Large-scale Assessment

Big Ideas in the Understanding of Fractions: A Learning Progression

Anonymous — Wed, 22 Mar 2023 19:23:29 +0000

Big Ideas in the Understanding of Fractions: A Learning Progression Anonymous (not verified) Wed, 03/22/2023 - 13:23

Sarah Wellberg , Derek Briggs , Sandy Student

Link to Resource: Big Ideas in the Understanding of Fractions: A Learning Progression

Authors: Sarah Wellberg, Derek C. Briggs, Sanford R. Student

This report details the development of a learning progression for the understanding of fractions. The intended use of the learning progression is to support formative assessment practices for children as they receive instruction during elementary school. The validity of the learning progression is evaluated by mapping its levels to vertically scaled test items answered by students taking Curriculum Associates' i-Ready Diagnostic Assessment. It is shown that there is a significant moderate correlation between the levels of the learning progression and the difficulty of the test items associated with each level.

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Classroom Assessment Principles to Support Teaching and Learning

Anonymous — Wed, 12 Feb 2020 00:51:34 +0000

Classroom Assessment Principles to Support Teaching and Learning Anonymous (not verified) Tue, 02/11/2020 - 17:51

Lorrie Shepard , Elena Diaz-Bilello , William Penuel , Scott Marion

Link to Resource: Classroom Assessment Principles to Support Teaching and Learning

Authors: Lorrie Shepard, Elena Diaz-Bilello, William Penuel, and Scott Marion

Suggested Citation: Shepard, L. A., Diaz-Bilello, E., Penuel, W. R., & Marion, S. F. (2020). Classroom assessment principles to support teaching and learning. Boulder, CO: Center for Assessment, Design, Research and Evaluation, ��ƷSM��ӰƬ.

This document presents a set of classroom assessment principles intended as a resource for practitioners, especially school leaders and district and state policymakers. Together these principles articulate a shared vision for effective classroom assessment practices. The key ideas guiding this vision come directly from learning sciences research and the research literatures on motivation and assessment. They explain how classroom assessment can best be enacted to support teaching and learning. An initial draft of these classroom assessment principles was developed in preparation for the NCME Special Conference on Classroom Assessment held at the ��ƷSM��ӰƬ, September 18-19, 2019.

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Between-Year Stability of Growth Percentiles : Technical Brief #3

Anonymous — Thu, 16 Jan 2020 23:54:02 +0000

Between-Year Stability of Growth Percentiles : Technical Brief #3 Anonymous (not verified) Thu, 01/16/2020 - 16:54

Benjamin Shear , Elena Diaz-Bilello

Link to Resource: Between-Year Stability of Growth Percentiles : Technical Brief #3

Authors: Benjamin R. Shear and Elena Diaz-Bilello

The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis Unit is to examine the stability of year‐to‐year median growth percentiles by assessment content area and grade levels to help inform the use of Median Growth Percentiles.

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Growth Percentiles, Achievement & Demographics: Technical Brief #1 - 2019

Anonymous — Thu, 16 Jan 2020 22:57:23 +0000

Growth Percentiles, Achievement & Demographics: Technical Brief #1 - 2019 Anonymous (not verified) Thu, 01/16/2020 - 15:57 Benjamin Shear , Elena Diaz-Bilello

Link to Resource: Growth Percentiles, Achievement & Demographics: Technical Brief #1 - 2019

Author: Benjamin R. Shear and Elena Diaz-Bilello

The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis is to address to what extent are school‐level Median Growth Percentiles correlated with current student achievement, prior student achievement, and current school demographic characteristics? How do these correlations compare to the correlation between current average achievement and the same demographic variables?

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Factors Impacting Growth Percentile Variability: Technical Brief #2 - 2019

Anonymous — Thu, 16 Jan 2020 22:51:26 +0000

Factors Impacting Growth Percentile Variability: Technical Brief #2 - 2019 Anonymous (not verified) Thu, 01/16/2020 - 15:51 Benjamin Shear , Elena Diaz-Bilello

Link to Resource: Factors Impacting Growth Percentile Variability: Technical Brief #2 - 2019

Author: Benjamin R. Shear and Elena Diaz-Bilello

The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis Unit is to address the question, compared to achievement, how much of the variability in school Median Growth Percentiles can be explained by school demographics. Additionally, what other factors can help explain the variability found in school MGPs?

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Summary of Median Growth Percentile Analyses: Descriptive Statistics, Demographics Correlations, and Stability Analyses

Anonymous — Thu, 16 Jan 2020 21:08:43 +0000

Summary of Median Growth Percentile Analyses: Descriptive Statistics, Demographics Correlations, and Stability Analyses Anonymous (not verified) Thu, 01/16/2020 - 14:08 Benjamin Shear

Link to Resource: Summary of Median Growth Percentile Analyses: Descriptive Statistics, Demographics Correlations, and Stability Analyses

Authors: Benjamin R. Shear

Citation: Shear, B.R. (2019). Summary of median growth percentile analyses: Descriptive statistics, demographics correlations, and stability analyses. Boulder, CO: The Center for Assessment, Design, Research and Evaluation (CADRE).

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Using Administrative Test-Score Data to Study Educational Opportunity in the US

Anonymous — Mon, 15 Apr 2019 19:49:21 +0000

Using Administrative Test-Score Data to Study Educational Opportunity in the US Anonymous (not verified) Mon, 04/15/2019 - 13:49 Benjamin Shear

Keynote address presented by Benjamin Shear at the St John’s University in Queens, New York at the 7th Annual Leadership Symposium on Mar. 16, 2019.

In this talk Dr. Shear will discuss the Stanford Education Data Archive (SEDA), a publicly available dataset containing information about student achievement for nearly all US public school districts. The talk will briefly describe the origin and features of the data, and will then summarize relevant research studies drawing on the data. The discussion will focus on studies that describe educational opportunity in the US at different geographic scales. The talk will consider both the substantive findings of these studies and the role that a large-scale dataset such as SEDA can play for educational administrators, leaders, and researchers.

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Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data

Anonymous — Mon, 15 Apr 2019 19:29:41 +0000

Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data Anonymous (not verified) Mon, 04/15/2019 - 13:29 Benjamin Shear

Link to Resource: Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data

Authors: Benjamin R. Shear

Citation: Shear, B.R. (2018). Using hierarchical logistic regression to study DIF and DIF variance in multilevel data. Pre-print: Journal of Educational Measurement, v. 55, no. 4, pp. 513 https://onlinelibrary.wiley.com/doi/full/10.1111/jedm.12190

Abstract:

When contextual features of test-taking environments differentially affect item responding for different test-takers and these features vary across test administrations, they may cause differential item functioning (DIF) that varies across test administrations. Because many common DIF detection methods ignore potential DIF variance, this paper proposes the use of random coefficient hierarchical logistic regression (RC-HLR) models to test for both uniform DIF and DIF variance simultaneously. A simulation study and real data analysis are used to demonstrate and evaluate the proposed RC-HLR model. Results show the RC-HLR model can detect uniform DIF and DIF variance more accurately than standard logistic regression DIF models in terms of bias and Type I error rates.

By Benjamin Shear. When contextual features of test-taking environments differentially affect item responding for different test-takers and these features vary across test administrations, they may cause differential item functioning (DIF) that varies across test administrations. Because many common DIF detection methods ignore potential DIF variance, this paper proposes the use of random coefficient hierarchical logistic regression (RC-HLR) models to test for both uniform DIF and DIF variance simultaneously.

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Considerations for Adopting and Implementing Innovative Assessment Systems

Anonymous — Wed, 06 Sep 2017 17:04:08 +0000

Considerations for Adopting and Implementing Innovative Assessment Systems Anonymous (not verified) Wed, 09/06/2017 - 11:04 Benjamin Shear , Elena Diaz-Bilello , Rajendra Chattergoon

Link to Resource: Considerations for Adopting and Implementing Innovative Assessment Systems

Authors: Benjamin Shear, Elena Diaz-Bilello, Rajendra Chattergoon

Suggested citation: Shear, B.R., Diaz-Bilello, E., & Chattergoon, R. (2017). Considerations for adopting and implementing innovative assessment systems. Brief commissioned by the Colorado Department of Education. Boulder, CO: Center for Assessment Design Research and Evaluation (CADRE).

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