Filtered Time Series Analyses of Student Problem-Solving Behaviors in Game-Based Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).

Autor/inn/en	Sawyer, Robert; Rowe, Jonathan; Azevedo, Roger; Lester, James
Titel	Filtered Time Series Analyses of Student Problem-Solving Behaviors in Game-Based Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).
Quelle	(2018), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Educational Games; Teaching Methods; Educational Technology; Technology Uses in Education; Science Instruction; Microbiology; Problem Solving; Program Effectiveness; Computer Simulation; College Students; College Science; Proximity + Suchen Sie Ihr Suchwort? Educational game; Lernspiel; Teaching method; Lehrmethode; Unterrichtsmethode; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Mikrobiologie; Problemlösen; Computergrafik; Computersimulation; Collegestudent; Lebensnähe
Abstract	Student interactions with game-based learning environments produce a wide range of in-game problem-solving sequences. These sequences can be viewed as trajectories through a game's problem-solving space. In this paper, we present a general framework for analyzing students' problem-solving behavior in game-based learning environments by filtering their gameplay action sequences into time series representing trajectories through the game's problem-solving space. This framework was investigated with data from a laboratory study conducted with 68 college students tasked with solving the problem scenario in a game-based learning environment for microbiology education, CRYSTAL ISLAND. Using this representation of student problem solving, we derive the slope of the problem-solving trajectories and lock-step Euclidean distance to an expert problem-solving trajectory. Analyses indicate that the trajectory slope and temporal distance to an expert path are both correlated with students' normalized learning gains, as well as a complementary measure of in-game problem-solving performance. The results suggest that the filtered time series framework for analyzing student problem-solving behavior shows significant promise for assessing the temporal nature of student problem solving during game-based learning. [For the full proceedings, see ED593090.] (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