Discovering Players' Problem-Solving Behavioral Characteristics in a Puzzle Game through Sequence Mining

Karen D. Wang, Haoyu Liu, David Deliema, Nick Haber, Shima Salehi

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

Abstract

Digital games offer promising platforms for assessing student higher-order competencies such as problem-solving. However, processing and analyzing the large volume of interaction log data generated in these platforms to uncover meaningful behavioral patterns remain a complex research challenge. In this study, we employ sequence mining and clustering techniques to examine students' log data in an interactive puzzle game that requires player to change rules to win the game. Our goal is to identify behavioral characteristics associated with the problem-solving practices adopted by individual students. The findings indicate that the most effective problem solvers made fewer rule changes and took longer time to make those changes across both an introductory and a more advanced level of the game. Conversely, rapid rule change actions were linked to ineffective problem-solving. This research underscores the potential of sequence mining and cluster analysis as generalizable methods for understanding student higher-order competencies through log data in digital gaming and learning environments. It also suggests future directions on how to provide just-in-time, in-game feedback to enhance student problem-solving competences.

Original languageEnglish (US)
Title of host publicationLAK 2024 Conference Proceedings - 14th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages498-506
Number of pages9
ISBN (Electronic)9798400716188
DOIs
StatePublished - Mar 18 2024
Event14th International Conference on Learning Analytics and Knowledge, LAK 2024 - Kyoto, Japan
Duration: Mar 18 2024Mar 22 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th International Conference on Learning Analytics and Knowledge, LAK 2024
Country/TerritoryJapan
CityKyoto
Period3/18/243/22/24

Bibliographical note

Publisher Copyright:
© 2024 Owner/Author.

Keywords

  • cluster analysis
  • digital games
  • log data
  • problem-solving
  • sequence mining

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