What Will You Do Next? A Sequence Analysis on the Student Transitions Between Online Platforms in Blended Courses [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Gitinabard, Niki; Barnes, Tiffany; Heckman, Sarah; Lynch, Collin F.
Titel	What Will You Do Next? A Sequence Analysis on the Student Transitions Between Online Platforms in Blended Courses [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Blended Learning; Student Adjustment; Online Courses; Study Habits; High Achievement; Low Achievement; Behavior Patterns; Sequential Approach; Intelligent Tutoring Systems; Data Analysis; State Universities; Discussion Groups; Integrated Learning Systems; Introductory Courses; Undergraduate Students; Mathematics Instruction; Predictive Measurement; North Carolina + Suchen Sie Ihr Suchwort? Student; Students; Adjustment; Schüler; Schülerin; Studentin; Adaptation; Online course; Online-Kurs; Study behavior; Study behaviour; Studienverhalten; Unterdurchschnittliche Leistung; Schrittfolge; Intelligentes Tutorsystem; Auswertung; Staatliche Universität; Einführungskurs; Mathematics lessons; Mathematikunterricht
Abstract	Students' interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict students' performance in classes. One key feature of these actions that is often overlooked is how and when the students transition between different online platforms. In this work, we study sequences of student transitions between online tools in blended courses and identify which habits make the most difference between the higher and lower performing groups. While our results showed that most of the time students focus on a single tool, we were able to find patterns in their transitions to differentiate high and low performing groups. These findings can help instructors to provide procedural guidance to the students, as well as to identify harmful habits and make timely interventions. [For the full proceedings, see ED599096.] (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	2020/1/01