Uncovering Causal Stereotypes: A Transformative Approach to Open Data as a Way of Knowing in Education

Potential Abstract:
In education, the perpetuation of stereotypes based on race, gender, and socio-economic status has long been a critical issue. These stereotypes not only influence the way students are perceived and treated within educational environments but also shape the opportunities and outcomes they experience. Utilizing open data to explore the causal relationships underlying these stereotypes presents a transformative way of knowing that can challenge existing narratives and inform more equitable educational practices. This research article examines the potential of open data to uncover causal relationships between stereotypes and educational outcomes, shedding light on the mechanisms through which stereotypes impact students’ experiences. By utilizing advanced statistical techniques and machine learning algorithms, this study aims to identify hidden patterns and connections within large datasets that traditional research methods may overlook. The findings from this research have the potential to inform policy decisions, interventions, and educational practices aimed at addressing and dismantling harmful stereotypes in education. By leveraging open data as a way of knowing, this research contributes to a deeper understanding of the complex interactions between stereotypes, educational environments, and student outcomes.

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