Using gameplay data to examine learning behavior patterns in a serious game

Jina Kang, Min Liu, Wen Qu

Research output: Contribution to journalArticlepeer-review


Research has shown how open-ended serious games can facilitate students' development of specific skills and improve learning performance through problem-solving. However, understanding how students learn these complex skills in a game environment is a challenge, as much research uses typical paper-and-pencil assessments and self-reported surveys or other traditional observational and quantitative methods. The purpose of this study is to identify students' learning behavior patterns of problem-solving and explore behavior patterns of different performing groups within an open-ended serious game called Alien Rescue. To accomplish this purpose, this study intends to use gameplay data by incorporating sequential pattern mining and statistical analysis. The findings of this study confirmed the results from previous research (using ex situ data such as interviews) and at the same time provide an analytical approach to understand in-depth students' sequential behavior patterns using in situ gameplay data. This study examined the frequent sequential patterns between low- and high-performing students and showed that problem-solving strategies were different between these two performing groups. By using this integrated analytical method, we can gain a better understanding of the learning pathway of students’ performance and problem-solving strategies of students with different learning characteristics in a serious games context.

Original languageEnglish (US)
Pages (from-to)757-770
Number of pages14
JournalComputers in Human Behavior
StatePublished - Jul 1 2017
Externally publishedYes


  • Learning behavior
  • Learning process
  • Middle school science
  • Pattern mining
  • Problem-solving
  • Serious games analytics

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)


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