Educational Technology Research and Development

, Volume 63, Issue 5, pp 671–690 | Cite as

Second language acquisition of Mandarin Chinese vocabulary: context of learning effects

  • Yu-Ju Lan
  • Shin-Yi Fang
  • Jennifer Legault
  • Ping LiEmail author
Research Article


In an increasingly multilingual world, it is important to examine methods that may lead to more efficient second language learning, as well as to analyze the mechanisms by which successful learning occurs. The purpose of the current study was to investigate how different learning contexts can impact the learning of Mandarin Chinese as a second language. Two contexts [virtual environment (VE) vs. traditional learning environment] were compared and examined from cognitive and linguistic perspectives. Thirty-one monolingual English speakers participated in a training study consisting of seven learning and testing sessions, followed by one additional sessions of delayed post-testing. The participants’ behavioral performances with regard to accuracy, reaction time, and exposure were collected and analyzed. Through analyses of variance and mixed-effects modeling, the current study shows that the learning trajectory of the participants in the VE showed a larger acceleration than that of those in the traditional learning context, which suggests that simulated embodied experience in the VE may have aided in the processing of a second language, especially with regard to enhancing the learning trajectory in short-term second language training.


Virtual worlds Mandarin Chinese Embodied cognition Contextual immersion Second language learning Vocabulary acquisition 



We would like thank Yu-Ting Hsiao, Yu-Hsuan Kan, Indy Majere, and Luis Tzeng for their assistance with constructing the VEs in Second Life, and Karishma Kodia, Sarah Newby, Evan Oliver, Shinmin Wang for their assistance with running the experiment. The research was supported by funds from the Aim for Top University Office of the National Taiwan Normal University, the Joint Advanced Center for the Study of Learning Sciences (MOST 104-2911-I-003-301), and the US National Science Foundation (BCS-1338946).


  1. Abercrombie, S. (2011). Examining the influence of seductive details in case-based instruction on pre-service teachers’ learning and learning performances. Albuquerque, NM: The University of New Mexico.Google Scholar
  2. Aziz-Zadeh, L., & Damasio, A. (2008). Embodied semantics for actions: Findings from functional brain imaging. Journal of Physiology, 102, 35–39.Google Scholar
  3. Baayen, R. H. (2004). Statistics in psycholinguistics: A critique of some current gold standards. Mental Lexicon Working Papers, 1, Edmonton, 1–45.Google Scholar
  4. Baayen, R. H. (2008). Analyzing linguistic data: A practical introduction to statistics using R. New York: Cambridge University Press.CrossRefGoogle Scholar
  5. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.CrossRefGoogle Scholar
  6. Baddeley, A. (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience, 4, 829–837.CrossRefGoogle Scholar
  7. Barcroft, J. (2004). Second language vocabulary acquisition: A lexical input processing approach. Foreign Language Annals, 37(2), 200–208.CrossRefGoogle Scholar
  8. Barr, D. J. (2008). Analyzing ‘visual world’ eyetracking data using multilevel logistic regression. Journal of Memory and Language, 59, 457–474.CrossRefGoogle Scholar
  9. Barr, D., Levy, R., Scheepersm, C., & Tily, H. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278.CrossRefGoogle Scholar
  10. Barsalou, L. W. (2008). Grounded cognition. The Annual Review of Psychology, 59, 617–645.CrossRefGoogle Scholar
  11. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). _lme4: Linear mixed-effects models using Eigen and S4_. R package version 1.1-7.
  12. Borghi, A. M., Glenberg, A. M., & Kaschak, M. P. (2004). Putting words in perspective. Memory & Cognition, 32(6), 863–873.CrossRefGoogle Scholar
  13. Brown, H. D. (2001). Teaching by Principles: An Interactice Approach to Language Pedagogy (2nd ed.). San Fransisco: Longman.Google Scholar
  14. Buccino, G., Riggio, L., Melli, G., Binkofski, F., Gallese, V., & Rizzolatti, G. (2005). Listening to action-related sentences modulated the activity of the motor system: A combined TMS and behavioral study. Cognitive Bran Research, 24, 355–363.CrossRefGoogle Scholar
  15. Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359.CrossRefGoogle Scholar
  16. Cobb, T. (2007). Computing the vocabulary demands of L2 reading. Language Learning & Technology, 11(3), 38–63.Google Scholar
  17. Cohen, A. D., & Aphek, E. (1980). Retention of second language vocabulary over time: Investigating the role of mnemonic associations. System, 8, 221–235.CrossRefGoogle Scholar
  18. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Erlbaum.Google Scholar
  19. Cowart, M. (2005). Embodied cognition. Accessed 23 April 2014.
  20. Development Core Team, R. (2004). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  21. Dixon, P. (2008). Memory and Language Models of accuracy in repeated-measures designs. Journal of Memory and Language, 59, 447–456.CrossRefGoogle Scholar
  22. Fang, S., Legault, J., Lan, Y., & Li, P. (2015). Neural correlates of short-term second language training: Context of learning effects (under review).Google Scholar
  23. Forster, K. I., & Dickinson, R. G. (1976). More on the language-as-fixed-effect fallacy: Monte carlo estimates of error rates for F1, F2, F’, and minF’. Journal of Verbal Learning and Verbal Behavior, 15, 135–142.CrossRefGoogle Scholar
  24. Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.Google Scholar
  25. Glenberg, A. M., & Kaschak, M. P. (2002). Grounding language in action. Psychonomic Bulletin & Review, 9(3), 558–565.CrossRefGoogle Scholar
  26. Harp, S. F., & Mayer, R. E. (1997). The role of interest in learning from scientific text and illustrations: On the distinction between emotional and cognitive interest. Journal of Educational Psychology, 89(1), 92–102.CrossRefGoogle Scholar
  27. Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414–434.CrossRefGoogle Scholar
  28. Hauk, O., Johnsrude, I., & Pulvermueller, F. (2004). Somatotopic representation of action words in human motor and premotor cortex. Neuron, 41, 301–307.CrossRefGoogle Scholar
  29. Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59, 434–446.CrossRefGoogle Scholar
  30. Juffs, A., & Harrington, M. (2011). Aspects of working memory in L2 learning. Language Teaching, 44, 137–166. doi: 10.1017/S0261444810000509.CrossRefGoogle Scholar
  31. Kern, R. G. (1989). Second language reading strategy instruction: Its effects on comprehension and word inference ability. The Modern Language Journal, 73(2), 135–149.CrossRefGoogle Scholar
  32. Krashen, S. D. (1982). Principles and practice in second language acquisition. Prentice-Hall International.Google Scholar
  33. Kroll, J. F., & Curley, J. (1988). Lexical memory in novice bilinguals. The role of concepts in retrieving second language words. In M. Grunenberg, P. Morris, & R. Sykes (Eds.), Practical aspects of memory (2nd ed., pp. 389–395). London: Wiley.Google Scholar
  34. Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B. (2014). lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R packageversion 2.0-11.
  35. Lan, Y. J. (2013). The effect of technology-supported co-sharing on L2 vocabulary strategy development. Educational Technology & Society, 16(4), 1–16.Google Scholar
  36. Lan, Y. J. (2014). Does Second Life improve Mandarin learning by overseas Chinese students? Language Learning & Technology, 18(2), 36–56.Google Scholar
  37. Lan, Y.-J., Kan, Y.-H., Hsiao, I. Y. T., Yang, S. J. H., & Chang, K.-E. (2013). Designing interaction tasks in Second Life for Chinese as a foreign language learners: A preliminary exploration. Australasian Journal of Educational Technology, 29(2), 184–202.Google Scholar
  38. Lan, Y.-J., Kan, Y. H., Sung, Y. T., & Chang, K. E. (2nd revision). Oral-performance language tasks for CSL beginners in Second Life. Language Learning & Technology (under review).Google Scholar
  39. Li, P. (2015). Bilingualism as a dynamic process. In B. MacWhinney & W. O’Grady (Eds.), Handbook of language emergence (pp. 511–536). Hoboken: John Wiley.Google Scholar
  40. Li, P., Zhang, F., Tsai, E., & Puls, B. (2014). Language History Questionnaire (LHQ 2.0): A new dynamic web-based research tool. Bilingualism: Language and Cognition, 17, 673–680.CrossRefGoogle Scholar
  41. Mayer, R. E., & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and Instruction, 12, 107–119.CrossRefGoogle Scholar
  42. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.CrossRefGoogle Scholar
  43. McCulloch, C. E., & Neuhaus, J. M. (2011). Misspecifying the shape of a random effects distribution: why getting it wrong may not matter. Statistical Science, 26(3), 388–402.CrossRefGoogle Scholar
  44. Meara, P. (1982). Vocabulary acquisition: A neglected aspect of language learning. In V. Kinsella (Ed.), Surveys I: Eight state-of-the-art articles on key areas in language teaching (pp. 100–126). Cambridge: Cambridge University Press.Google Scholar
  45. Park, B., Moreno, R., Seufert, T., & Brünken, R. (2011). Does cognitive load moderate the seductive details effect? A multimedia study. Computers in Human Behavior, 27, 5–10.CrossRefGoogle Scholar
  46. Peterson, M. (2011). Towards a research agenda for the use of three-dimensional virtual worlds in language learning. CALICO Journal, 29(1), 67–80.CrossRefGoogle Scholar
  47. Peterson, M. (2012). Learner participation patterns and strategy use in Second Life: an exploratory case study. ReCALL, 22(3), 273–292.CrossRefGoogle Scholar
  48. Posner, M. I., & Snyder, C. R. (1975). Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition: the Loyola symposium. L. Erlbaum Associates: Hillsdale.Google Scholar
  49. Prince, P. (1996). Second language vocabulary learning: The role of context versus translations as a function of proficiency. The Modern Language Journal, 80(4), 478–493.CrossRefGoogle Scholar
  50. Proctor, C. P., Carlo, M., August, D., & Snow, C. (2005). Native Spanish-speaking children reading in English: Toward a model of comprehension. Journal of Educational Psychology, 97(2), 246–256.CrossRefGoogle Scholar
  51. Rey, G. D. (2012). A review of research and a meta-analysis of seductive detail effect. Educational Research Review, 7(3), 216–237.CrossRefGoogle Scholar
  52. Rueda, Y. T. (2006). Developing pragmatic competence in a foreign language. Colombian Applied Linguistics Journal, 8, 169–182.Google Scholar
  53. Rueschemeyer, S. A., Lindemann, O., van Rooij, D., van Dam, W., & Bekkering, H. (2010). Effects of intentional motor actions on embodied language processing. Experimental Psychology, 57(4), 260–266.CrossRefGoogle Scholar
  54. Sanchez, C. A., & Wiley, J. (2006). An examination of the seductive details effect in terms of working memory capacity. Memory & Cognition, 34(2), 344–355.CrossRefGoogle Scholar
  55. Schneider, W., Eschman, A., & Zuccolotto, A. (2012). E-Prime User’s Guide. Pittsburgh: Psychology Software Tools Inc.Google Scholar
  56. Snyder, P. J., & Harris, L. J. (1993). Handedness, sex, and familial sinistrality effects on spatial tasks. Cortex, 29(1), 115–134.CrossRefGoogle Scholar
  57. Smidt, E., & Hegelheimer, V. (2004). Effects of online academic lectures on ESL listening comprehension, incidental vocabulary acquisition, and strategy use. Computer Assisted Language Learning, 17(5), 517–556.CrossRefGoogle Scholar
  58. Snow, M. A. (2005). A model of academic literacy for integrated language and content instruction. In E. Hinkel (Ed.), Handbook of research in second language teaching and learning (pp. 693–712). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  59. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  60. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312.CrossRefGoogle Scholar
  61. Sweller, J. (2010). Element interactivity and intrinsic, extraneous and germane cognitive load. Educational Psychology Review, 22, 123–138.CrossRefGoogle Scholar
  62. Sweller, J., & Chandler, P. (1994). Why some materials is difficult to learn. Cognition and Instruction, 12(3), 185–233.CrossRefGoogle Scholar
  63. Thorne, S. L., Fischer, I., & Lu, X. (2012). The semiotic ecology and linguistic complexity of an online game world. ReCALL, 24(3), 279–301.CrossRefGoogle Scholar
  64. Upal, M. A., Gonce, L. O., Tweney, R. D., & Slone, D. J. (2007). Contexualizing counterintuitiveness: How context affects comprehension and memorability of counterintuitive concepts. Cognitive Science, 31, 415–439.CrossRefGoogle Scholar
  65. Van Selst, M., & Jolicœur, P. (1994). A solution to the effect of sam-ple size on outlier elimination. Quarterly Journal of Experimental Psychology, 47A(3), 631–650.CrossRefGoogle Scholar
  66. Wechsler, D. (1997). WAIS-III administration and scoring manual. San Antonio, TX: Psychological Corporation.Google Scholar
  67. Willems, R. M., & Casasanto, D. (2011). Flexibility in embodied language understanding. Frontiers in Psychology, 2, 116.Google Scholar
  68. Yang, J., & Li, P. (2012). Brain networks of explicit and implicit learning. PLoS ONE, 7, e42993. doi: 10.1371/journal.pone.0042993.CrossRefGoogle Scholar
  69. Zwaan, R. A., Stanfield, R. A., & Yaxley, R. H. (2002). Language comprehenders mentally represent the shapes of objects. Psychological Science, 13, 168–171.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2015

Authors and Affiliations

  • Yu-Ju Lan
    • 1
  • Shin-Yi Fang
    • 2
  • Jennifer Legault
    • 2
  • Ping Li
    • 2
    Email author
  1. 1.Department of Applied Chinese Language and CultureNational Taiwan Normal UniversityTaipeiTaiwan
  2. 2.Department of Psychology and Center for Brain, Behavior, and CognitionPennsylvania State UniversityUniversity ParkUSA

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