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Modeling the effects of class size r...

Davis, Jeffrey C.

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Modeling the effects of class size reduction on student achievement.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling the effects of class size reduction on student achievement./
作者:	Davis, Jeffrey C.
面頁冊數:	143 p.
附註:	Adviser: Kathy E. Green.
Contained By:	Dissertation Abstracts International68-08A.
標題:	Education, Tests and Measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3278553
ISBN:	9780549201038

Modeling the effects of class size reduction on student achievement.
Davis, Jeffrey C.

Modeling the effects of class size reduction on student achievement. - 143 p.

Adviser: Kathy E. Green.

Thesis (Ph.D.)--University of Denver, 2007.

This dissertation studied whether an intervention to reduce class size had an effect on student achievement. Using data from a school district ( n = 1,502), the study used a quasi-experimental approach in an attempt to isolate the effects from the class size intervention, i.e., the difference between a child's academic performance with the intervention and the performance that would have been expected if the child had an identical context but no intervention. This required including non-intervention variables in the statistical model---teacher characteristics, student demographics, and previous test scores---that could possibly influence the academic performance outcomes. It also required using appropriate statistical techniques, i.e., hierarchical linear modeling, that could take into consideration the nested data structures at the student and classroom levels.

ISBN: 9780549201038Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

Modeling the effects of class size reduction on student achievement.
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245 1 0 $a Modeling the effects of class size reduction on student achievement.
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502 $a Thesis (Ph.D.)--University of Denver, 2007.
520 $a This dissertation studied whether an intervention to reduce class size had an effect on student achievement. Using data from a school district ( n = 1,502), the study used a quasi-experimental approach in an attempt to isolate the effects from the class size intervention, i.e., the difference between a child's academic performance with the intervention and the performance that would have been expected if the child had an identical context but no intervention. This required including non-intervention variables in the statistical model---teacher characteristics, student demographics, and previous test scores---that could possibly influence the academic performance outcomes. It also required using appropriate statistical techniques, i.e., hierarchical linear modeling, that could take into consideration the nested data structures at the student and classroom levels.
520 $a Contrary to most of the previously published research findings, this study found no positive effects of the class size intervention. The results were consistent on different outcome measures, whether in different subject areas (reading, language, and mathematics) or different types of tests (norm- and criterion-referenced). In some of the analyses, the intervention even showed negative effects. Again, contrary to the hypotheses, there were no differential effects for subgroups of students, namely those of different ethnic/racial groups or socioeconomic status. Finally, the number of years that students-participated in the class size program did not have a significant effect on student outcomes.
520 $a Several reasons for the results were proposed. In the statistical modeling, there may have been a lack of precision in the measurements of the predictor variables (covariates). In the implementation of the intervention, the district did not reduce class size to the extent recommended in prior research, most of the student participants did not start in the program until grade 3, and the district did not concentrate enough on teacher professional development and support.
520 $a Recommendations for further research included devoting more attention to professional development in teaching smaller classes and using advanced features of hierarchical linear modeling to examine growth curves. A final recommendation was to devote more attention to best research evidence when designing programs, including building in strong evaluation models at the outset.
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