This research article introduces a novel approach to investigating the impact of racism through the lens of naive behavioral economics experiments. Racism remains a pervasive and pressing issue in education, and exploring its effects on students’ decision-making and academic outcomes is crucial for developing effective interventions. While previous research has shed light on the various manifestations of racism and its detrimental consequences, the use of naive behavioral economics experiments offers a fresh perspective that allows for a deeper understanding of the underlying mechanisms.
By employing a mixed-methods design, this study aims to unpack the ways in which racism influences individuals’ decision-making processes in educational contexts. We propose to conduct a series of experiments that simulate typical scenarios encountered by students in their academic journeys. These experiments will involve various racial contexts, allowing for a nuanced analysis of how racial biases impact decision-making and academic performance.
The study will involve a diverse sample of students from different racial backgrounds to ensure the generalizability of findings. Participants will be presented with scenarios that mirror real-life educational situations, such as college admissions, grading policies, and classroom dynamics. Through carefully designed experimental manipulations, we will assess how participants’ decision-making is influenced by racial biases, both implicitly and explicitly. Quantitative data collected during the experiments will be analyzed using advanced statistical techniques, including regression and structural equation modeling, to uncover the complex relationships between racism, decision-making, and academic outcomes.
Complementing the quantitative analysis, qualitative data will be collected through stimulated recall interviews to capture participants’ thought processes and subjective experiences. This will provide valuable insights into the mechanisms underlying the observed effects of racism on decision-making. Thematic analysis will be employed to identify recurring patterns and themes, enriching our understanding of the complex interplay between racism, decision-making, and academic outcomes.
This study’s innovative approach bridges the gap between artificial intelligence and education research, providing a fresh perspective on the impact of racism in educational settings. Findings will contribute to the development of evidence-based interventions aimed at mitigating the negative effects of racism on students’ decision-making and academic success.
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