A Model for Predicting Student Performance on High-Stakes Assessment

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Autor/in	Dammann, Matthew Walter
Titel	A Model for Predicting Student Performance on High-Stakes Assessment
Quelle	(2010), (115 Seiten) PDF als Volltext Verfügbarkeit Ed.D. Dissertation, The Johns Hopkins University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3037-6831-6
Schlagwörter	Hochschulschrift; Dissertation; Models; Prediction; Academic Achievement; High Stakes Tests; Student Evaluation; Reading Tests; Mathematics Tests; Grade 5; Grade 8; Elementary School Students; Middle School Students; Science Achievement; Mathematics Achievement; Integrated Curriculum; State Standards; National Standards; Accountability; Educational Legislation; Federal Legislation; Reading Skills; Cost Effectiveness + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Analogiemodell; Vorhersage; Schulleistung; Schulnote; Studentische Bewertung; Lesetest; School year 05; 5. Schuljahr; Schuljahr 05; School year 08; 8. Schuljahr; Schuljahr 08; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Verantwortung; Bildungsrecht; Schulgesetz; Bundesrecht; Reading skill; Lesefertigkeit; Kosten-Nutzen-Analyse; Kosten-Nutzen-Denken
Abstract	This research study examined the use of student achievement on reading and math state assessments to predict success on the science state assessment. Multiple regression analysis was utilized to test the prediction for all students in grades 5 and 8 in a mid-Atlantic state. The prediction model developed from the analysis explored the combined impact of student achievement in reading and math with student characteristics of race, gender, and SES status on student performance in science. Predicted science assessment scores were then compared with actual science assessment scores. The results confirmed that reading and mathematics achievement were significant predictors of science achievement on standards-based state assessments. Additionally, reading achievement was the strongest significant individual predictor of student achievement in science. Results also indicated that the combined variables of reading and math performance with student characteristics of race, gender and SES were significant predictors of student performance on state science assessments. A comparison of predicted science scores and actual science scores revealed no significant differences between the scores. The results of this research have implications for curriculum and instruction as well as addressing the accountability mandates of NCLB. The results of this study contribute to the body of research related to prediction models, integrated curriculum, national standards and assessment movement, and meeting the accountability requirements of NCLB. Based on the results of this study, it was concluded that reading and math performance could predict science achievement. Reading skill knowledge seems to be the key to academic achievement in other academic areas. Instructionally, curriculum that integrates reading and math skills with other content area knowledge would benefit students. The use of the prediction model could reduce the cost of implementing NCLB accountability systems by eliminating the science component. This would only be applicable to the study state. The prediction model developed in this study and the results do not apply to states that employ different standards and assessments. However, states with standardized assessments could replicate the study utilizing their individual accountability systems. Additionally, the accountability mandates of NCLB prohibit comparisons of students' performance across states. An alternative approach would be consideration of national standards and assessments that would allow valid comparisons of students' learning and achievement based on consistent standards and standardized assessments to measure those standards. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01