@inbook{81a434e53b4a49af9af30c4e0797c9b9,
title = "Big Data and Higher Education Research: Toward a Mode III Paradigm",
abstract = "This chapter examines the systematic application of big data in higher education research (HER). It begins by reviewing how big data and its underlying paradigm have reshaped adjacent social science disciplines, including linguistics, sociology, and economics, offering these fields as exemplars of computational integration. The chapter then outlines the implications of big data for HER in four dimensions: theoretical advancement, methodological innovation, tool design and research infrastructure, and the ethical concerns of data use and trustworthiness. By highlighting these dimensions, it emphasizes both the opportunities and challenges that emerge when HER engages with large scale computational approaches. Finally, the chapter offers suggestions for future research, including the development of computational higher education as an interdisciplinary field that bridges data science and education. In doing so, it underscores the transformative potential of big data to enrich theoretical understanding and enhance empirical inquiry in the study of higher education.",
keywords = "Artificial intelligence, Big Data, CV analysis, Computational education, Higher education",
author = "Jin Liu and Haoshen Liang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",
year = "2026",
doi = "10.1007/978-3-032-07781-3\_11",
language = "English",
series = "Palgrave Studies in Global Higher Education",
publisher = "Palgrave Macmillan",
pages = "239--262",
booktitle = "Palgrave Studies in Global Higher Education",
address = "United Kingdom",
}