Student Profiling on Behavioral Patterns in an Online Mathematics Game: Clustering Using K-Means [Konferenzbericht] Paper presented at the International Conference of the Learning Sciences (16th, Hiroshima, Japan, Jun 6-10, 2022).

Autor/inn/en	Norum, Reilly; Lee, Ji-Eun; Ottmar, Erin
Titel	Student Profiling on Behavioral Patterns in an Online Mathematics Game: Clustering Using K-Means [Konferenzbericht] Paper presented at the International Conference of the Learning Sciences (16th, Hiroshima, Japan, Jun 6-10, 2022).
Quelle	(2022), (3 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Computer Games; Game Based Learning; Student Characteristics; Visual Aids; Student Behavior; Behavior Patterns; Mathematics Instruction; Teaching Methods; Algebra; Outcomes of Education; Grade 7; Middle School Students + Suchen Sie Ihr Suchwort? Computer game; Computerspiel; Computerspiele; Anschauungsmaterial; Student behaviour; Schülerverhalten; Mathematics lessons; Mathematikunterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Lernleistung; Schulerfolg; School year 07; 7. Schuljahr; Schuljahr 07; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
Abstract	This preliminary study examined whether distinct student profiles (N = 760) emerged based on their behavioral patterns in an online algebraic learning game. We applied k-means clustering analysis to clickstream data collected in the game and then examined how students' behavioral patterns varied across the clusters using data visualization. The results identified four groups of students based on their in-game behaviors, showing that there was a large variation in their behavioral patterns for engaging with the game. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01