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Graduate Defense: Jamie Vick
September 16 @ 9:00 am - 11:00 am MDT
Dissertation Information
Title: Elementary Teachers’ Self-Efficacy And Anxiety Related To Data-Based Decision Making
Program: Doctor of Education in Educational Technology
Advisor: Dr. Lida Uribe-Flórez, Educational Technology
Committee Members: Dr. Jesús Trespalacios, Educational Technology and Dr. Yu-hui Ching, Educational Technology
Abstract
Data-based decision-making (DBDM) is an evidence-based practice that K-12 educators can use to gather, analyze, and act on various available data. When used effectively, this data can help guide and inform decisions to support students in achieving learning goals and improving overall student achievement. Teachers’ beliefs about data use directly influence their decisions to use data. This study aimed to investigate the beliefs of K-6 teachers regarding DBDM and how their self-efficacy and anxiety influence their perceptions of using DBDM. The study utilized the Self-Efficacy Theory as its theoretical framework when exploring those beliefs. The research followed an explanatory sequential mixed-methods design, with a survey administered and one-on-one interviews conducted with teachers to help understand the quantitative results. In this study, teachers reported medium levels of efficacy and anxiety regarding DBDM. However, for some specific DBDM tasks, they felt higher levels of anxiety and lower levels of efficacy. The study found that teachers’ perceptions of efficacy and anxiety when accessing and using data should not be grouped, as they are distinct. Four main themes emerged from the data analysis. These themes were Data Dashboard Navigation, Data, Connecting Data to Practice, and Frustration. The first three themes were factors that can potentially impact teachers’ efficacy and anxiety with DBDM and provided insight into K-6 teachers’ data use practices, beliefs, affective states, and barriers to using data in the classroom. The final theme, Frustration, found that teachers’ efficacy, anxiety, and perceptions may be influenced by their frustration with different DBDM tasks.
