Degrees of difficulty to learn an additional language: the role of typological distinctions and linguistic distances between the languages involved
Organizers: Job Schepens, Freie University; Frans van der Slik, Radboud University; and R. van Hout, Radboud University
Transfer plays an important role in second language acquisition. It appears that humans can quickly perform quite well on new but similar tasks, such as learning an additional language that is similar to a previously learned language. In contrast, difficulty in learning a new language depends on the typological distinctions and the linguistic distance between the language involved.
New approaches are currently being developed that might present opportunities for
- Language testing institutions can provide access to language proficiency testing scores for learners with diverse language backgrounds and learning trajectories (age and exposure).
- Educational technology generates similarly large numbers of constructions from language learners with diverse backgrounds.
- Typological databases make it easier to quantify linguistic differences across many languages.
- The availability of computational tools for SLA research such as mixed effects modeling, NLP, and Bayesian modeling.
These innovations have resulted in new theoretical perspectives that quantify the roles of linguistic similarities or distances. Recent research suggests that the linguistic starting points of the learners determine aspects across all domains of language proficiency. Varying types of similarity also seem to have varying impacts on language learnability. This colloquium showcases new research on the different types of similarity and highlights the implications for additional language learning.
Capturing the role of L1 experience in L2 learning
Florian Jaeger, University of Rochester
An adult learner’s native language (L1) has a tremendous influence on the difficulty they experience when acquiring a second or
I will use a related, but simpler, learning problem—native speakers’ adaptation to an unfamiliar foreign accent—to demonstrate how Bayesian inference provides an effective way to model previous experience and its effect on learning. Computational models that implement Bayesian inference (ideal adapters, Kleinschmidt & Jaeger, 2015) allow us to make testable predictions about how a learners’ implicit knowledge (or in Bayesian terminology: beliefs) changes with exposure to unfamiliar input (e.g., from a novel language or an unfamiliar foreign accent), and how these changes are predicted to affect, for example, comprehension (Xie et al., in progress).
The advantages of explaining learners’ L2 Dutch language variation by means of L1-Ln lexical, morphological, and phonological distance measures
Job Schepens, Freie University; Frans van der Slik, Radboud University; and Roeland van Hout, Radboud University
We studied the impact of three L1 to additional language (Ln) Dutch distance measures on the speaking test scores of more than 50,000 adult learners of Dutch: lexical distance, morphological distance
The impact of the three distance measures on the acquisition of Dutch as an additional language was examined in immigrants from 49 mother tongue backgrounds, spoken in 74 countries, 20 of which were Indo-European (IE) and 13 non-Indo-European (non-IE). We found that the combination of lexical, morphological and phonological distance measures successfully yields an accumulative, unbiased, and fairly complete account of differences in Ln Dutch speaking test scores.
Linguistic typology and learnability in
Dora Alexopoulou (in collaboration with Xiaobin Chen and Ianthi Tsimpli), University of Cambridge
To obtain a dataset rich enough for the investigation of typological effects across developmental stages with significant learner numbers, we exploit advances in online learning technology. Specifically, we use the EF Cambridge Open Language Database (EFCAMDAT), an open access corpus consisting of L2 writings submitted to Englishtown, the online school of EF Education First, an international school of English as a foreign language. EFCAMDAT is an open access corpus standing out for its size, with 1.2 million scripts summing 71.8 million words. Available at http://corpus.mml.cam.ac.uk/efcamdat, it contains 128 distinct tasks across the proficiency spectrum drawing from learners across the globe (170 nationalities).
Our main research question is the impact of linguistic distance on the acquisition of L2 features that are absent from the L1. Specifically, we focused on whether there is evidence for typological effects on
The Hartshorne, Tenenbaum, and Pinker data revisited
Frans van der Slik, Roeland van Hout, Job Schepens & Theo Bongaerts, Radboud University
Based on the data of 2/3 million speakers of English, Hartshorne, Tenenbaum
We first came to the conclusion that their claim about the CPH is unwarranted and based on at least two fundamental analytical flaws. First, rather than making use of individual data, Hartshorne, Tenenbaum
We claim that the evidence supporting the CPH is an
Discussant: Prof. Scott Jarvis, University of Utah