LEARNING CONDITION, LINGUISTIC COMPLEXITY, AND FIRST LANGUAGE TRANSFER IN SEMIARTIFICIAL LANGUAGE LEARNING

Abstract

AbstractThis study explored the interaction between learning conditions, linguistic complexity, and first language (L1) syntactic transfer in semiartificial grammar learning by conceptually replicating and extending Tagarelli et al. (2016). We changed the L1 background, elicited production data during debriefing, and added a binary mixed-effects logistic regression analysis to compare variability at learner and item levels with group-level variation on exposure condition, linguistic complexity, and their interaction. Our results replicated those of the original study regarding the comparative efficacy of explicit instruction; however, we also found a condition × complexity interaction absent in the original study. Debriefing sentence-production data suggest that the changed L1-L2 typological distance may have leveled off the advantage of explicit instruction in the learning of the complex V2-VF structure. Finally, our mixed-effects modeling analysis revealed that variability at learner and item levels accounted for a larger proportion of the variance of the outcomes than all the predictors combined.