Educational Data Collection & ... District A 2012-2013 Graduation Rate (2009 Cohort)...

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Transcript of Educational Data Collection & ... District A 2012-2013 Graduation Rate (2009 Cohort)...

  • Educational Data Collection & Analysis

    Timothy Johnson, Erie 1 BOCES

    Dennis Atkinson, Western New York Regional Information Center

  • DennyTimothy

    Educational Data Collection & Analysis

  • Types of Data Collected

  • 011011000011101010010111001000100101010001010101000010001010111 110101010001000010101000101000100101010101010010101010010101000 100110101010001001010101001001010111010101010000101000101010101 001010101010100010100010010101000101010001000011110010101010100 101010100101010010101010010100100101010100010001001010101010001

    Data Collection Processes

  • Data/Information Available

  • wnyric-mapping.wikispaces.com

    Publicly Available Information, NY Educational Data System

  • 1866 NOVEMBER 1866

    30

    1 2 3

    4 5 6 7 8 9 10

    11 12 13 14 15 16 17

    18 19 20 21 22 23 24

    25 26 27 28 29

  • Types of Data Collected

  • Institutions

    Students Performance

    & Attendance

    Teachers

  • https://upload.wikimedia.org/wikipedia/commons/1/12/Briles_One-room_Schoolhouse.jpg

    Institution Data

  • SEDREF and BEDS

    http://www.p12.nysed.gov/irs/beds/IMF

    http://www.oms.nysed.gov/sedref

  • Teacher Information

  • SIRS and TAA/ePMF

    http://www.p12.nysed.gov/irs/beds/PMF

    http://www.p12.nysed.gov/irs/sirs

  • Teacher & Class Connection Data

  • Student Demographic & Attendance Data

  • Privacy & Data Security

    ersonally dentifiable nformationIP I

  • Privacy & Data Security

    http://www2.ed.gov/policy/gen/guid/fpco/ferpa

  • nysdsp.org

  • Student Demographic & Attendance Data

  • SIRS

    http://www.p12.nysed.gov/irs/sirs

  • http://www.p12.nysed.gov/irs/sirs

  • Student Assessment Data

  • X X X X X X

    X X X X X X

    X X

    ELA

    Math

    Science

    K 1 2 3 4 5 6 7 8

  • X

    X X X

    X X X X

    X X

    ELA

    Math

    Science

    9 10 11 12

    Social

    Studies

  • Scores & Performance Levels

  • Scores & Performance Levels

    100

    400

    300

    200 Level 1

    Level 4

    Level 3

    Level 2

    0

    100

    65

    Level 1

    Level 4

    Level 3

    Level 2

    85

    Level 1

    Level 5

    Level 4

    Level 2

    Level 3

  • Item Response Data

  • Example

  • Living Environment, June 2014, question 1

  • 1 2 3 4

  • http://data.nysed.gov/

    http://data.nysed.gov/

  • Subgroups Include • Students with Disabilities (SWDs) • Limited English Proficiency (ELLs) • Economically Disadvantaged • Gender • Ethnicity • Migrant

  • District A

    • This district is experiencing an increase in poverty and a decrease in enrollment.

    • This district is a first ring suburb of a major urban area of upstate NY

    • The BOCES has a very diverse range of districts in terms of demographics.

  • District A 2014-2015 Graduation Rate (2011 Cohort)

    • District A is an odds beating school.

    • Poverty is 43%

    • Graduation Rate is 91%

    • Number of Students 138

  • District A 2013-2014 Graduation Rate (2010 Cohort)

    • Poverty is 40%

    • Graduation Rate is 85%

    • Number of Students 151

  • District A 2012-2013 Graduation Rate (2009 Cohort)

    • Poverty is 31%

    • Graduation Rate is 87%

    • Number of Students 150

  • 2012-2013

  • 2013-2014

  • 2014-2015

  • District A Financial Need %

    Total %

    Econ Dis %

    Spec Ed %

    Males %

    Females %

    F-M

    2009 31 87 85 55 86 86 0

    2010 40 85 77 68 83 87 4

    2011 43 91 86 71 87 94 7

  • What were the student outcomes for District A?

    Cohort Yr Graduates Non-Graduates Total Cohort Graduation Rate

    2009 132 20 152 87 %

    2010 129 22 151 85 %

    2011 126 12 138 91 %

  • So what is in the Non-graduate category?

    • Dropouts • 20 or more days absent

    • Students who have contacted the school to drop out

    • Still Enrolled – not done yet

    • Transferred to GED or high school equivalency program

  • How could we decrease the number of non- graduates? • Keep track of the students as they move through High School and

    identify early whether they are on track to meeting graduation requirements such as.. • Course credits required for graduation

    • Community service hours

    • Have passed the “Big 5” Regents Examinations

  • Using this data educators can …

    • Identify students who are struggling and match them with instructional programs that can help them.

    • Connect them with a mentor to monitor and encourage students to complete the work.

    • Identify students who have completed courses but have not passed their corresponding exams. These students may be candidates for credit recovery programs.

  • The Story of District A

    • This district did target students who were at risk. After analyzing the trend of having an ever increasing number of students not graduating, they started a program with the 2011 cohort of students and put in place educator mentors and customized credit recovery programs. One reason why they were able to identify the issue was they had a data consultant to find the problem. Not all districts have this luxury.

  • District B – Look at these data and tell me what you see!

  • District B 2012-2013 Graduation Rate

    • 21% Poverty

    • 70% Grad Rate

    • 30 Students

  • District B 2013-2014 Graduation Rate

    • 17% Poverty

    • 77% Grad Rate

    • 43 Students

  • District B 2014-2015 Graduation Rate

    • 22% Poverty

    • 92% Grad Rate

    • 26 Students

  • What can we conclude?

    • This is only the tip of the iceberg. With this session we just focused on Graduation Rate.

    • There is a “huge” amount of data that goes into analyzing what makes a district or school effective or ineffective.

  • Inputs/Givens include…

    • Student background

    • Staff background

    • Parent community characteristics

    • Perceptions, preconceived notions, & expectations

    • Learning styles preferences

    • Teaching styles preferences

    • Core values and beliefs

    • Student learning standards

  • Process/Systems include… • Purpose, Mission, Vision

    • Leadership Policies

    • Curriculum

    • Program Offerings and Access

    • Staffing Assignments

    • Instructional Strategies and Materials

    • Assessment Strategies and Materials

    • Professional Learning, Planning and Collaboration

    • Parent Community Relationships

    • Physical Environment

    • Financial Allocation

  • Outcome/Results include… • Student Achievement Results

    • Student and Teacher Attendance

    • Student Behaviors

    • Student Attitudes

    • Teacher Attitudes

    • Graduation Rates/Dropout Rates

    • Student Careers

    • Student Success in College

    • Parent-Community Attitudes

    • District/School Climate

  • Predictive Analytics

  • predict

    student

    success

    rates

    Student Learning Objectives

  • Is there a

    correlation

    between previous

    test performance

    and future test

    scores

  • 100

    50

    10050 Score Test #1

    Score Test # 2

    78

    74

  • Score Test #1

    Score Test # 2

    100

    50

    10050

    Line of Best Fit

  • Math 8

    Living Env

    Algebra

    Poverty

    LEP

    SpEd

  • Student 2012 Math 7

    (500-800)

    2013 Math 8

    (119-403)

    2014 Algebra I

    (0-100)

    2014 Algebra I

    (0-100)

    Goal Met

    (Y/N)

    student01 94 89 92 student02 81 92 88 student03 65 76 71 student04 91 85 88 student05 95 90 91 student06 100 99 95 student07 93 83 89 student08 58 61 63 student09 90 95 91 student10 71 55 61 student11 85 79 82 student12 99 99 95 student13 82 75 77 student14 85 81 83 student15 91 94 94 student16 98 99 95

    Past Test Scores Projection Final Score

  • Student 2012 Math 7

    (500-800)

    2013 Math 8

    (119-403)

    2014 Algebra I

    (0-100)

    2014 Algebra I

    (0-100)

    Goal Met

    (Y/N)

    studen