This research article explores the potential of personalized learning analytics to address educational inequities, drawing on the theoretical framework of Keynesian economics to uncover nuanced insights. In an era of rapid technological advancements and increasing concerns about inequities in education, personalized learning has emerged as a promising approach to cater to diverse learner needs. However, there is a need to ensure that personalized learning initiatives are designed and implemented in a manner that promotes equity and mitigates existing disparities. This study investigates the role of learning analytics within personalized learning environments to identify and address inequities, taking into account the multifaceted nature of educational settings and students’ diverse backgrounds.
Drawing on the Keynesian perspective, which emphasizes the role of government intervention to ensure equitable outcomes, this research article delves into the complexities of personalized learning analytics in educational contexts. It explores how key economic concepts such as resource allocation, demand-side policies, and income distribution can be applied to educational settings to enhance the effectiveness of personalized learning analytics. By integrating this theoretical lens, the study aims to provide a more comprehensive understanding of the potential of personalized learning analytics to address inequities and promote equitable educational opportunities for all learners.
Through a comprehensive literature review, this article synthesizes existing research on personalized learning analytics, inequities in education, and the application of Keynesian economics in educational contexts. It critically examines the current state of research and identifies gaps in the literature. Moreover, it proposes a conceptual framework that combines personalized learning analytics and Keynesian economic principles to guide future research and practice in promoting equity in personalized learning environments.
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