Potential Abstract:
As educational systems strive to become more inclusive and equitable, the integration of data science practices has emerged as a promising approach. This study investigates the extent to which data science practices are mediated and contested within the educational milieu, with a focus on equity considerations. By examining the ways in which data science is applied and understood in educational contexts, this research aims to shed light on the unexplored dimensions of data-driven decision-making and its potential impact on educational equities.
This qualitative research study employs a multi-case design to examine three educational institutions that have implemented data science practices. Drawing on critical theory and socio-cultural perspectives, we analyze the practices, beliefs, and experiences of educators, administrators, and students to uncover the contested nature of data science in educational settings. The research utilizes a combination of interviews, observations, and document analysis to gather rich and nuanced data.
Preliminary findings reveal that the integration of data science practices in education is not a straightforward process but is shaped by various factors, including power dynamics, institutional structures, and socio-cultural contexts. The contested nature of data science practices manifests in multiple ways, such as varying interpretations of data, unequal access to resources, and differential impacts on marginalized communities. These findings highlight the importance of critically examining the use of data science in education and addressing potential inequities that may arise from its implementation.
The implications of this research are twofold. Firstly, it contributes to the theoretical understanding of data science practices in educational contexts by uncovering the complexities and contestations surrounding their integration. Secondly, it informs educational policymakers and practitioners about the potential equity implications of data-driven decision-making. By identifying the challenges and opportunities associated with data science practices, this research aims to stimulate discussions and guide the development of ethical and equitable data practices in education.
Potential References:
- UTILIZING DATA ANALYTICS TO INCORPORATE RACIAL EQUITY INTO STEM FACULTY DEVELOPMENT
- Visualizing Equity: A Data Science for Social Good Tool and Model for Seattle
- Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management
- A call for a humanistic stance toward K–12 data science education
- Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities matter?
