Deep Knowledge Tracing and Engagement with MOOCs [Konferenzbericht] Paper presented at the Learning Analytics and Knowledge Conference (LAK) (9th, Tempe, AZ, Mar 4-8, 2019).

Autor/inn/en	Mongkhonvanit, Kritphong; Kanopka, Klint; Lang, David
Titel	Deep Knowledge Tracing and Engagement with MOOCs [Konferenzbericht] Paper presented at the Learning Analytics and Knowledge Conference (LAK) (9th, Tempe, AZ, Mar 4-8, 2019).
Quelle	(2019), (3 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Online Courses; Large Group Instruction; Knowledge Level; Learner Engagement; Prediction; Intervention; Academic Persistence; Artificial Intelligence; At Risk Students; Dropouts; Probability; Video Technology + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Wissensbasis; Vorhersage; Künstliche Intelligenz; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted intervention scan be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding. [This paper was published in: "The 9th International Learning Analytics Knowledge Conference (LAK19)," ISBN 978-1-4503-6256-6, March 4-8, 2019, Tempe, AZ, USA" (pp. 340-342). New York, NY: ACM, 2019.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01