A fun-accuracy trade-off in game-based learning

Simon Greipl, Manuel Ninaus, Darlene Bauer, Kristian Kiili, Korbinian Moeller

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

4 Citations (Scopus)
14 Downloads (Pure)


The present paper illustrates that the game-based implementation of a learning task - here to train basic math skills - entails benefits with strings attached. We developed a game for learning math with its core element based on the number line estimation task. In this task, participants have to indicate the position of a target number on a number-line, which is thought to train basic numerical skills. Participants completed both the game on a mobile device and a conventional paper-pencil version of the task. They indicated to have significantly more fun using the game-based environment. However, they also made considerably higher estimation errors in the game compared to the paper-pencil version. In this case, more fun in a math-learning task was ultimately bought at the expense of lower reliability, namely lowered accuracy of estimations in the learning game. This fun-accuracy trade-off between adding elements for enjoyment and clarity of content is discussed together with the consequences for game-design.

Original languageEnglish
Title of host publicationGames and Learning Alliance - 7th International Conference, GALA 2018, Proceedings
EditorsHeinrich Söbke, Manuel Gentile, Mario Allegra
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030115470
Publication statusPublished - 2019
Publication typeA4 Article in conference proceedings
EventInternational Conference on Games and Learning Alliance - Palermo, Italy
Duration: 5 Dec 20187 Dec 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Games and Learning Alliance


  • Enjoyment
  • Game-based learning
  • Mathematics
  • Number-line estimation
  • Reliability
  • User-experience

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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