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Increased emotional engagement in game-based learning – A machine learning approach on facial emotion detection data

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number103641
JournalComputers and Education
Volume142
DOIs
Publication statusPublished - 1 Dec 2019
Publication typeA1 Journal article-refereed

Abstract

It is often argued that game-based learning is particularly effective because of the emotionally engaging nature of games. We employed both automatic facial emotion detection as well as subjective ratings to evaluate emotional engagement of adult participants completing either a game-based numerical task or a non-game-based equivalent. Using a machine learning approach on facial emotion detection data we were able to predict whether individual participants were engaged in the game-based or non-game-based task with classification accuracy significantly above chance level. Moreover, facial emotion detection as well as subjective ratings consistently indicated increased positive as well as negative emotions during game-based learning. These results substantiate that the emotionally engaging nature of games facilitates learning.

ASJC Scopus subject areas

Keywords

  • Emotions, Game-based learning, Human-computer interface, Interactive learning environments, Media in education

Publication forum classification

Field of science, Statistics Finland