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Engagement, a psychological individual difference variable with three facets (vigour, dedication and absorption), has recently attracted scholarly attention. Through a large-scale survey, we examined what we call ‘L2 engagement’ among 21,370 secondary school students in China, with an L2 engagement scale adapted from the Utrecht Work Engagement Scale (UWES)-student version. Factor analysis showed this scale to be empirically unidimensional with three highly intercorrelated facets and very high internal consistency; this contributes to our understanding of the conceptual challenges surrounding the construct of engagement (e.g., dimensionality) and the broader issue concerning the correspondence between empirical constructs and theoretical terms (e.g., engagement in our case). Hierarchical regression revealed that the selected sociobiographical variables (e.g., L2 proficiency) were linked to L2 engagement to varying degrees; adopting a more refined approach to gauge the unique contribution of a predictor to L2 engagement in hierarchical regression, we identified L2 proficiency, parental attention, study time and frequency of parental coaching as (very) important predictors for L2 engagement. We call for more studies to adopt our L2 engagement scale, a sufficiently valid and reliable instrument developed based on a large sample. We also propose a few future research directions (e.g., combining self-reports with other data sources).

Aims and objectives: This study explores the effect of cognitive load on code-switching (CS), by examining whether an increase in cognitive load can lead to a different amount and/or pattern of CS use, and whether there is an interplay between the effect of cognitive load and the effect of social factors (i.e., language and use, and attitudes towards CS). Methodology: Thirty-one Chinese-English bilinguals participated in a picture recall experiment consisting of three sessions, with an incremental increase in cognitive load which was achieved by manipulating the number of attentional targets that should be attended to. The increase in cognitive load was validated by a self-report survey adapted from Paas. Information on social variables was collected by a language use questionnaire. Data and analysis: The amount and pattern (intraclausal vs interclausal) of CS were coded and quantified. One-way analysis of variance (ANOVA) was performed to compare the within-participant use of CS across the sessions. Hierarchal regression analyses were also conducted to examine how cognitive load and the social variables of interest predicted the between-participant variation of CS in each session. Findings/conclusions: The results showed that the participants used significantly less intraclausal CS in Session 3, in which they reported the highest degree of cognitive load. In addition, the results of hierarchical regression analyses for overall CS use in Session 2 confirmed the significant effect of cognitive load. The influence of attitudes towards CS was also shown to be another significant predictor with large effect size. However, in the more demanding Session 3, none of the factors of interest could predict between-participant variation in CS use. Originality: This study is among the first to examine how cognitive load affects nonlaboratory CS. Significance/implications: This study argues for a recognition of the cognitive processing basis of socially driven language use, linking sociolinguistic and psycholinguistic perspectives on the use of CS.

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