Language Learning Following Immigration: Modeling Choices and Challenges
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No agent-based model exists of language learning following immigration to a new country. Language learning has features which make it a good fit to Agent Based Models (ABMs), such as diffusion/adoption effects: people learn language via social interaction and are influenced by other social actors about how and when to invest in learning. Language learning involves positive and negative feedback loops, such that poor progress early in learning can spiral into negativity and avoidance, while early success can accelerate learning. Most importantly, the question of why language learning is difficult for adults is controversial. Should implementers program into models the equations that match the robust age effects observed in data, or should these patterns emerge from multiple factors and actors? To address this, the large literature on foreign language acquisition was reviewed as part of the background of making modeling decisions. Decisions were sufficiently challenging that it was decided to begin with a narrative description, using the Overview, Design Concepts and Details protocol (ODD). The ODD protocol provided an organizing framework in which many details were worked out. These included identifying outcome variables (frequency of use and fluency in the two languages), basic entities (representing individuals, families, neighborhood, global environment), defining rules for initiating and continuing conversation, and rules for agents to move to new locations. Considerable narrative space was used to discuss the rationale for simplifications, as well as decisions that were left for future extensions. Given the complexity of the domain, the narrative description was a necessary foundation to smooth the way for a working simulation.
KeywordsLanguage learning Bilingualism Immigration Critical period Motivation Fluency ODD
- Allen, M., C. V. Goldman, and S. Zilberstein 2005. Language learning in multi-agent systems. In International joint conference on artificial intelligence, vol 19, 1649. Lawrence Erlbaum. Downloaded from http://rbr.cs.umass.edu/shlomo/papers/AGZijcai05.pdf.
- American Academy of Arts and Sciences. 2017. America’s languages: Investing in language education for the 21st century. Cambridge, MA: American Academy of Arts and Sciences.Google Scholar
- Caldwell-Harris, C.L., M. Staroselsky, S. Smashnaya, and N. Vasilyeva. 2012. Emotional resonances of bilinguals’ two languages vary with age of arrival: The Russian–English bilingual experience in the US. In Dynamicity in emotion concepts, ed. P. Wilson, 373–395. Frankfurt am Main: Peter Lang.Google Scholar
- Chomsky, N. 1965. Aspects of the theory of syntax. Cambridge, MA: MIT Press.Google Scholar
- DeKeyser, R. 2000. The robustness of critical period effects in second language acquisition. Studies in Second Language Acquisition 22: 499–533.Google Scholar
- DeVoretz, D.J., and C. Werner. 2000. A theory of social forces and immigrant second language acquisition, vol. 110. Bonn: Institute for the Study of Labor (IZA).Google Scholar
- Epstein, J.M. 2007. Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.Google Scholar
- Flege, J.E. 2018. L2 speech research: Time to change the paradigm. Paper presented at Stockholm University, June 2018. Downloaded from https://www.researchgate.net/publication/325895360_L2_speech_learning_Time_to_change_the_paradigm.
- Gleason, J.B. 1998. You can take it with you: Helping students maintain foreign language skills beyond the classroom. Englewood Cliffs: Prentice Hall.Google Scholar
- Hartshorne, J.K. 2018. Data: A critical period for second language acquisition: Evidence from 2/3 Million English Speakers. Retrieved from osf.io/pyb8s.Google Scholar
- Havrylov, S., and I. Titov. 2017. Emergence of language with multi-agent games: Learning to communicate with sequences of symbols. In Advances in neural information processing systems, 2149–2159.Google Scholar
- John, A. 2016. Dynamic models of language evolution: The economic perspective. In The Palgrave handbook of economics and language, ed. V. Ginsburgh and S. Weber, 101–120. London: Palgrave Macmillan.Google Scholar
- Klabunde, A., F. Willekens, S. Zinn, and M. Leuchter. 2015. An agent-based decision model of migration, embedded in the life course-model description in ODD+ D format (No. WP-2015-002). Rostock: Max Planck Institute for Demographic Research.Google Scholar
- Krashen, S.D. 1985. Inquiries & insights: Second language teaching: Immersion & bilingual education, literacy. Englewood Cliffs: Alemany Press.Google Scholar
- MacWhinney, B. 1997. Second language acquisition and the competition model. In Tutorials in bilingualism: Psycholinguistic perspectives, ed. A.M.B. de Groot and J.F. Kroll, 113–142. Mahwah: Lawrence Erlbaum Associates.Google Scholar
- ———. 2006a. Emergent fossilization. In Studies of fossilization in second language acquisition, ed. Z. Han and T. Odlin, vol. 14. Bristol: Multilingual Matters.Google Scholar
- ———. 2018. Attrition and the competition model. Downloaded from https://psyling.talkbank.org/years/2018/attrition-chapter.docx.
- Mordatch, I., and P. Abbeel. 2017. Emergence of grounded compositional language in multi-agent populations. Presented at The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Downloaded from arXiv preprint arXiv:1703.04908, https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17007/15846.
- Schumann, J.H. 1997. The neurobiology of affect in language. Malden: Blackwell.Google Scholar
- Snow, C. 1987. Relevance of the notion of a critical period to language acquisition. In Sensitive periods in development: Interdisciplinary perspectives, ed. M.H. Bornstein. Hillsale: Erlbaum.Google Scholar
- Pinker, S. 1994. The language instinct: How the mind creates language. New York: Harper Perennial Modern Classics.Google Scholar
- Vygotsky, L. 1978. Interaction between learning and development. Readings on the development of children 23 (3): 34–41.Google Scholar
- Wilensky, U., and W. Rand. 2015. An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge: MIT Press.Google Scholar