A Theoretical Model of Micro-Learning for Second Language Instruction

Autor/inn/en	Khong, Hou Keat; Kabilan, Muhammad Kamarul
Titel	A Theoretical Model of Micro-Learning for Second Language Instruction
Quelle	In: Computer Assisted Language Learning, 35 (2022) 7, S.1483-1506 (24 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Khong, Hou Keat)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0958-8221
DOI	10.1080/09588221.2020.1818786
Schlagwörter	Second Language Instruction; Cognitive Processes; Difficulty Level; Self Determination; Multimedia Instruction; Learning Theories; Learning Strategies; Learning Motivation; Models + Suchen Sie Ihr Suchwort? Fremdsprachenunterricht; Cognitive process; Kognitiver Prozess; Schwierigkeitsgrad; Selbstbestimmung; Multimediales Lernen; Learning theory; Lerntheorie; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Motivation for studies; Lernmotivation; Analogiemodell
Abstract	The notion of "Micro-Learning" (ML) has been repeatedly accented as a successful learning approach in different learning phenomena. Despite these optimistic emphases, several studies lack a theoretical grounding in adoption of ML, thus missing a shared perspective of the education community. The scarce theoretical justification for understanding the nuanced dynamics of ML restricts the practical use of this pedagogical approach in "Second Language" (L2) instruction. Therefore, this paper seeks to fill the gap by proposing a theoretical model of ML for L2 instruction. First, a brief background on ML is provided evaluating its benefits and pitfalls in general teaching and learning enterprise. Second, three established theories are explicitly discussed based on a careful examination of the conceptual characteristics and empirical observations of ML. A theoretical model of ML is then devised based on relations postulated among proposed theories and application of the model to existing L2 MLs is made explicit. Finally, implications for research and practice are discussed to offer a more robust and descriptive picture of how ML can promote L2 teaching and learning across different contexts. Drawing from these theoretical insights, a principled way to integrating ML into L2 instruction can be made available for future research. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01