Reviewing the differences between learning analytics and educational data mining: Towards educational data science

Abstract

Over the last decade, Educational Data Mining (EDM) and Learning Analytics (LA) have evolved enormously as interrelated research areas and disciplines. Many researchers interested in these areas may wonder why there are two different communities, whether they are the same concept or not, and the differences between them, which is key information for designing their research and publication strategies. To address this, we conducted a systematic review of academic papers about the differences between LA and EDM following the Preferred Reporting Method for Systematic Reviews (PRISMA) guidelines. We selected 10 research works and identified 11 differences. Our conclusions are that, although both use the same data and share similar goals and interests, EDM and LA are different research communities with different origins and focuses, with their respective conferences and journals. However, there is active collaboration between the two communities and their members often tend to publish in both fields’ conferences and journals. Additionally, none of the differences are apparently large enough to conclude that LA and EDM follow different paths for improving the teaching-learning process, but rather the opposite. Following a common future line, it seems that the two “sister” communities are working together with the same perspective, along with some “cousin” communities such as AIED (Artificial Intelligence in Education), L@S (Learning at Scale), Learning Science (LS), etc. in the same area that could be called Educational Data Science (EDS). We propose using the term EDS to integrate both LA and EDM with all these related communities.