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A theoretical model of micro-learning for second language instruction

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dc.contributor.author Khong, H.K.
dc.contributor.author Kabilan, M.K. UniKL MSI
dc.date.accessioned 2022-01-05T00:52:19Z
dc.date.available 2022-01-05T00:52:19Z
dc.date.issued 2022-01-05
dc.identifier.uri http://hdl.handle.net/123456789/25271
dc.description.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. en_US
dc.subject Cognitive load theory en_US
dc.subject cognitive theory of multimedia learning en_US
dc.subject micro-learning en_US
dc.subject second language instruction en_US
dc.subject self-determination theory of motivation en_US
dc.title A theoretical model of micro-learning for second language instruction en_US
dc.conference.name Computer Assisted Language Learning en_US
dc.conference.year 2020 en_US


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