An Algorithm Quantifying Flow for Adaptive Learning

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  • Title: An Algorithm Quantifying Flow for Adaptive Learning
  • Author(s): M. Anthony Kapolka III, Bernie Graham
  • Publisher: Common Ground Research Networks
  • Collection: Common Ground Research Networks
  • Series: Science in Society
  • Journal Title: The International Journal of Science in Society
  • Keywords: Csíkszentmihályi, Flow, Task-Centric Adaptation, Computer Adaptive Testing, Multiple-Choice Testing
  • Volume: 7
  • Issue: 4
  • Year: 2015
  • ISSN: 1836-6236 (Print)
  • ISSN: 1836-6244 (Online)
  • DOI: https://doi.org/10.18848/1836-6236/CGP/v07i04/59290
  • Citation: Kapolka III, M. Anthony, and Bernie Graham. 2015. "An Algorithm Quantifying Flow for Adaptive Learning." The International Journal of Science in Society 7 (4): 7-17. doi:10.18848/1836-6236/CGP/v07i04/59290.
  • Extent: 11 pages

Abstract

Educators recognize the need to provide users with context-appropriate challenges. Despite this belief, online learning games typically provide uninformed adaptive selection of learning tasks. Instead, we present our design of a quantified flow-channel metric, a ratio of user skill over problem difficulty. By mining and clustering historical play data from brainrush.com, a crowdsourced online learning platform, we weigh distractors for each learning objective. Our algorithm adaptively constructs questions based on our metric to maintain Flow. Our approach provides in-game adaptation to maintain the user's Flow experience and is applicable to a wide range of learning tasks.