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Using Observation Protocol Scores to...

Alzen, Jessica Lynn.

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Using Observation Protocol Scores to Make Inferences About Change in Teacher Practices.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using Observation Protocol Scores to Make Inferences About Change in Teacher Practices./
作者:	Alzen, Jessica Lynn.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	162 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
Contained By:	Dissertation Abstracts International78-10A(E).
標題:	Educational evaluation. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271143
ISBN:	9781369785593

Using Observation Protocol Scores to Make Inferences About Change in Teacher Practices.
Alzen, Jessica Lynn.

Using Observation Protocol Scores to Make Inferences About Change in Teacher Practices. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 162 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.

Thesis (Ph.D.)--University of Colorado at Boulder, 2017.

Research on teacher learning and teacher change indicates that it is not unreasonable to expect teachers at all stages of their careers to change in their practices. However, measures of such change traditionally take the form of self-reflection and observation following teacher preparation programs (Grossman, Valencia, Evans, Thompson, Martin, & Place, 2000) or survey responses following professional development activities (Garet, Porter, Desimone, Birman, & Yoon, 2001). Even though observation protocols historically serve as the method for measuring teacher practices generally, they have yet to be used to understand change in teacher practices over time due to data limitations.

ISBN: 9781369785593Subjects--Topical Terms:

526425
Educational evaluation.

Using Observation Protocol Scores to Make Inferences About Change in Teacher Practices.
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520 $a Research on teacher learning and teacher change indicates that it is not unreasonable to expect teachers at all stages of their careers to change in their practices. However, measures of such change traditionally take the form of self-reflection and observation following teacher preparation programs (Grossman, Valencia, Evans, Thompson, Martin, & Place, 2000) or survey responses following professional development activities (Garet, Porter, Desimone, Birman, & Yoon, 2001). Even though observation protocols historically serve as the method for measuring teacher practices generally, they have yet to be used to understand change in teacher practices over time due to data limitations.
520 $a Recent changes in teacher evaluation systems initiated more frequent and consistent teacher observations (Doherty & Stevens, 2015), so change in teacher practices as measured by observation protocols might soon be of greater interest to researchers and school leaders alike. Fortunately, the Measures of Effective Teaching (MET) project provides sufficient data for beginning to understand longitudinal changes in teacher practices. The two key contributions of this project are first, an application of hierarchical linear models to estimate growth over time in teacher observation scores and second, a careful investigation of the conditions that maximize the reliability of those growth estimates.
520 $a The findings of this study suggest that teacher observation scores may change by about half of a standard deviation during a two-year time span for a few teachers, but most will show much more modest rates of growth. Further, the reliability of the growth parameter estimates can reach as high as 0.5, but the number and spacing of observation occasions as well as number of raters required to reach such levels of reliability may be too high for practical use in some districts.
520 $a The HLM estimates in this study make an initial contribution to the research literature regarding the modeling of growth in observation scores over time. The reliability investigation provides practical information about observation system designs with the potential to yield maximally reliable estimates of growth. The former analysis gives context for future work regarding growth in observation scores while the latter informs decision-makers regarding the best choices in designing observation systems if longitudinal growth estimates are a target measure of interest.
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