3 results on '"Supovitz, Jonathan A"'
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2. Using Integrative Implementation Theory to Understand New Jersey's 2015 Opt-Out Movement
- Author
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Supovitz, Jonathan A.
- Abstract
Background/Context: In the spring of 2015, about 135,000 New Jersey students--almost 20% of the test-eligible children--did not take the state's test. Opposition of this magnitude directly contradicted a central stipulation of the federal No Child Left Behind Act of 2001 (NCLB), which required states to test 95% of their eligible students to receive federal education funding. This article examines what happened in New Jersey in 2015 to produce such a large change in the implementation of state testing policy. Purpose/Objective/Research Question/Focus of Study: In this article, I use Richard Matland's (1995) integrative policy implementation theory to explain the circumstances that produced such a large change in the enactment of New Jersey testing policy in 2015. More specifically, I focus on two research questions: (1) What were the major national, state, and local factors that contributed to parent and student opt-out decisions in New Jersey in 2015? (2) How does integrative implementation theory help us to understand the different circumstances contributing to the opt-out movement in New Jersey in 2015? Population/Participants/Subjects: The article is based upon extensive document review and 33 interviews with New Jersey state policymakers, professional education association representatives, advocacy group leaders, school administrators, teachers, parents, and students. Intervention/Program/Practice: Since the 1990s, testing has become an increasingly important function of state education policy (Fuhrman & Elmore, 2004). High-stakes state testing policies were further expanded under the NCLB, which required states to test 95% of their eligible students to receive streams of federal education funding. The opt-out movement across the nation in 2015 was a major departure from this well-established policy. Research Design: This study employs qualitative interpretive research to examine how multiple actors at different levels of the New Jersey education system understood and interpreted the 2015 opt-out movement. Using Matland's (1995) ambiguity-conflict model of policy implementation I interpret the interactions between the policy design and local implementers' beliefs and goals. Findings/Results: The findings illuminate the shifting national, state, and local factors that contributed to district opt-out rates as well as variation across school levels and districts with different socioeconomic conditions. The combination of increased federal press on states, New Jersey's fast timelines for new standards and assessment adoption even as it ratcheted up accountability, and inconsistent state policy signals all contributed to backlash from teachers, community members, and anti-testing advocates. These factors illuminate many of the changing circumstances in New Jersey that fueled the dramatic opt-out movement in 2015. Conclusions/Recommendations: While Matland's integrative implementation model helps to explain the dynamics of the 2015 New Jersey opt-out movement, it does not account for additional contributing factors including changes over time, dynamics at different system levels, and consideration of a broader range of actors beyond policymakers and policy targets. Incorporating these factors can help make integrative implementation theories even more robust.
- Published
- 2021
3. The Linking Study: An Experiment to Strengthen Teachers' Engagement with Data on Teaching and Learning
- Author
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Consortium for Policy Research in Education and Supovitz, Jonathan
- Abstract
The allure of using data to improve performance is a source of tremendous activity in the education field today. "Data use" has spurred a wide variety of reforms at all different levels of the education system, ranging from infrastructure augmentation to state databases, to district dashboard systems that collect and display an array of indicators, to the formation of school data teams that conduct data-informed inquiries into subgroups of students, to specific formative assessment classroom techniques. From this cornucopia it is increasingly apparent that data use means different things to decision-makers at different levels of the education system, and that the type of data, frequency of the data, mode of inquiry, and decision-making processes look quite different from one another according to role, situation, and purpose (Supovitz & Klein, 2003; others). Thus, when we talk about the term "data use," we must hone in on "for whom?" and "for what purpose?" In this paper, the author is interested in what it means for teachers to fruitfully use data to enhance the teaching and learning process. Informed by research on the challenges teachers face to use data meaningfully, and clues from the rich literature on formative assessment, this paper reports on the design and effects of an intervention designed to help teachers connect data on their teaching with data on the learning of their students for the purpose of informing subsequent instruction which leads to better student outcomes. The hypothesis of this study, therefore, is that while examining data may be useful, the real value of data use is to examine the connection between data points--in this case the instructional choices that teachers make and the learning outcomes of students. Thus, "data use" in this study means encouraging and facilitating teachers' analytical experiences of linking data on teaching to data on the learning of their students. Using a randomized control trial, the Linking Study tests the impacts of the intervention on teachers' perceptions of their fluency with data and their self-reported learning about their instructional practices and their students' thinking. Moreover, the study estimates effects on instruction caused by the intervention, based upon external trained raters' judgments of the quality of instructional practice. Finally, this research examines impacts of the intervention on student outcomes. Appended is: Survey Items and Scale Reliabilities.
- Published
- 2013
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