NAVIGATING THE FUTURE OF LEARNING: THE ROLE OF MACHINE LEARNING IN SHAPING EDUCATIONAL PATHWAYS

Abstract

This paper offers an extensive survey of the application of Machine Learning (ML) in educational support systems, explaining ML’s role in the personalization revolution of educational experiences, academic advising and career planning. Various models and algorithms of ML are examined for suggesting academic major/specialization, performance prediction, and adaptive learning environments. Both supervised, unsupervised, and reinforcement learning techniques are addressed for the promise and potential limitations of ML within the educational domain. The paper incorporates case studies and publications in which educational support systems are being augmented with ML, making a strong case for ML’s central role in the customization of educational support and pathways. Some hurdles and aspects of ML and Artificial Intelligence (AI) of which educational service systems may be unaware or have not fully appreciated, such as: considerations of data quality of the inputs, privacy implications of released analytics, and the potential for algorithmic bias in the outputs are explained. The paper concludes with speculation of what are likely future research investigations in this area and emphasizes one of the seminal revolutions ML’s exploitation of educational data will be mastered not within the laboratory but in the many arduous venues of application, policy, and interdisciplinary collaboration.