| dc.description.abstract | Learning analytics has emerged as a data-driven way to improve learner outcomes over the past decade. However, as the adoption and implementation of learning analytics continues to surge, there are some significant barriers to this process, such as stakeholder buy-in, training, and support, concerns over privacy and ethical issues, the quality of tools provided by third-party vendors, and institutional capacity to collect and meaningfully analyze and interpret data. Poor implementation can increase inequities, squander public funding, foster stakeholder resistance around future initiatives, and ultimately lead to abandonment. Another challenge stems from the need for educators to not only understand a new tool, but the data that goes into and comes out of it. While there has been a growth in research on the adoption process in the higher education context, little has taken place in K-12. The purpose of this dissertation is to investigate key factors that may promote or hinder the adoption of learning analytics in North Texas K-12 schools by leaders. To do so, I explore psychosocial factors of leaders at the campus, district, and educational service center levels as well as how individual and school district capacities influence the decision to adopt learning analytics.
Given the exploratory nature of the study, I used a qualitative approach. My primary data source was semi-structured interviews with leaders in rural, suburban, and urban districts and educational service centers. I chose to investigate leaders over other stakeholders given their role in the adoption process, whereas other groups, such as teachers and students, play a bigger part in later implementation phase. Several key themes emerged from the data. The first theme was knowledge, where leaders’ understanding of learning analytics and large-scale learning data varies significantly. The second was perceptions and attitudes, where leaders are conflicted about the available data that they have and perceive numerous challenges, opportunities, and concerns about the use of learning analytics in a K-12 context. The final theme is capacity. While North Texas school districts in this study have a robust technology infrastructure and mechanisms for adopting new tools, there are discrepancies between small, rural districts and large, suburban and urban districts with regard to their capacity to adopt learning analytics. The findings also indicate that the participants have greater technology literacy than data literacy.
This study has numerous implications for policy, practice, and research. Given the limited nature of the size of the study, additional research needs to take place in order to better develop a broader framework that can guide leaders in the adoption process. This research could further investigate differences between leader characteristics, such as educational background and perceived innovativeness, and district characteristics, such as size and funding. Additional studies could also investigate the relationship between leaders/districts and third-party vendors who offer learning analytics solutions, which are often quite expensive and do not always fit in a certain district’s context. Finally, with the rise of data and technology in K-12 districts, educator preparation needs to include more emphasis on understanding and thinking critically about learning data as a core, 21st-century skill. | |
