Demystifying Learning Analytics in Personalised Learning

TLDR

Learning analytics, defined as measuring, collecting, analysing and reporting learner data to understand and optimise learning, is typically developed in‑house by individual institutions to meet their students’ specific needs. The study proposes that learning analytics, when integrated within a personalised learning framework, can improve student learning by defining key features of their relationship and highlighting how analytics depend on personalised approaches for educators and learners. The authors support students with performance‑comparison visualisations and personalised recommendations for resources, paths, or peers via a recommendation system.

Abstract

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.