Towards a Maturity Model for Learning Analytics adoption an overview of its levels and areas

Elyda Freitas, Fernando Fonseca, Vinicius Garcia, Rafael Ferreira, Dragan Gasevic

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

Abstract

Learning Analytics is a new field in education whose adoption can bring benefits for teaching and learning processes. However, many higher education institutions may not be ready to start using learning analytics due to challenges such as organizational culture, infrastructure, and privacy. In this context, Maturity Models (MMs) can support institutions to systematize their processes, enabling them to progress successively in the learning analytics adoption. MMs are used in different fields to support the improvement of processes, describing them in terms of maturity levels, and identifying enhancements that could lead an organization to higher levels of such maturity. Thus, this paper presents an outline of a MM for Learning Analytics adoption in higher education institutions, describing its levels and areas, together with its development methodology.

Original languageEnglish
Title of host publicationProceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020
EditorsMaiga Chang, Demetrios G Sampson, Ronghuai Huang, Danial Hooshyar, Nian-Shing Chen, Kinshuk , Margus Pedaste
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages180-184
Number of pages5
ISBN (Electronic)9781728160900
ISBN (Print)9781728160917
DOIs
Publication statusPublished - 2020
EventIEEE International Conference on Advanced Learning Technologies 2020 - Virtual, Online, Estonia
Duration: 6 Jul 20209 Jul 2020
Conference number: 20th
https://ieeexplore.ieee.org/xpl/conhome/9146898/proceeding (Proceedings)
https://icalt2020.ut.ee/home-0 (Website)

Publication series

NameProceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2161-3761
ISSN (Electronic)2161-377X

Conference

ConferenceIEEE International Conference on Advanced Learning Technologies 2020
Abbreviated titleICALT 2020
CountryEstonia
CityVirtual, Online
Period6/07/209/07/20
Internet address

Keywords

  • Adoption
  • Learning analytics
  • Maturity model
  • Policy development

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