Nevada

Edward Caffarella, State University of New York, College at Cortland

Rorie Fitzpatrick, Nevada Department of Education

Page 4: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Government Performance and Results Act (GPRA) performance measure #5

• The Statewide number and percentage of highly qualified special education teachers in State-identified professional disciplines consistent with sections 602(a)(10) and 612(a)(14) of IDEA, who remain teaching after the first three years of employment.

• 483 started / 261 remained = 54%

Page 5: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Mentoring New Special Education Teachers

• Clark County (Las Vegas area)

• Washoe County (Reno area)

Page 6: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Mentoring New Special Education Teachers

• Clark County (Las Vegas area)• Lost 16% after 1 year• Lost 30% after 2 years

• Washoe County (Reno area)• Lost 25% after 1 year• Lost 45%% after 2 years

• Statewide• Lost 19% after 1 year• Lost 32% after 2 years

Page 7: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

About the Data Source• Teaching Assignment Database

– Teacher, Class, School, District• Teacher Certification Database

– Teacher, Certifications, Age• Used for Reporting Highly Qualified Teachers

under NCLB• Designed to look within the year• Rotated database to look across multiple years

Page 8: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Actual Data• Using existing data • All teachers since 1997• Repurposed the data• Tracked a Teacher’s Career • Full Population

Page 9: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Nevada Special Education TeachersStarted

in fall

ofTotal

Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 346 77 39 34 29 18 17 87

1999 397 77 39 46 28 28 117

2000 397 81 50 27 33 22 136

2001 373 89 42 47 29 25 108

2002 439 82 63 57 24 37 148

2003 415 76 69 49 24 30 167

2004 473 89 69 45 26 244

2005 483 78 56 88 261

2006 543 104 72 367

2007 452 77 375

2008 498 498

Page 10: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Nevada Special Education TeachersStarted

in fall

ofTotal

Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 346 22% 11% 10% 8% 5% 5% 25%

1999 397 19% 10% 12% 7% 7% 29%

2000 397 20% 13% 7% 8% 6% 34%

2001 373 24% 11% 13% 8% 7% 29%

2002 439 19% 14% 13% 5% 8% 34%

2003 415 18% 17% 12% 6% 7% 40%

2004 473 19% 15% 10% 5% 52%

2005 483 16% 12% 18% 54%

2006 543 19% 13% 68%

2007 452 17% 83%

2008 498 100%

Page 11: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Cumulative % Nevada Special Education TeachersStarted

in fall

ofTotal

Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 346 22% 34% 43% 52% 57% 75% 25%

1999 397 19% 29% 41% 48% 55% 29%

2000 397 20% 33% 40% 48% 54% 34%

2001 373 24% 35% 48% 55% 62% 29%

2002 439 19% 33% 46% 51% 60% 34%

2003 415 18% 35% 47% 53% 60% 40%

2004 473 19% 33% 43% 48% 52%

2005 483 16% 28% 46% 54%

2006 543 19% 32% 68%

2007 452 17% 83%

2008 498 100%

Page 12: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Cum

ulati

ve %

of N

V Sp

Ed

Teac

hers

Re

mai

ning

Tea

chin

g

After X Years 1 5 10Statewide 81% 42% 25%Orthopedic Impairments 67% 67% 67%Adapted Physical Education 79% 60% 58%Speech & Language Impaired 90% 55% 34%Hearing Impaired 88% 51% 29%Mental Retardation 80% 51% 34%Gifted and Talented 83% 49% 19%Visually Impaired 76% 48% 28%Multiple/Diversely Impaired 77% 44% 21%Seriously Emotionally Disturbed 80% 42% 24%Autism 79% 41% 29%Early Childhood Develop Delayed 80% 40% 29%Generalist 79% 39% 19%Specific Learning Disabilities 87% 36% 21%Health Impairments, not Orthopedic 100% 33% 33%American Sign Language 100% 25% 0%

Page 13: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Nevada Regular Education Teachers

Started in

fall of

Total Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 1670 218 143 146 105 95 45 628

1999 1637 215 166 137 88 107 632

2000 1464 182 133 112 85 118 645

2001 1540 203 142 140 82 94 727

2002 1665 242 175 155 88 90 823

2003 1536 218 143 139 88 82 866

2004 2179 303 249 196 123 1308

2005 2380 345 237 205 1593

2006 2602 328 286 1988

2007 2239 328 1911

2008 1254 1254

Page 14: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Nevada Regular Education Teachers

Started in

fall of

Total Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 1670 13% 9% 9% 6% 6% 3% 628

1999 1637 13% 10% 8% 5% 7% 632

2000 1464 12% 9% 8% 6% 8% 645

2001 1540 13% 9% 9% 5% 6% 727

2002 1665 15% 11% 9% 5% 5% 823

2003 1536 14% 9% 9% 6% 5% 866

2004 2179 14% 11% 9% 6% 1308

2005 2380 14% 10% 9% 1593

2006 2602 13% 11% 1988

2007 2239 15% 1911

2008 1254 1254

Page 15: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Cumulative % NV Reg. Ed. Teachers

Started in

fall of

Total Hired

Leftafter

1 year

Left after

2 years

Left after

3 years

Left after

4 years

Left after

5 years

Left after

10 years

Still Teaching

Fall 2008

1998 1670 13% 22% 30% 37% 42% 62% 628

1999 1637 13% 23% 32% 37% 44% 632

2000 1464 12% 22% 29% 35% 43% 645

2001 1540 13% 22% 31% 37% 43% 727

2002 1665 15% 25% 34% 40% 45% 823

2003 1536 14% 24% 33% 38% 44% 866

2004 2179 14% 25% 34% 40% 1308

2005 2380 14% 24% 33% 1593

2006 2602 13% 24% 1988

2007 2239 15% 1911

2008 1254 1254

Page 16: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Cumulative Percentage of Nevada Teachers Remaining Teaching

by Years of Service

After X Years 1 2 3 4 5 10Regular Only 86% 77% 68% 62% 57% 38%Special Only 84% 72% 62% 56% 49% 33%Mixed 100% 95% 92% 87% 83% 65%All Teachers 87% 77% 68% 63% 57% 39%USA Total* 86% 76% 67% 60% 54%

*from National Commission on Teaching and America's Future. (2003). No Dream Denied: A Pledge to America's Children. Washington, DC: NCTAF.

Page 17: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Percentage of NV Teachers by Assignment 1997-2008

Number% of Total

% of Sp Ed

All service regular 34,625 82.2%All service special 5,526 13.1% 73.6%Reg Ed changed to Sp Ed 582 1.4% 7.8%Reg Ed to Sp Ed & back to Reg 351 0.8% 4.7%Sp Ed changed to Reg Ed 742 1.8% 9.9%Sp Ed to Reg & back to Sp Ed 167 0.4% 2.2%Other changing assignment 137 0.3% 1.8%TOTAL 42,130TOTAL any special education 7,505TOTAL mixed 1,979

Page 18: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

41

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0

20

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80

100

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Age of State of Nevada Special Education Teachers Currently Close to Retirement on

Oct. 1, 1997 (green solid), 2003 (yellow dash), & 2008 (light blue dot)

Page 19: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 780.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%Percentage of Nevada Teachers for Each Age

Reg.

Spec.

Page 20: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

Bermuda Triangle

• High Attrition Rates• Large Numbers At or

Approaching Retirement • Difficulty Hiring New Sp Ed

Teachers

Page 21: Special Education Teacher Shortages – Building the Data Systems and Predictive Models to Understand the Scope of the Problem Edward Caffarella, State University.

System Development Considerations

• Use actual data• Sp Ed trends get buried in overall data• Mixed assignments = high retention rates• Losing large numbers of new teachers• Losing more special education teachers

than regular education teachers