Decoding the Learning Analytics to Determine the Relationship of Student-Instructor and Student-Student Interactions on Students Learning and Motivation in a Flipped Classroom

Autor/inn/en	Gungadeen, Anuradha; Rajnee, Lobind
Titel	Decoding the Learning Analytics to Determine the Relationship of Student-Instructor and Student-Student Interactions on Students Learning and Motivation in a Flipped Classroom
Quelle	In: International Journal on E-Learning, 22 (2023) 2, S.109-128 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1537-2456
Schlagwörter	Learning Analytics; Teacher Student Relationship; Peer Relationship; Interaction; Learning Processes; Learning Motivation; Flipped Classroom; Learning Management Systems; Teacher Educators; Trainees; Class Activities; Learning Experience + Suchen Sie Ihr Suchwort? Teacher student relationships; Lehrer-Schüler-Beziehung; Peer-Beziehungen; Interaktion; Learning process; Lernprozess; Motivation for studies; Lernmotivation; Flipped classrooms; Flip teaching; Inverted teaching; Teacher education; Education; Lehrerausbildung; Lehrerbildung; Auszubildender; Weibliche Auszubildende; Lernerfahrung
Abstract	With the current shift in educational settings to blended and flipped classroom and the introduction of learning management systems (LMS) such as Moodle, it is no surprise big data has found its place in education and is predicted to be extensively implemented in institutions of higher education (Johnson et al., 2013). In a flipped classroom approach, it is instrumental that there are quality interactions between student-to-instructor as well as student-to-student. Altogether, derived from business intelligence and data mining, learning analytics is referred to as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs" (Siemens, 2011). The purpose of this mixed method was to decode the learning analytics installed on Moodle LMS to determine the student-instructor and student-student interactions that promotes student learning and motivation. The sample population consisted of trainee educators enrolled multiple academic disciplines. An online instrument was also designed to capture trainees' characteristics, their perceptions of learning, motivation, student-to-student interactions and student-to-instructor interactions. The results revealed that the quality interactions are determining and significant contributors of student learning and motivation. (As Provided).
Anmerkungen	Association for the Advancement of Computing in Education. P.O. Box 719, Waynesville, NC 28786. Tel: 828-246-9558; Fax: 828-246-9557; e-mail: info@aace.org; Web site: http://www.aace.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01