Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors

Autor/inn/en	Chen, Weiyu; Brinton, Christopher G.; Cao, Da; Mason-Singh, Amanda; Lu, Charlton; Chiang, Mung
Titel	Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 1, S.44-58 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chen, Weiyu) ORCID (Brinton, Christopher G.) ORCID (Mason-Singh, Amanda) ORCID (Lu, Charlton)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2018.2793193
Schlagwörter	Online Courses; Outcomes of Education; Prediction; Course Content; Social Networks; Student Behavior; Computer Assisted Instruction; Learning Processes; Data Analysis; Data Use; Correlation; Engineering; Instructional Design; Computer Software + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Lernleistung; Schulerfolg; Vorhersage; Kursprogramm; Social network; Soziales Netzwerk; Student behaviour; Schülerverhalten; Computer based training; Computerunterstützter Unterricht; Learning process; Lernprozess; Auswertung; Korrelation; Maschinenbau; Lesson concept; Lessonplan; Unterrichtsentwurf
Abstract	We study learning outcome prediction for online courses. Whereas prior work has focused on semester-long courses with frequent student assessments, we focus on short-courses that have single outcomes assigned by instructors at the end. The lack of performance data and generally small enrollments makes the behavior of learners, captured as they interact with course content and with one another in Social Learning Networks (SLN), essential for prediction. Our method defines several (machine) learning features based on the processing of behaviors collected on the modes of (human) learning in a course, and uses them in appropriate classifiers. Through evaluation on data captured from three two-week courses hosted through our delivery platforms, we make three key observations: (i) behavioral data contains signals predictive of learning outcomes in short-courses (with classifiers achieving AUCs = 0.8 after the two weeks), (ii) early detection is possible within the first week (AUCs = 0.7 with the first week of data), and (iii) the content features have an "earliest" detection capability (with higher AUC in the first few days), while the SLN features become the more predictive set over time as the network matures. We also discuss how our method can generate behavioral analytics for instructors. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01