How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses

Autor/inn/en	Gitinabard, Niki; Xu, Yiqiao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F.
Titel	How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses
Quelle	In: IEEE Transactions on Learning Technologies, 12 (2019) 2, S.184-197 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Gitinabard, Niki) ORCID (Heckman, Sarah)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2019.2911832
Schlagwörter	Blended Learning; Educational Technology; Technology Uses in Education; Prediction; Models; Undergraduate Study; Student Characteristics; Study Habits; Interaction; Interpersonal Relationship; Performance; Teamwork; Large Group Instruction; Online Courses; Computer Science Education; College Students; Social Networks; North Carolina + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Vorhersage; Analogiemodell; Grundstudium; Study behavior; Study behaviour; Studienverhalten; Interaktion; Interpersonal relation; Interpersonal relations; Interpersonelle Beziehung; Zwischenmenschliche Beziehung; Achievement; Leistung; Online course; Online-Kurs; Computer science lessons; Informatikunterricht; Collegestudent; Social network; Soziales Netzwerk
Abstract	Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions, even for repeated offerings of the same course. In this paper, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models of student performance based on persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This paper tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01