Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning

Autor/inn/en	Botelho, Anthony F.; Varatharaj, Ashvini; Patikorn, Thanaporn; Doherty, Diana; Adjei, Seth A.; Beck, Joseph E.
Titel	Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 2, S.158-170 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Botelho, Anthony F.) ORCID (Adjei, Seth A.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2019.2912162
Schlagwörter	Student Attrition; Student Behavior; Early Intervention; Identification; At Risk Students; Academic Persistence; Learning Analytics; Stopouts; Productivity; Assignments; Mastery Learning; Online Courses; Integrated Learning Systems; Feedback (Response); Memory; Transfer of Training; Prediction; Models; Networks; Middle School Students + Suchen Sie Ihr Suchwort? Schülerbeurlaubung; Student behaviour; Schülerverhalten; Identifikation; Identifizierung; Ausstieg; Produktivität; Assignment; Auftrag; Zuweisung; Online course; Online-Kurs; Gedächtnis; Training; Transfer; Ausbildung; Vorhersage; Analogiemodell; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
Abstract	The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student "stopout," and unproductive high persistence, operationalized through student "wheel spinning," in an effort to better understand the relationship between these measures of unproductive persistence (i.e., stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01