Dissertation/Thesis Abstract

Teacher education programs and data driven decision making: Are we preparing our future teachers to be data and assessment literate?
by Killion, Jenniffer Michelle, Ed.D., University of Southern California, 2009, 186; 3355456
Abstract (Summary)

Accountability and standards-based reform are buzz words in educational settings today. Much of this is due to the passage of the No Child Left Behind Act of 2001. As a result of this increased accountability, school districts, administrators, and teachers are utilizing data to drive instructional decisions and practices. This focus on data driven decision making has had a far-reaching impact, from the top level of the federal government who essentially mandated data-use, down to the classroom where students learn every day.

Because data driven decision making is so widely used, it is expected that teachers have quantitative knowledge and understand how to use the data in their classrooms. However, there appears to be a gap in the knowledge of our teachers where assessment and data literacy are concerned. Experts cite a lack of training in sound assessment practices as a major problem in schools today, and many are looking to teacher preparation programs to help close this gap.

This research study looked at three university teacher preparation programs in order to find out what they are doing to prepare teachers for data and assessment practices in schools. This study was qualitative in nature, and relied on interviews with teacher educators, a focus group, and student surveys for data collection purposes. The goal was to add new knowledge and begin a research base on data literacy in teacher preparation programs.

Findings in this study show that teacher preparation programs have a greater focus on teaching assessment within coursework, particularly within reading methods courses. Data collection is also a focus, especially with regards to student demographic data. This study also shows that teacher preparation programs teach about differentiated instruction for special student populations, including English Language Learners, Gifted and Talented students, and special needs students. However, utilizing data to differentiate instruction appears to be an area that needs greater focus.

Indexing (document details)
Advisor: Datnow, Amanda
Commitee: Ragusa, Gisele, Stillman, Jamy
School: University of Southern California
Department: Education
School Location: United States -- California
Source: DAI-A 70/05, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Teacher education
Keywords: Assessment, Data, Data-driven decision-making, Differentiation, Preservice teachers, Teacher preparation
Publication Number: 3355456
ISBN: 9781109140552
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