Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning

Autor/inn/en	Alonso-Fernández, Cristina; Calvo-Morata, Antonio; Freire, Manuel; Martínez-Ortiz, Iván; Fernández-Manjón, Baltasar
Titel	Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
Quelle	In: Journal of Learning Analytics, 9 (2022) 3, S.32-49 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Alonso-Fernández, Cristina) ORCID (Calvo-Morata, Antonio) ORCID (Freire, Manuel) ORCID (Martínez-Ortiz, Iván) ORCID (Fernández-Manjón, Baltasar)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Educational Games; Learning Analytics; Data Collection; Educational Improvement; Game Based Learning; Visualization; Student Evaluation; Evidence Based Practice + Suchen Sie Ihr Suchwort? Educational game; Lernspiel; Data capture; Datensammlung; Teaching improvement; Unterrichtsentwicklung; Visualisation; Visualisierung; Schulnote; Studentische Bewertung
Abstract	Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners' actions and results in serious games. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01