Learning Analytics: definitions, applications and related fields

Abstract

In the last few decades, the number of people connected online for educational purpose is increasing dramatically and consequently a huge quantity of data is being generated. This data is mainly “traces” or “digital breadcrumbs” that students leave as they interact with online learning environments. Confident that this data can teach us about learners’ behaviors and help us enhancing learning experience, there has been a growing interest in the automatic analysis of such data. A research area referred to as Learning Analytics (LA) is identified. It is considered by many researchers as a strategic trend in education. Nevertheless, LA cannot be considered as a new field, it actually derives from different related fields such as Educational Data Mining, Academic Analytics, Action research, Personalized Adaptive Learning.

In this paper, we begin with an examination of the educational factors that have driven the need and the development of analytics in education. We study connections between LA and its most related fields (Educational Data Mining and Academic Analytics). We summarize this interconnection in a table showing for each field the objectives, the stakeholders, the methods and the initial trigger behind the analysis actions. After that we study and run through LA applications presented in the International Learning Analytics & Knowledge Conferences during the three last years. Finally, we conclude by identifying some challenges in the area of LA in relation to the driven factors related to Educational Data.