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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 15 of 15 1 sj-ipynb-3-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 2 Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 3 sj-ipynb-3-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 4 Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 5 sj-csv-2-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 6 sj-csv-1-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 7 sj-csv-2-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 8 sj-csv-1-ltr-10.1177_13621688211020412 for Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items ... Naismith, Ben; Juffs, Alan. - : SAGE Journals, 2021 BASE Show details 9 A Corpus Study of the English Suffixes -ness and -acy: Productivity, Genre, and Implications for L2 Learning Kanwit, Matthew; Naismith, Ben. - : University of New Brunswick, 2021. : Érudit, 2021 BASE Show details 10 A Corpus Study of the English Suffixes -ness and -acy: Productivity, Genre, and Implications for L2 Learning Naismith, Ben; Kanwit, Matthew. - 2021 BASE Show details 11 Multiword sequences in L2 English language learners’ speech: The relationship between trigrams and lexical variety across development. Vercellotti, Mary Lou; Juffs, Alan; Naismith, Ben. - : Elseiver, 2021 BASE Show details 12 A Corpus Study of the English Suffixes -ness and -acy: Productivity, Genre, and Implications for L2 Learning Naismith, Ben; Kanwit, Matthew In: Canadian Journal of Applied Linguistics; Vol. 24 No. 1 (2021); 115-137 ; Revue canadienne de linguistique appliquée; Vol. 24 No. 1 (2021); 115-137 ; 1920-1818 ; 1481-868X (2021) BASE Show details 13 The University of Pittsburgh English Language Institute Corpus (PELIC) ... Juffs, Alan; Han, Na-Rae; Naismith, Ben. - : Zenodo, 2020 BASE Show details 14 Accurate Measurement of Lexical Sophistication in ESL with Reference to Learner Data Naismith, Ben; Han, Na-Rae; Juffs, Alan; Hill, Brianna; Zheng, Daniel. - : International Educational Data Mining Society (IEDMS), 2018 Abstract: One commonly used measure of lexical sophistication is the Advanced Guiraud (AG; [9]), whose formula requires frequency band counts (e.g., COCA; [13]). However, the accuracy of this measure is affected by the particular 2000-word frequency list selected as the basis for its calculations [27]. For example, possible issues arise when frequency lists that are based solely on native speaker corpora are used as a target for second language (L2) learners (e.g., [8]) because the exposure frequencies for L2 learners may vary from that of native speakers. Such L2 variation from comparable native speakers may be due to first language (L1) culture, home country teaching materials, or the text types which L2 learners commonly encounter. This paper addresses the aforementioned problem through an English as a Second Language (ESL) frequency list validation. Our validation is established on two sources: (1) the New General Service List (NGSL; [4]) which is based on the Cambridge English Corpus (CEC) and (2) written data from the 4.2 million-word Pitt English Language Institute Corpus (PELIC). Using open-source data science tools and natural language processing technologies, the paper demonstrates that more distinct measurable lexical sophistication differences across levels are discernible when learner-oriented frequency lists (as compared to general corpora frequency lists) are used as part of a lexical measure such as AG. The results from this research will be useful in teaching contexts where lexical proficiency is measured or assessed, and for materials and test developers who rely on such lists as being representative of known vocabulary at different levels of proficiency. This research applies data-driven exploration of learner corpora to vocabulary acquisition and pedagogy, thus closing a loop between educational data mining and classroom applications. URL: http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018\_paper\_37.pdf http://d-scholarship.pitt.edu/40665/ BASE Hide details 15 Integrating corpus tools on intensive CELTA courses Naismith, Ben. - : Oxford University Press, 2016 BASE Show details

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