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How Do We Match Instructional Effectiveness with Learning Curves?

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Title: How Do We Match Instructional Effectiveness with Learning Curves?
Language: English
Authors: Branum-Martin, Lee, Mehta, Paras D., Taylor, W. Patrick, Carlson, Coleen D., Lei, Xiaoxuan, Hunter, C. Vincent, Francis, David J., Society for Research on Educational Effectiveness (SREE)
Source: Society for Research on Educational Effectiveness. 2015.
Availability: Society for Research on Educational Effectiveness. 2040 Sheridan Road, Evanston, IL 60208. Tel: 202-495-0920; Fax: 202-640-4401; e-mail: inquiries@sree.org; Web site: http://www.sree.org
Peer Reviewed: Y
Page Count: 8
Publication Date: 2015
Document Type: Reports - Research
Education Level: Elementary Education
Descriptors: Instructional Effectiveness, Learning Processes, Multivariate Analysis, Classroom Observation Techniques, Measurement, Cohort Analysis, Research Design, Elementary School Students, Goodness of Fit, Structural Equation Models, Factor Analysis, Longitudinal Studies
Abstract: In order to examine the effectiveness of instruction, the authors confront formidable statistical problems, including multivariate structure of classroom observations, longitudinal dependence of both classroom observations and student outcomes. As the authors begin to examine instruction, classroom observations involve multiple variables for which they need valid measurement models. These classrooms, however, involve students who are not only growing, but typically switching classrooms each year. While measurement models for multiple variables are commonplace as confirmatory factor analysis (CFA), and individual student growth can be modeled under switching classrooms with multilevel analysis software, connecting these two types of models is currently challenging and limited. Consequently, it becomes difficult to jointly examine two types of important substantive questions. First, the authors are interested in the nature of instruction: to what extent can they fit a measurement model which is consistent over time and what might that say about teachers and classrooms with respect to the stability of instructional quality? Second, how might instructional quality relate to student growth, given changing classrooms essentially every year? In order to answer these two substantive questions, the authors can fit a model of instructional observations, a model of student growth, and then join these two models. The authors present an empirical example involving a cohort sequential design of 13,236 students over three years (grades 1-3), nested in 974 classrooms, 762 teachers, in 146 schools. The measurement properties of the classroom observations were strong, with good fit and high validity coefficients (loadings). The instructional factors suggest an increase in quality and homogeneity across grades. Issues for future investigation include missing data, clustering due to teachers, and instructional carryover effects across years. Figures are appended.
Abstractor: ERIC
Number of References: 12
Entry Date: 2015
Accession Number: ED562339
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED562339
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  Data: How Do We Match Instructional Effectiveness with Learning Curves?
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  Data: In order to examine the effectiveness of instruction, the authors confront formidable statistical problems, including multivariate structure of classroom observations, longitudinal dependence of both classroom observations and student outcomes. As the authors begin to examine instruction, classroom observations involve multiple variables for which they need valid measurement models. These classrooms, however, involve students who are not only growing, but typically switching classrooms each year. While measurement models for multiple variables are commonplace as confirmatory factor analysis (CFA), and individual student growth can be modeled under switching classrooms with multilevel analysis software, connecting these two types of models is currently challenging and limited. Consequently, it becomes difficult to jointly examine two types of important substantive questions. First, the authors are interested in the nature of instruction: to what extent can they fit a measurement model which is consistent over time and what might that say about teachers and classrooms with respect to the stability of instructional quality? Second, how might instructional quality relate to student growth, given changing classrooms essentially every year? In order to answer these two substantive questions, the authors can fit a model of instructional observations, a model of student growth, and then join these two models. The authors present an empirical example involving a cohort sequential design of 13,236 students over three years (grades 1-3), nested in 974 classrooms, 762 teachers, in 146 schools. The measurement properties of the classroom observations were strong, with good fit and high validity coefficients (loadings). The instructional factors suggest an increase in quality and homogeneity across grades. Issues for future investigation include missing data, clustering due to teachers, and instructional carryover effects across years. Figures are appended.
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  Data: ED562339
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    Languages:
      – Text: English
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      Pagination:
        PageCount: 8
    Subjects:
      – SubjectFull: Instructional Effectiveness
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Multivariate Analysis
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      – SubjectFull: Classroom Observation Techniques
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      – SubjectFull: Measurement
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      – SubjectFull: Cohort Analysis
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      – SubjectFull: Research Design
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      – SubjectFull: Elementary School Students
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      – SubjectFull: Goodness of Fit
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      – SubjectFull: Structural Equation Models
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      – SubjectFull: Factor Analysis
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      – SubjectFull: Longitudinal Studies
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      – TitleFull: How Do We Match Instructional Effectiveness with Learning Curves?
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