TBA

In this chapter, we focus on the challenges and considerations associated with the identification and verification of significant cut-off point in L2 developmental processes. Based on these considerations, we propose novel ways of detecting and estimating thresholds in association between continuous SLA-related variables. As we will also argue, apart from its analytical relevant, a clear concept(ualisation) of 'threshold' can provide a powerful tool for informing educational policy and intervention strategies. It also sheds light on the importance of contextual variety for SLA across the lifespan.

This study is the first to examine predictors of oral and written second language (L2) chunking development among older adults over time. Based on a two-year longitudinal design with time-series data, we set out to determine how and when individual differences in prior L2 exposure and working memory (WM) predict the frequency and association strength of adjective–noun bigrams in both the written and oral modes. We analyzed L2 chunking development among 43 older adult learners aged 61–70 who completed a two-year L2 English course in Switzerland (first language German), each of whom participated in 33 consecutive data collection waves over the instructional period. Unsurprisingly, high-frequency bigrams dominated oral speech compared to written texts, but exclusive constituent words (measured via Mutual Information [MI] scores) were not more prevalent in one mode than the other. Generalized additive modeling revealed that prior L2 exposure was only a weak predictor of chunking development, while complex WM emerged as the strongest predictor, particularly for written production, where learners with higher WM capacity produced more frequent and tightly associated bigrams. We also observed a temporally limited effect of complex WM, which supported collocational exclusivity early in training but later reversed, suggesting shifting cognitive involvement as learning progressed. This study offers timely insights into how a long-neglected demographic acquires and utilizes formulaic language and which individual learner differences contribute when to L2 chunking development.

Recent years have seen a marked increase in the use of time-intensive longitudinal designs in applied linguistics, particularly in second language acquisition (SLA) research, where individual developmental trajectories have become inferential targets in their own right. Generalized additive mixed-effects models (GAMMs) have emerged as a powerful tool for modeling between- and within-person variation, for disentangling linear from nonlinear relationships, and for assessing the predictive power of (relatively) static (e.g., gender, educational attainment) alongside time-varying (e.g., socioaffect, cognition) predictors on developmental pathways. Setting our tutorial apart from other GAMM resources in computational linguistics, phonetics, and sociolinguistics, we present the first in-depth application of GAMMs to identify and explain periods of significant change (growth or decline) in longitudinal datasets with repeated measurements. We draw on a novel micro-development study comprising 43 older adult L2 learners who completed a battery of L2, socioaffective, and cognitive tasks in 30 consecutive waves across a period of two years (i.e., 30 measures per participant per task). Our contribution guides readers through the computational steps to identify periods of statistically significant change. We then illustrate how findings can be interpreted and supplemented with both qualitative introspective data and quantitative measures of individual learner differences. While our tutorial focuses on SLA research, the methods are applicable to any number of disciplines in the social and natural sciences where developmental patterns are of direct inferential interest.