Predicting eLearning Readiness Model: A Case Study of Fiji's Secondary School Science Teachers

Autor/inn/en	Prasad, Shaneel; Rao, Dinesh; Raturi, Shikha
Titel	Predicting eLearning Readiness Model: A Case Study of Fiji's Secondary School Science Teachers
Quelle	In: International Journal on E-Learning, 20 (2021) 1, S.17-45 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1537-2456
Schlagwörter	Electronic Learning; Learning Readiness; Secondary School Teachers; Science Teachers; Technology Integration; Technological Literacy; Computer Use; Internet; Teacher Education; Teacher Motivation; Correlation; Public Schools; Foreign Countries; Fiji + Suchen Sie Ihr Suchwort? Lernbereitschaft; Science; Teacher; Teachers; Science teacher; Wissenschaft; Lehrer; Lehrerin; Lehrende; Technisches Wissen; Lehrerausbildung; Lehrerbildung; Korrelation; Public school; Öffentliche Schule; Ausland; Fidschi
Abstract	The use of information communication technologies (ICTs) tools for eLearning has immense prospects in teaching and learning especially in the Sciences. Despite the Fiji governments' focus and efforts towards introduction of ICTs in school, not all science teachers seem to make use of ICTs. This study investigated how well-grounded these science teachers are in the use and implementation of eLearning tools in their pedagogical practices. A cross-sectional survey design was used with random sampling of 150 participants from 42 public secondary schools in nine designated districts in Fiji. The independent variables used were "computer technology usage," "internet usage," "perception," "implication of eLearning on science education," "teacher training experiences" and "institutional eLearning status" while the dependent variable was "readiness & motivation" for eLearning. The findings reveal that all six independent variables are positively correlated to "readiness & motivation" of the science teachers for acceptance of eLearning with "perception" and "implication of eLearning on science education" having the strongest correlation. Utilizing the multiple regression technique, a best fit model is proposed for eLearning readiness. (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