Personalising Learning with Learning Analytics: A Review of the Literature

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Advances have been made in personalised learning by the developments in learning analytics, where useful information can be extracted from educational data and analysed to devise personalised learning solutions. This paper presents a review of the literature in this area, covering a total of 144 relevant empirical articles published between 2012 and 2019 collected from Scopus. It identifies the patterns in the use of learning analytics to personalise learning in terms of the environments (what), stakeholders (who), objectives (why) and methods (how). The results show a clear growth in the number of practices, and diversity in terms of the learning contexts where learning analytics was implemented; the types of data collected; the groups of target stakeholders; the objectives of learning analytics practices; the personalised learning goals; and the analytics methods. The findings also reveal the emergence of practices related to the teacher perspective and some areas which have not been fully addressed, such as personalised intervention for future work.

Original languageEnglish
Title of host publicationBlended Learning. Education in a Smart Learning Environment - 13th International Conference, ICBL 2020, Proceedings
EditorsSimon K.S. Cheung, Richard Li, Kongkiti Phusavat, Naraphorn Paoprasert, Lam-For Kwok
Pages39-48
Number of pages10
DOIs
Publication statusPublished - 2020
Event13th International Conference on Blended Learning, ICBL 2020 - Bangkok, Thailand
Duration: 24 Aug 202027 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12218 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Blended Learning, ICBL 2020
Country/TerritoryThailand
CityBangkok
Period24/08/2027/08/20

Keywords

  • Adaptive learning
  • Learning analytics
  • Personalisation
  • Personalised learning

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