Data Science in Education

Abstract

Technology advances have impacted education, giving rise to technology enhanced learning, e-learning, and online learning, including MOOCs, making available rich educational data for research-informed educational decision-making. While data science and learning analytics is being used to draw insights from educational data, the underlying view of education is that the learning experience is regulated in terms of deductively arrived elements of lecture-tutorial-practical in higher education or classroom-lab experience in other educational settings, including corporate environments, with content presumed to be predetermined and largely static. This “decontextualized content teaching” alone seems inadequate to meet the requirements of changing and complex needs of learners, workplaces, and a society that is undergoing rapid digital transformation. The chapter describes a new framework and model, industry linked additive green curriculum using feed-backward instructional design approach, and a shift from Internet of things to Internet of learning things, that can inform data science and learning analytics. The chapter traces the forces that shape educational systems, describes the current view of data science from educational system perspective, including educational data mining and learning analytics, examines the framework and features of smart educational systems proposed in literature, describes relevant socioeconomic and technical challenges in adopting learning analytics in educational systems, and concludes with the proposed new framework.