Leveraging Democratic Constructs in Educational Data Mining: An Artificial Framework

Potential Abstract:
Educational data mining (EDM) has emerged as a powerful tool for extracting valuable insights from educational data to inform decision-making processes. However, the application of EDM in educational settings raises important questions about the ethical implications and potential biases that may be embedded in the algorithms used. In this study, we propose a novel framework that leverages democratic constructs to enhance the transparency, fairness, and accountability of EDM models in education.

Drawing on principles of democratic theory and artificial intelligence, our framework integrates participatory design processes, stakeholder engagement, and algorithmic transparency mechanisms to promote a more inclusive and equitable approach to data-driven decision-making in education. By involving a diverse range of stakeholders, including teachers, students, parents, and policymakers, in the design and evaluation of EDM models, we aim to address issues of power asymmetries and promote democratic values such as transparency, equity, and social responsibility.

Through a series of case studies and simulations, we demonstrate the potential of our framework to uncover and mitigate biases in educational data, improve predictive accuracy, and enhance the interpretability of machine learning models in educational contexts. Our findings highlight the importance of incorporating democratic principles into the design and implementation of EDM systems to ensure that they align with the values and goals of a democratic society.

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