Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Nguyen, Huy Anh; Hou, Xinying; Stamper, John; McLaren, Bruce M.
Titel	Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Computer Games; Educational Games; Game Based Learning; Instructional Effectiveness; Learning Analytics; Mastery Learning; Difficulty Level; Arithmetic; Number Concepts; Middle School Students; Emotional Response; Student Attitudes; Gender Differences; Grade 5; Grade 6 + Suchen Sie Ihr Suchwort? Computer game; Computerspiel; Computerspiele; Educational game; Lernspiel; Unterrichtserfolg; Schwierigkeitsgrad; Addition; Arithmetik; Arithmetikunterricht; Rechnen; Number concept; Zahlbegriff; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Emotionales Verhalten; Schülerverhalten; Geschlechterkonflikt; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06
Abstract	A challenge in digital learning games is assessing students' learning behaviors, which are often intertwined with game behaviors. How do we know whether students have learned enough or needed more practice at the end of their game play? To answer this question, we performed post hoc analyses on a prior study of the game "Decimal Point," which teaches decimal numbers and decimal operations to middle school students. Using Bayesian Knowledge Tracing, we found that students had the most difficulty with mastering the number line and sorting skills, but also tended to over-practice the skills they had previously mastered. In addition, using students' survey responses and in-game measurements, we identified the best feature sets to predict test scores and self-reported enjoyment. Analyzing these features and their connections with learning outcomes and enjoyment yielded useful insights into areas of improvement for the game. We conclude by highlighting the need for combining traditional test measures with rigorous learning analytics to critically evaluate the effectiveness of learning games. [For the full proceedings, see ED607784.] (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	2024/1/01