Leveraging Genetic Algorithms as Anchors in Learning Analytics: A Keynesian Research Method

Potential Abstract: This research article presents a novel approach to enhancing learning analytics through the integration of genetic algorithms as anchor points. Genetic algorithms, inspired by natural selection and genetics, are used to optimize the learning process and personalize educational experiences based on individual student needs. By treating key learning milestones as genetic anchors, this study explores how incorporating genetic algorithms can improve the efficiency and effectiveness of educational interventions. Drawing on Keynesian economic principles, the research method employed in this study focuses on understanding the cyclical nature of learning processes and the impact of external factors on student outcomes. Through a series of experiments and data analyses, we demonstrate the potential of genetic algorithms to adapt to changing student needs and provide personalized learning pathways. This research contributes to the ongoing conversation about the intersection of artificial intelligence, education, and learning analytics, offering insights into how genetic algorithms can be harnessed to optimize educational outcomes in diverse learning environments.

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