1,917
Views
47
CrossRef citations to date
0
Altmetric
Articles

Toward a taxonomy of adaptivity for learning

ORCID Icon & ORCID Icon
Pages 275-300 | Received 18 Jun 2019, Accepted 13 Jan 2020, Published online: 22 Jun 2020
 

Abstract

Adaptive learning and personalization have long been of great interest to learning designers and educators, and recent technological advances that have opened up a range of new possibilities for adaptivity. However, we lack clear definitions of the terms adaptivity and personalization, and the theoretical and empirical soundness of implementations of corresponding systems varies greatly. We therefore first provide definitions for key concepts related to adaptivity. We then discuss what variable systems should adapt for, how these variables can be measured, and what modifications to the learner experience can be made based on these variables. We propose a taxonomy of adaptivity that distinguishes adaptivity based on cognitive, emotional, motivational, and social/cultural variables, and that defines types of adaptivity at a macro-level and a micro-level.

Acknowledgments

We would like to thank the editors and the three anonymous reviewers for their very useful and insightful comments on an earlier draft of our paper.

Notes

1 We thank the anonymous reviewer for making this important point.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 176.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.