This research article investigates the prevalence of algorithmic prejudices within multivoiced educational portals and proposes a framework for their negotiation. As algorithms increasingly shape educational experiences, concerns about their potential biases and discriminatory outcomes have emerged. Despite the potential of multivoiced portals to foster inclusivity and diversity, algorithmic systems may inadvertently perpetuate existing biases and inequities. This study aims to address this gap by examining the algorithmic biases present in educational portals and exploring strategies for their negotiation and mitigation.
Drawing on theoretical perspectives from critical algorithm studies and educational technology, this research employs a mixed-methods approach. Firstly, we conduct a systematic literature review to examine existing research on algorithmic prejudices in educational settings. This review reveals the need for a more nuanced understanding of the specific biases embedded within multivoiced educational portals. Secondly, we conduct a series of case studies to analyze the algorithms deployed in popular portals and assess their impact on diverse student populations. Through this analysis, we identify the potential biases and discriminatory outcomes that result from algorithmic decision-making within educational portals.
Based on our findings, we propose a framework for the negotiation of algorithmic prejudices in multivoiced educational portals. This framework includes three key components: (1) transparent algorithmic design and implementation, (2) user agency and control, and (3) ongoing monitoring and evaluation. We argue that these components are essential for addressing algorithmic prejudices and fostering a more inclusive and equitable educational environment. Moreover, we discuss the ethical considerations associated with algorithmic decision-making and highlight the importance of involving multiple stakeholders, including educators, students, and administrators, in the negotiation and implementation of algorithms.
By exploring algorithmic prejudices in multivoiced educational portals, this research article contributes to the growing body of literature on critical algorithm studies in education. The proposed framework for negotiation provides practical guidance for the development and implementation of algorithms in educational settings. Ultimately, this study aims to inform policymakers, educators, and developers about the potential biases embedded within educational portals and offers strategies for promoting fairness and inclusivity.
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