Collaborative Problem-Solving Process in a Science Serious Game: Exploring Group Action Similarity Trajectory [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Kang, Jina; An, Dongwook; Yan, Lili; Liu, Min
Titel	Collaborative Problem-Solving Process in a Science Serious Game: Exploring Group Action Similarity Trajectory [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Cooperative Learning; Problem Solving; Science Education; Educational Games; Learning Processes; Middle School Students; Grade 6; Cluster Grouping; Sequential Approach + Suchen Sie Ihr Suchwort? Kooperatives Lernen; Problemlösen; Naturwissenschaftliche Bildung; Educational game; Lernspiel; Learning process; Lernprozess; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 06; 6. Schuljahr; Schuljahr 06; Eingruppierung; Schrittfolge
Abstract	Collaborative problem-solving (CPS) as a key competency required in the 21st century. There has been an increasing need to understand CPS since it involves not only cognitive but also social processes, and thus its process is difficult to examine. Recent research has highlighted that computer-based learning environments provide an opportunity for students to collaborate with others to solve scientific problems and facilitate their knowledge building process, which can be dynamically tracked within the systems. However, limited research has attempted to identify CPS process captured in the computer-based learning environments designed for supporting CPS. This study therefore aimed to investigate students' CPS process in a serious game, "Alien Rescue," by analyzing a student's daily tool use action sequence generated in the game. First, we computed a daily gameplay action similarity among students in a group using a similarity coefficient, "Jaccard" ("Jac"). Each group's "Jac" coefficients over the entire gameplay period (i.e. six days over three weeks) were considered as the group action similarity trajectory. The "Jac" coefficient of each day was entered as a single feature (i.e. a total of six features) to conduct a "KmL" cluster analysis that clusters longitudinal data. Three clusters of groups with similar behavior traits (i.e. group action similarity trajectories) were identified. The groups' background information (e.g. solution scores, knowledge gain scores) further provided how the groups' CPS traits can be related to their learning performance. [For the full proceedings, see ED599096.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01