Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25271
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dc.contributor.authorKhong, H.K.-
dc.contributor.authorKabilan, M.K. UniKL MSI-
dc.date.accessioned2022-01-05T00:52:19Z-
dc.date.available2022-01-05T00:52:19Z-
dc.date.issued2022-01-05-
dc.identifier.urihttp://hdl.handle.net/123456789/25271-
dc.description.abstractThe 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.en_US
dc.subjectCognitive load theoryen_US
dc.subjectcognitive theory of multimedia learningen_US
dc.subjectmicro-learningen_US
dc.subjectsecond language instructionen_US
dc.subjectself-determination theory of motivationen_US
dc.titleA theoretical model of micro-learning for second language instructionen_US
dc.conference.nameComputer Assisted Language Learningen_US
dc.conference.year2020en_US
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