Four paradigms in learning analytics: Why paradigm convergence matters

作     者：Baker, Ryan S. Gašević, Dragan Karumbaiah, Shamya 

作者机构：Graduate School of Education University of Pennsylvania 3700 Walnut Street PhiladelphiaPA19104 United States Centre for Learning Analytics at Monash Faculty of Information Technology Monash University 20 Exhibition Walk ClaytonVIC3800 Australia Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University 20 Exhibition Walk ClaytonVIC3800 Australia 

出 版 物：《Computers and Education: Artificial Intelligence》 (Comput. Educ.)

年 卷 期：2021年第2卷

核心收录：

主　　题：Data mining 

摘      要：Learning analytics has matured significantly since its early days. The field has rapidly grown in terms of the reputation of its publication venues, established a vibrant community, and has demonstrated an increasing impact on policy and practice. However, the boundaries of the field are still being explored by many researchers in a bid to determine what differentiates a contribution in learning analytics from contributions in related fields, which also center around data in education. In this paper, we propose that instead of emphasizing the examination of differences, a healthy development of the field should focus on collaboration and be informed by the developments in related fields. Specifically, the paper presents a framework for analysis how contemporary fields focused on the study of data in education influence trends in learning analytics. The framework is focused on the methodological paradigms that each of the fields is primarily based on – i.e., essentialist, entatitive/reductionst, ontological/dialectical, and existentialist. The paper uses the proposed framework to analyze how learning analytics (ontological) is being methodologically influenced by recent trends in the fields of educational data mining (entatitive), quantitative ethnography (existentialist), and learning at scale (essentialist). Based on the results of the analysis, this paper identifies gaps in the literature that warrant future research. © 2021 The Author(s)