What Teachers Want: School Factors Predicting Teachers’ Decisions to Work in Low-Performing Schools

IF 3.5 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Samantha Viano, L. Pham, G. Henry, Adam Kho, R. Zimmer
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引用次数: 17

Abstract

Attracting and retaining teachers can be an important ingredient in improving low-performing schools. In this study, we estimate the expressed preferences for teachers who have worked in low-performing schools in Tennessee. Using adaptive conjoint analysis survey design, we examine three types of school attributes that may influence teachers’ employment decisions: fixed school characteristics, structural features of employment, and malleable school processes. We find that teachers express a strong preference for two malleable school processes, administrative support and discipline enforcement, along with a higher salary, a structural feature. Estimates indicate these attributes are 2 to 3 times more important to teachers than fixed school characteristics like prior achievement. We validate our results using administrative data on teachers’ revealed preferences.
教师想要的:学校因素预测教师在低绩效学校工作的决定
吸引和留住教师是改善低绩效学校的一个重要因素。在这项研究中,我们估计了在田纳西州低绩效学校工作的教师的表达偏好。采用自适应联合分析调查设计,我们研究了可能影响教师就业决策的三种类型的学校属性:固定的学校特征、就业的结构性特征和可塑的学校过程。我们发现,教师对两种可延展的学校流程——行政支持和纪律执行——以及更高的工资(这是一种结构性特征)表达了强烈的偏好。估计表明,对教师来说,这些属性比学校的固定特征(如先前的成绩)重要2到3倍。我们使用教师透露的偏好的行政数据来验证我们的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
American Educational Research Journal
American Educational Research Journal EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
8.70
自引率
0.00%
发文量
19
期刊介绍: The American Educational Research Journal (AERJ) is the flagship journal of the American Educational Research Association, featuring articles that advance the empirical, theoretical, and methodological understanding of education and learning. It publishes original peer-reviewed analyses that span the field of education research across all subfields and disciplines and all levels of analysis. It also encourages submissions across all levels of education throughout the life span and all forms of learning. AERJ welcomes submissions of the highest quality, reflecting a wide range of perspectives, topics, contexts, and methods, including interdisciplinary and multidisciplinary work.
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