Potential Abstract:
The rapid advancements in data science have opened up new possibilities for developing innovative measurement scales in education. This study explores the intersection of data science, neoliberal ideologies, and the concept of commons in educational research. By leveraging big data analytics and machine learning techniques, we aim to design and validate novel scales that capture complex educational constructs more accurately and efficiently. Our research critically examines the implications of neoliberal policies on educational measurement practices and how these practices influence the accessibility and ownership of data as a common resource in the field of education. Through a mixed-methods approach, including quantitative analyses and qualitative interviews with stakeholders, we investigate the potential benefits and challenges associated with implementing data-driven scale development strategies within the current neoliberal landscape. Our findings shed light on the ethical, political, and methodological considerations that researchers and policymakers must address when incorporating data science into the creation of educational scales. By promoting transparency, equity, and collaboration, this study contributes to a more informed and responsible use of data in education research and practice.
Potential References:
- Productive measures: Culture and measurement in the context of everyday neoliberalism
- Measuring neoliberalism: Development and initial validation of a scale of anti-neoliberal attitudes
- The neoliberal subject of value: Measuring human capital in information economies
- Neoliberalism, performance measurement, and the governance of American academic science
- How Neoliberal are You? Development and Validation of the Neoliberal Orientation Questionnaire.
