Taking Control: Stealth Assessment of Deterministic Behaviors Within a Game-Based System

Erica L. Snow, Aaron D. Likens, Laura K. Allen, Danielle McNamara

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning environments. The current work captures variations in students’ behavior patterns by employing two dynamic analyses to classify students’ sequences of choices within an adaptive learning environment. Random Walk analyses and Hurst exponents were used to classify students’ interaction patterns as random or deterministic. Forty high school students interacted with the game-based system, iSTART-ME, for 11-sessions (pretest, 8 training sessions, posttest, and a delayed retention test). Analyses revealed that students who interacted in a more deterministic manner also generated higher quality self-explanations during training sessions. The results point toward the potential for dynamic analyses such as random walk analyses and Hurst exponents to provide stealth assessments of students’ learning behaviors while engaged within a game-based environment.

Original languageEnglish (US)
Pages (from-to)1011-1032
Number of pages22
JournalInternational Journal of Artificial Intelligence in Education
Issue number4
StatePublished - Dec 1 2016


  • Dynamic analyses
  • Game-based intelligent tutoring systems
  • Log data
  • Stealth assessment

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics


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