Unfolding Students' Online Assignment Submission Behavioral Patterns Using Temporal Learning Analytics

Autor/inn/en	Kokoç, Mehmet; Akçapinar, Gökhan; Hasnine, Mohammad Nehal
Titel	Unfolding Students' Online Assignment Submission Behavioral Patterns Using Temporal Learning Analytics
Quelle	In: Educational Technology & Society, 24 (2021) 1, S.223-235 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Electronic Learning; Assignments; Behavior Patterns; Learning Analytics; College Students; Prediction; Teaching Methods; Foreign Countries; Integrated Learning Systems; At Risk Students; Turkey + Suchen Sie Ihr Suchwort? Assignment; Auftrag; Zuweisung; Collegestudent; Vorhersage; Teaching method; Lehrmethode; Unterrichtsmethode; Ausland; Türkei
Abstract	This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining analysis were used to analyze students' assignment submission behaviors in an online learning environment. The results revealed that students displayed similar patterns in terms of assignment submission behavior. Moreover, it was observed that students' assignment submission behavior did not change much across the semester. When these results are analyzed together with the students' academic performance at the end of the semester, it was observed that students' end-of-term academic performance can be predicted from their assignment submission behaviors at the beginning of the semester. Our results, within the scope of precision education, can be used to diagnose and predict students who are not going to submit the next assignments as the semester progresses as well as students who are going to fail at the end of the semester. Therefore, learning analytics interventions can be designed based on these results to prevent possible academic failures. Furthermore, the findings of the study are discussed considering the development of early-warning intervention systems for at-risk students and precision education. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01