Using Temporal Analytics to Detect Inconsistencies between Learning Design and Student Behaviours

Autor/inn/en	Nguyen, Quan; Huptych, Michal; Rienties, Bart
Titel	Using Temporal Analytics to Detect Inconsistencies between Learning Design and Student Behaviours
Quelle	In: Journal of Learning Analytics, 5 (2018) 3, S.120-135 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Nguyen, Quan) ORCID (Huptych, Michal) ORCID (Rienties, Bart)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Time Management; Online Courses; Educational Technology; Technology Uses in Education; Instructional Design; Assignments; Teaching Methods; Context Effect; Data Collection; Data Analysis; Foreign Countries; Distance Education; Integrated Learning Systems; College Students; Environmental Education; United Kingdom + Suchen Sie Ihr Suchwort? Zeitmanagement; Online course; Online-Kurs; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Lesson concept; Lessonplan; Unterrichtsentwurf; Assignment; Auftrag; Zuweisung; Teaching method; Lehrmethode; Unterrichtsmethode; Data capture; Datensammlung; Auswertung; Ausland; Distance study; Distance learning; Fernunterricht; Collegestudent; Umweltbildung; Umwelterziehung; Umweltpädagogik; Großbritannien
Abstract	Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students' timing of engagement and its relation to learning design (LD) and academic performance have received limited attention. This study investigates to what extent students' timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. Our findings revealed a mismatch between how instructors designed for learning and how students study. In most weeks, students spent less time studying the assigned materials on the virtual learning environment (VLE) compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. By incorporating the pedagogical context into learning analytics, not only we can understand what, why, and when students engage, but also how their behaviours are influenced by the way instructors design for learning. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01