Grammatical knowledge of english cleft constructions among Pakistani ESL learners across L2 proficiency levels and learning styles

The study examines the grammatical knowledge of English cleft constructions among Pakistani ESL learners across L2 proficiency levels and learning styles (field dependent/independent). Cleft constructions have been found to be problematic and difficult to master for Pakistani ESL learners, across proficiency levels and learning styles. Second language learners may encounter greater difficulty in forming cleft constructions (Chu et al., 2014; Chung & Shin, 2022; Wu & Ionin, 2022). Therefore, the study investigates the contribution of these students’ L2 proficiency and learning styles on their accurate judgement of cleft constructions. The study addresses the main research question: “To what extent are Pakistani ESL learners with different L2 proficiency levels and learning styles able to correctly judge English cleft constructions in the grammaticality judgment task?” The research employed the cross-sectional study design. A total sample of 390 respondents with different L2 proficiency levels and learning styles wasrecruited from the selected institutions of higher learning in Lahore, Pakistan, using stratified random sampling technique. The respondents were further classified in elementary, intermediate, and advanced L2 proficiency levels. There were 130 respondents including 65 field dependent, and 65 field independent in each L2 proficiency (Elementary, Intermediate, Advanced) level. Oxford Placement Test (OPT) and Group Embedded Figure Test (GEFT) were administrated among the respondents. Oxford Placement Test (OPT) was employed to determine the language proficiency levels of the respondents and Group Embedded Figure Test (GEFT) distinguished field-dependent and fieldindependent learning styles of the respondents. Target data were collected using Grammaticality Judgment Task (GJT) to measure respondents’ grammatical knowledge of cleft constructions. A two-way MANOVA was employed to examine a significant mean score difference of GJT across L2 proficiency levels and learning styles. The findings revealed a significant GJT mean score difference among L2 proficiency groups and between field-dependent/independent learners. The results also showed a significant main and interaction effect of Language proficiency and learning styles on GJT. Field-independent outperformed field-dependent learners on GJT total score, GJT grammatical, and GJT ungrammatical cleft constructions. The findings have interesting pedagogical implications. English language teachers and syllabus designers should design activities on cleft constructions used in the felicitous and infelicitous context for low proficiency learners

Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well‐formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large‐scale experiments using crowd‐sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state‐of‐the‐art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic