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

Autor/inn/en	Snow, Erica L.; Likens, Aaron D.; Allen, Laura K.; McNamara, Danielle S.
Titel	Taking Control: Stealth Assessment of Deterministic Behaviors within a Game-Based System
Quelle	In: International Journal of Artificial Intelligence in Education, 26 (2016) 4, S.1011-1032 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-015-0085-5
Schlagwörter	Student Behavior; Pretests Posttests; High School Students; Teaching Methods; Technology Uses in Education; Educational Technology; Interaction; Training; Intelligent Tutoring Systems; Educational Games; Recall (Psychology); Instructional Effectiveness; Statistical Analysis + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Teaching method; Lehrmethode; Unterrichtsmethode; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtsmedien; Interaktion; Ausbildung; Intelligentes Tutorsystem; Educational game; Lernspiel; Abberufung; Unterrichtserfolg; Statistische Analyse
Abstract	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. [This article is also available in ERIC as a Grantee Submission, see ED565488.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11