Linking Language to Math Success in an On-Line Course [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).

Autor/inn/en	Crossley, Scott; Barnes, Tiffany; Lynch, Collin; McNamara, Danielle S.
Titel	Linking Language to Math Success in an On-Line Course [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	Success; Mathematics Instruction; Language Usage; Blended Learning; Conventional Instruction; Undergraduate Students; College Mathematics; Models; Scores; Peer Teaching; Data Collection; Data Analysis; Language Proficiency; Syntax; Computer Mediated Communication; Educational Technology; Technology Uses in Education; Online Courses; North Carolina + Suchen Sie Ihr Suchwort? Erfolg; Mathematics lessons; Mathematikunterricht; Sprachgebrauch; Analogiemodell; Peer group teaching; Peer Group Teaching; Data capture; Datensammlung; Auswertung; Language skill; Language skills; Sprachkompetenz; Computerkonferenz; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Online course; Online-Kurs
Abstract	This study takes a novel approach toward understanding success in a math course by examining the linguistic features and affect of students' language production within a blended (with both on-line and traditional face to face instruction) undergraduate course (n=158) on discrete mathematics. Three linear effects models were compared: (a) a baseline linear model including nonlinguistic fixed effects, (b) a model including only linguistic factors, (c) a model including both linguistic and non-linguistic effects. The best model (c) explained 16% of the variance of final course scores, revealing significant effects for one non-linguistic feature (days on the system) and two linguistic features ("Number of dependents per prepositional object nominal and Sentence linking connectives"). One non-linguistic factor ("Is a peer tutor") and two linguistic variables ("Words related to self and Words related to tool use") demonstrated marginal significance. The findings indicate that language proficiency is strongly linked to math performance such that more complex syntactic structures and fewer explicit cohesion devices equate to higher course performance. The linguistic model also indicated that less self-centered students and students using words related to tool use were more successful. In addition, the results indicate that students that are more active in on-line discussion forums are more likely to be successful. [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