Language to Completion: Success in an Educational Data Mining Massive Open Online Class [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Crossley, Scott; McNamara, Danielle S.; Baker, Ryan; Wang, Yuan; Paquette, Luc; Barnes, Tiffany; Bergner, Yoav
Institution	International Educational Data Mining Society
Titel	Language to Completion: Success in an Educational Data Mining Massive Open Online Class [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Online Courses; Large Group Instruction; Information Retrieval; Data Analysis; Academic Achievement; Dropouts; Academic Persistence; Discourse Analysis; Natural Language Processing; Computer Mediated Communication; Discussion Groups; Predictor Variables; Success; Accuracy; Multivariate Analysis; Discriminant Analysis; Statistical Analysis + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Auswertung; Schulleistung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Diskursanalyse; Natürliche Sprache; Computerkonferenz; Prädiktor; Erfolg; Multivariate Analyse; Diskriminanzanalyse; Statistische Analyse
Abstract	Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused on click-streams, demographics, and sentiment analysis. This study uses natural language processing (NLP) to examine if the language in the discussion forum of an educational data mining MOOC is predictive of successful class completion. The analysis is applied to a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums. The findings indicate that the language produced by students can predict with substantial accuracy (67.8%) whether students complete the MOOC. This predictive power suggests that NLP can help us both to understand student retention in MOOCs and to develop automated signals of student success. [For complete proceedings, see ED560503.] (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	2022/4/11