Design challenge levels in e-learning? Insights from a large-scale field experiment

Andy Tao Li, De Liu, Sean Xin Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The existing adaptive e-learning literature focuses on detecting and mitigating knowledge gaps but has paid scant attention to the issue of regulating the challenge levels. The latter issue is especially relevant when it comes to sequence the set of practice problems. Insights from flow theory suggest that learners would be more engaged if their perceived challenges match their abilities. Despite these theoretical predictions, there are gaps in terms of how to administrate the desirable level of challenge in dynamic learning environments and whether it pays to do so despite the fact that one may not perfectly control the level of challenge. Field experiments at seven middle schools reveal that steady challenge benefits both weak and strong learners compared with fluctuating challenges. Moreover, the optimal challenge is heterogeneous on learner's preparations, and weak learners benefit more from low challenges, while strong learners are relatively insensitive to the challenge level.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems, ICIS 2020 - Making Digital Inclusive
Subtitle of host publicationBlending the Local and the Global
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632553
StatePublished - 2020
Event2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020 - Virtual, Online, India
Duration: Dec 13 2020Dec 16 2020

Publication series

NameInternational Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global

Conference

Conference2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020
Country/TerritoryIndia
CityVirtual, Online
Period12/13/2012/16/20

Bibliographical note

Publisher Copyright:
© ICIS 2020. All rights reserved.

Keywords

  • Adaptive learning
  • Flow theory
  • Optimal challenge
  • Personalized feedback
  • Steady challenge

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