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      Machine Learning and Knowledge Extraction : Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018, Proceedings 

      The Impact of Corpus Linguistics on Language Teaching in Russia’s Educational Context: Systematic Literature Review

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          The Sketch Engine: ten years on

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            A Systematic Analysis and Synthesis of the Empirical MOOC Literature Published in 2013–2015

            A deluge of empirical research became available on MOOCs in 2013–2015 and this research is available in disparate sources. This paper addresses a number of gaps in the scholarly understanding of MOOCs and presents a comprehensive picture of the literature by examining the geographic distribution, publication outlets, citations, data collection and analysis methods, and research strands of empirical research focusing on MOOCs during this time period. Results demonstrate that (a) more than 80% of this literature is published by individuals whose home institutions are in North America and Europe, (b) a select few papers are widely cited while nearly half of the papers are cited zero times, and (c) researchers have favored a quantitative if not positivist approach to the conduct of MOOC research, preferring the collection of data via surveys and automated methods. While some interpretive research was conducted on MOOCs in this time period, it was often basic and it was the minority of studies that were informed by methods traditionally associated with qualitative research (e.g., interviews, observations, and focus groups). Analysis shows that there is limited research reported on instructor-related topics, and that even though researchers have attempted to identify and classify learners into various groupings, very little research examines the experiences of learner subpopulations.
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              Corpus Use in Language Learning: A Meta-Analysis

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                Book Chapter
                2020
                July 10 2020
                : 339-355
                10.1007/978-3-030-50513-4_26
                ffac3e21-7414-435c-96a1-7d2747e7e9b8
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