Technical Questions & Current Specifications: Using Predicted District Effectiveness Rates as a Performance Benchmark
The purpose of this paper is to open the methods, construction and assumptions of a new performance benchmark to examination, critique, and improvement by technical experts. The paper begins with known technical questions about this new benchmark. In includes suggestions made by technical advisors....
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| description | The purpose of this paper is to open the methods, construction and assumptions of a new performance benchmark to examination, critique, and improvement by technical experts. The paper begins with known technical questions about this new benchmark. In includes suggestions made by technical advisors. It then details the construction of the benchmark's measures, and reports the relevant statistics. Kansas State Department of Education (KSDE's) new performance measure, the effectiveness rate, measures school districts' effectiveness in enrolling their students in postsecondary education for two continuous years after graduation, or in getting students certified in an industry-recognized skill. Its companion benchmark, the predicted effectiveness rate and the focus of this paper, uses linear regression to describe districts' mean effectiveness rates after adjusting for risk factors largely beyond the districts' control. By comparing districts' actual effectiveness rates to their predicted rates, while accounting for student and environmental risk factors like cumulative poverty, the model assumes that the residuals contain some comparable measure of district performance. |
| format | Report |
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The paper begins with known technical questions about this new benchmark. In includes suggestions made by technical advisors. It then details the construction of the benchmark's measures, and reports the relevant statistics. Kansas State Department of Education (KSDE's) new performance measure, the effectiveness rate, measures school districts' effectiveness in enrolling their students in postsecondary education for two continuous years after graduation, or in getting students certified in an industry-recognized skill. Its companion benchmark, the predicted effectiveness rate and the focus of this paper, uses linear regression to describe districts' mean effectiveness rates after adjusting for risk factors largely beyond the districts' control. 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By comparing districts' actual effectiveness rates to their predicted rates, while accounting for student and environmental risk factors like cumulative poverty, the model assumes that the residuals contain some comparable measure of district performance.</description><subject>Attendance Patterns</subject><subject>Beginning Teachers</subject><subject>Benchmarking</subject><subject>Disabilities</subject><subject>Disadvantaged Schools</subject><subject>Educational Assessment</subject><subject>English Language Learners</subject><subject>Expulsion</subject><subject>Faculty Mobility</subject><subject>Low Achievement</subject><subject>Lunch Programs</subject><subject>Migrants</subject><subject>Performance Based Assessment</subject><subject>Poverty</subject><subject>Predictor Variables</subject><subject>Program Effectiveness</subject><subject>School Districts</subject><subject>School Effectiveness</subject><subject>State Departments of Education</subject><subject>Student Mobility</subject><subject>Suspension</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2017</creationdate><recordtype>report</recordtype><sourceid>GA5</sourceid><recordid>eNqFjD0LwkAQRNNYiPoPLLays5LgR2kSsYwa63Ccc7qYXMLuRfDfe4i9MPAG3jDjZKhgH56taeg0QAN3XmlB2SACH-jSw7KL-it2dFX2dyoFN7YBN8pZg8RKhXOwgV_wUKWzCVAyMVRCXCet8Ra0h7eP1shzmoycaRSzHyfJ_FBU2XGJeFb3wnH0ros83WzX23T1R38A5TBCVA</recordid><startdate>20171020</startdate><enddate>20171020</enddate><creator>Moss, Tony</creator><general>Kansas State Department of Education</general><scope>ERI</scope><scope>GA5</scope></search><sort><creationdate>20171020</creationdate><title>Technical Questions & Current Specifications: Using Predicted District Effectiveness Rates as a Performance Benchmark</title><author>Moss, Tony</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-eric_primary_ED5897953</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Attendance Patterns</topic><topic>Beginning Teachers</topic><topic>Benchmarking</topic><topic>Disabilities</topic><topic>Disadvantaged Schools</topic><topic>Educational Assessment</topic><topic>English Language Learners</topic><topic>Expulsion</topic><topic>Faculty Mobility</topic><topic>Low Achievement</topic><topic>Lunch Programs</topic><topic>Migrants</topic><topic>Performance Based Assessment</topic><topic>Poverty</topic><topic>Predictor Variables</topic><topic>Program Effectiveness</topic><topic>School Districts</topic><topic>School Effectiveness</topic><topic>State Departments of Education</topic><topic>Student Mobility</topic><topic>Suspension</topic><toplevel>online_resources</toplevel><creatorcontrib>Moss, Tony</creatorcontrib><creatorcontrib>Kansas State Department of Education</creatorcontrib><collection>ERIC</collection><collection>ERIC - Full Text Only (Discovery)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Moss, Tony</au><aucorp>Kansas State Department of Education</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><ericid>ED589795</ericid><atitle>Technical Questions & Current Specifications: Using Predicted District Effectiveness Rates as a Performance Benchmark</atitle><jtitle>Kansas State Department of Education</jtitle><date>2017-10-20</date><risdate>2017</risdate><abstract>The purpose of this paper is to open the methods, construction and assumptions of a new performance benchmark to examination, critique, and improvement by technical experts. The paper begins with known technical questions about this new benchmark. In includes suggestions made by technical advisors. It then details the construction of the benchmark's measures, and reports the relevant statistics. Kansas State Department of Education (KSDE's) new performance measure, the effectiveness rate, measures school districts' effectiveness in enrolling their students in postsecondary education for two continuous years after graduation, or in getting students certified in an industry-recognized skill. Its companion benchmark, the predicted effectiveness rate and the focus of this paper, uses linear regression to describe districts' mean effectiveness rates after adjusting for risk factors largely beyond the districts' control. By comparing districts' actual effectiveness rates to their predicted rates, while accounting for student and environmental risk factors like cumulative poverty, the model assumes that the residuals contain some comparable measure of district performance.</abstract><pub>Kansas State Department of Education</pub><tpages>32</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Attendance Patterns Beginning Teachers Benchmarking Disabilities Disadvantaged Schools Educational Assessment English Language Learners Expulsion Faculty Mobility Low Achievement Lunch Programs Migrants Performance Based Assessment Poverty Predictor Variables Program Effectiveness School Districts School Effectiveness State Departments of Education Student Mobility Suspension |
| title | Technical Questions & Current Specifications: Using Predicted District Effectiveness Rates as a Performance Benchmark |
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