Titlebook: Exploring the Interface Between Individual Difference Variables and the Knowledge of Second Language; Mirosław Pawlak Book 2021 The Editor

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https://doi.org/10.1007/978-3-319-91770-2ngs regarding the link between ID variables and the knowledge of English grammar as well as the ways in which relationships of this kind were investigated. The aim of the present chapter is to report the results of these studies and also to offer a discussion of such results, which will be further elaborated on in the conclusion.

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Individual Differences as Factors Mediating the Knowledge of L2 Grammar,cit (highly automatized) knowledge as well as issues involved in the measurement of different dimensions of such knowledge. The present chapter goes a step further by considering the role of individual difference factors in mediating the mastery of L2 grammar.

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Methodology of the Research Project,ing both the process and outcomes of L2 learning. Several caveats are in order at this point. First, most of the existing empirical evidence is related to the mastery of TL grammar, whether it is conceptualized globally in terms of overall attainment or with respect to the use of particular TL features or various sets of such features.

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Findings of the Research Project,ngs regarding the link between ID variables and the knowledge of English grammar as well as the ways in which relationships of this kind were investigated. The aim of the present chapter is to report the results of these studies and also to offer a discussion of such results, which will be further elaborated on in the conclusion.

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Book 2021 and implicit (or highly automatized) knowledge of the English passive voice. In doing so, the studysheds the so-much-needed light on the predictors of second language grammar knowledge but also, to some extent, on the usefulness of instructional techniques used to develop it.

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Hardwaree likely to be influenced, for example, by software availability, the wishes of donor agencies, established practice in universities and business institutions, pressure from manufacturers and, not infrequently, the nationality of the machines being considered. This chapter aims at answering three important questions related to such choices:

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Modelling Visit Similarity Using Click-Stream Data: A Supervised Approachearest neighbour (kNN) classification. Experimental results showing significant improvements over baseline algorithms based on Hidden Markov Model(HMM), support vector machine (SVM) and random forest are presented on two large real-world data sets.