Improving Student Achievement through Labor-Management Collaboration in Urban School Districts

2003 ◽  
Vol 17 (4) ◽  
pp. 503-518 ◽  
Author(s):  
Adam Urbanski
2016 ◽  
Vol 1 (1) ◽  
pp. 9-16 ◽  
Author(s):  
John Shindler ◽  
Albert Jones ◽  
A. Dee Williams ◽  
Clint Taylor ◽  
Hermenia Cardenas

This study examined the relationship between school climate and student achievement rat- ings in urban school districts in five states (N =230). Many educators view school climate and student achievement as separate considerations. However the results of this study suggest that climate and student achievement were highly related. In fact, the quality of the climate appears to be the single most predictive factor in any school’s capacity to promote student achievement. The findings of the study suggest a se- ries of general and theoretical implication for the field of education. It appears that the use of practices that promote a “psychology of success” lead to greater achievement and higher quality climate, and those that promote a “psychology of failure” lead to under- performance.


Author(s):  
Christopher Harrison ◽  
Kristen Davidson ◽  
Caitlin Farrell

Expectations for the role of research in educational improvement are high. Meeting these expectations requires productive relationships between researchers and practitioners. Few studies, however, have systematically explored the ways researchers can build stronger, more productive relationships with practitioners. This study seeks to identify such strategies by examining district leaders’ views of how researchers might work with practitioners in more effective, beneficial, and collaborative ways. Through an analysis of 147 interviews with 80 district leaders in three urban school districts, we identify several key pieces of advice highlighted by district leaders for researchers. For researchers, these findings reveal potential strategies for shaping the design, conduct, and communication of their research in order to ensure its usefulness for practitioners. 


2018 ◽  
Vol 11 (27) ◽  
pp. 329-344
Author(s):  
Nadine Bonda

Beginning in 2009, and with the passage of the American Recovery and Reinvestment Act of 2009, school districts across the United States began to be held to higher standards and their progress publicly reported.  Student achievement began to be measured by standardized testing and great efforts were being made to reduce the achievement gap. This paper is based on a five-year study of teacher evaluation in two urban districts in Massachusetts where improving teacher practice was seen as an important factor in raising student achievement. This research studied efforts to address those teachers who were identified as underperforming and were supported through individual improvement plans.  This paper used a case study approach to show what the practices of a sampling of these teachers looked like, teachers’ reactions to being rated unsatisfactory, and teachers’ reactions to the improvement planning process.


2007 ◽  
Vol 29 (1) ◽  
pp. 30-59 ◽  
Author(s):  
Howard S. Bloom ◽  
Lashawn Richburg-Hayes ◽  
Alison Rebeck Black

This article examines how controlling statistically for baseline covariates, especially pretests, improves the precision of studies that randomize schools to measure the impacts of educational interventions on student achievement. Empirical findings from five urban school districts indicate that (1) pretests can reduce the number of randomized schools needed for a given level of precision to about half of what would be needed otherwise for elementary schools, one fifth for middle schools, and one tenth for high schools, and (2) school-level pretests are as effective in this regard as student-level pretests. Furthermore, the precision-enhancing power of pretests (3) declines only slightly as the number of years between the pretest and posttests increases; (4) improves only slightly with pretests for more than 1 baseline year; and (5) is substantial, even when the pretest differs from the posttest. The article compares these findings with past research and presents an approach for quantifying their uncertainty.


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