Online Learning Persistence and Academic Achievement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).

Autor/inn/en	Fang, Ying; Nye, Benjamin; Pavlik, Philip; Xu, Yonghong Jade; Graesser, Arthur; Hu, Xiangen
Titel	Online Learning Persistence and Academic Achievement [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).
Quelle	(2017), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Learning Processes; Academic Persistence; Correlation; Academic Achievement; Mathematics Instruction; Recordkeeping; Standardized Tests; Mastery Learning; Online Courses; Intelligent Tutoring Systems; Middle School Students; Teaching Methods; Achievement Tests; Scores; Pretests Posttests; Tennessee + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Korrelation; Schulleistung; Mathematics lessons; Mathematikunterricht; Leistungsnachweis; Standadised tests; Standardisierter Test; Online course; Online-Kurs; Intelligentes Tutorsystem; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Teaching method; Lehrmethode; Unterrichtsmethode; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen
Abstract	Student persistence in online learning environments has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.). The current examines student persistence in an adaptive learning environment, ALEKS (Assessment and LEarning in Knowledge Spaces). Specifically, the study explores the relationship between students' academic achievement and their persistence during learning. By using archived data that included their math learning log data and performance on two standardized tests, we first explored student learning behavior patterns with regard to their persistence during learning. Clustering analysis identified three distinctive patterns of persistence-related learning behaviors: (1) High persistence and rare topic shifting; (2) Low persistence and frequent topic shifting; and (3) Moderate persistence and moderate topic shifting. We further explored the association between persistence and academic achievement. No significant differences were observed between academic achievement and the different learning patterns. We interpret this result in addition to a preliminary exploration of topic mastery trends, to suggest that "wheel-spinning" behaviors coexist with persistence, and is ultimately not beneficial to learning. [For the full proceedings, see ED596512.] (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