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part 2 researching language as a complex adaptive system


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Lecture 2 for researching language as a complex adaptive system. complete. by M Phil English (scholars)

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part 2 researching language as a complex adaptive system

  3. 3. BACKGROUND • Chaos/complexity theory • Developed from ancient Greek philosophy • Modern views of mathematics and the physical world • Explains the nature and characteristics of complex systems • Defines different types of change
  4. 4. PROGRESSING IDEAS • Explores patterns of nonlinearity • Unpredictability in a complex system • Avoids marking this as a hybrid state • The bridging role of complexity theory • Framing research and practice
  5. 5. CHAOS • 'To some physicists chaos is a science of process rather than state, of becoming rather than being' (Gleick 1987: 5)
  6. 6. A COMPLEX SYSTEM • Emerges from the interactions of its components. • (LARSEN–FREEMAN & CAMERON,2008)
  7. 7. COMPLEX SYSTEMS • Often heterogeneous, being made up of both agents and elements. (LARSEN–FREEMAN & CAMERON,2008)
  8. 8. APPLIED LINGUISTIC COMPLEX SYSTEMS • Contain many subsystems, nested one within another. (LARSEN–FREEMAN & CAMERON,2008)
  9. 9. COMPLEX SYSTEMS AT ALL LEVELS AND TIME SCALES • From the social level to the individual levels. • Milliseconds of neural connections • Millennia of evolution etc • (LARSEN–FREEMAN & CAMERON,2008) • va
  10. 10. LARSEN-FREEMAN’S STANCE (1997) • Learning linguistic items is not a linear process— learners do not master one item and then move on to another. In fact, the learning curve for a single item is not linear either. (p. 151) • There are no natural divisions or end points in the overall learning process; it is continuous but erratic and the target is a moving one (Larsen-Freeman, 1997).
  11. 11. LEARNING • ―We can neither claim that learning is caused by environmental stimuli (the behaviourist position) nor that it is genetically determined (the innatist position). Rather, learning is the result of complex (and contingent) interactions between individual and environment‖. • (Van Lier ,1996:170)
  12. 12. LARSEN-FREEMAN VIEWS LANGUAGE • A dynamic system • Emerges and self-organizes from frequently occurring patterns of language use" (p. 111) • The product of multiple, patterned, and non-linear integrated contexts and times • Maintains an identity(social and national) in the face of constant change • CAS Attempts to keep language in a state of status quo in order to keep its standard
  14. 14. FEATURES OF COMPLEX NONLINEAR SYSTEMS Adaptive Feedback sensitive Sensitive to initial conditions
  15. 15. UNPREDICTABILITY • The weather is constantly changing • Also stays within the boundaries of climate. • The climate 'We can tell where the system cannot be, and we can identify the states that the system is most likely to be, but we cannot tell exactly where the system will be' (Mohanan 1992:650)
  16. 16. SENSITIVE TO INITIAL CONDITIONS • UG the initial condition of human language • —it contains certain substantive universal principles that apply to constrain the shape of human languages • For instance, there are a small number of core phonological patterns that apply to all languages, e g voicing assimilation of obstruent in all languages • (Mohanan ,1992)
  17. 17. EXAMPLE FROM ENGLISH LANGUAGE • Languages also differ; In English, the voiced consonant assimilates to the voiceless (Salzmann, 2004) • , whereas in Spanish and Russian, the first consonant assimilates to the second regardless of the voicing feature • (Lombardi,1996) • Alan Hewat's novel Lady's Time (1985)
  18. 18. (DST) AS NONLINEAR CHANGE • Usually no straightforward linear cause-effect relationships where increased input leads to a proportionate increase in the output (e.g. the higher the motivation, the higher the achievement)
  19. 19. BUTTERFLY EFFECT • A huge input can sometimes result in very little or no impact, while at others even a tiny input can lead to what seems like a disproportionate ‗explosion‘ • The system‘s behavioural outcome depends on the overall constellation of the system components (Dornyei,2011)
  20. 20. BUTTERFLY EFFECT IN LANGUAGE LEARNING • The various interlinked components of the system can moderate the impact of any input(both in a positive and negative way ). • (Dornyei,2011)
  21. 21. LANGUAGE IS ALSO COMPLEX • Satisfies both criteria of complexity • First, it is composed of many different subsystems phonology, morphology, lexicon, syntax, semantics, pragmatics • Second, the subsystems are interdependent ,a change in any one of them can result in a change in the others • (Larsen-Freeman 1989, 1991b, 1994)
  22. 22. LARSEN-FREEMAN AND CAMERON (2008:96) • First and second languages are both live complex systems which change over time. • ―we change a language by using it‖.
  23. 23. FUNCTIONS OF L1 AND L2 • Work as attractors. • An attractor is ―a region of a system into which the system tends to move‖ (Larsen-Freeman and Cameron 2008:50) • Language development swings between these two poles. • The language learner is attracted or repelled by one of these poles and out of this cycle of attraction and repulsion emerges a third element, namely, interlanguage.
  24. 24. INTER-LANGUAGE AS A STRANGE ATTRACTOR • Highly sensitive to initial conditions. • Small changes in the initial conditions result in unpredictable shifts in language development.
  25. 25. BYBEE’S, VIEW (2006) • The fact is that language forms are being continually transformed by use . • Any linguistic representation in the learner‘s mind is strongly tied to the experience that a speaker has had with language • May bear little resemblance to forms that NSs employ or that fit linguists‘ categories.
  26. 26. POINT OF DIFFERENCE The behavior of the whole emerges out of the interaction of the subsystems. Thus, describing each subsystem tells us about the subsystems, it does not do justice to the whole of language. (Larsen-Freeman and Cameron ,2008)
  27. 27. LANGUAGE DEVELOPMENT AS DYNAMIC • Real-time language processing, developmental change in learner language, and evolutionary change in language are all reflections of the same dynamic process of language usage • ( Bybee, 2006; Larsen-Freeman, 2003; Smith & Thelen, 1993).
  28. 28. MAIN STANDPOINT • Researchers' grammars containing static rules do not do justice to the everchanging character of learners' internal L2 grammars.
  29. 29. CHANGES FROM TRADITIONAL RESEARCH • (1)The Nature of Hypotheses • Many complex systems are interconnected and coordinated • Not always possible to explain behavior, and changes in behavior, by detailing their separate components and roles (Clark ,1997) • Prediction (or forecasting (Traditional) • Retrodiction (or retrocasting (CAS)
  30. 30. EXAMPLE GIVEN BY BAK (1997) • Our explanation of sand pile avalanches is expressed in terms of the structure and stability of the sand pile, rather than in terms of the behavior of individual grains of sand.
  31. 31. CHANGES FROM TRADITIONAL RESEARCH • (2)Causality • In the traditional reductionist scenario, the researcher searches for a critical ―element whose removal from a causal chain would alter the outcome‖ (Gaddis, 2002, p.54) • ―Death to the variable‖(Byrne,2002) • Instead of investigating single variables, we study modes of system change that include selforganization and emergence. • Emergent properties or phenomena occur when change on one level of social grouping or on the timescale of a system leads to a new mode on another level or timescale.
  32. 32. CAMERON AND DEIGNAN (2006) EXAMPLE • The phrase emerged fairly recently in English • Influenced by social changes and language uses. • Emergence could not have been predicted using the usual definition of prediction. • The genealogy of such phrases can be studied and their origin can sometimes be explained in retrospect.
  33. 33. CHANGES FROM TRADITIONAL RESEARCH • The Process of Co-adaptation • In first language learning Dynamic alteration in both; child and the caretaker language and behaviours • In classroom between teacher and the students • The structure emerges; the lesson • Multi subsystems at students individual levels • Emerge new language resources
  34. 34. CHANGES FROM TRADITIONAL RESEARCH • No Single Independent Variable • The relationships are reciprocal but not symmetrical • A web of interacting components • Entertain Supportive, Competitive and conditional relationships (Van Geert and Steenbeek,in press,p:9) • Everything is connected to some way to everything else (Gaddis,2002,p.64)
  35. 35. CHANGES FROM TRADITIONAL RESEARCH • Stability and Variability • A complex system even in a stable mode(attractor) • Still continuously changing • Change occurs in their constituents or agents • In their interaction. • Stability is not stasis
  36. 36. CHANGES FROM TRADITIONAL RESEARCH • The Changed Nature of Context • Context includes Physical, social, cultural, and cognitive perspectives ; inseparable from the system • Soft assembly • Learner /learning and the context are inseparable while explaining and measuring them
  37. 37. CHANGES FROM TRADITIONAL RESEARCH • Nested levels and Timescales • Systems exist at different levels • From macro levels to micro levels • Interconnected • Systems operate at different timescales • From milliseconds of neural processing to the minutes and hours of classroom learning
  38. 38. METHODOLOGICAL PRINCIPLES FOR RESEARCHING LANGUAGE AND LANGUAGE DEVELOPMENT 1) Ecologically valid, Including context 2) Avoidance of reductionism but up to practical level 3) Keeping self-organisation, feedback, and emergence central while thinking in terms of dynamic processes and changing relationships 4) Reciprocal causality rather than simple cause-effect links
  39. 39. METHODOLOGICAL PRINCIPLES FOR RESEARCHING LANGUAGE AND LANGUAGE DEVELOPMENT 5) Coadaptation and soft assembly process rather than dualistic thinking 6) Rethinking units of analysis ,identifying collective variables 7) Avoidance of conflating levels and timescales; seeks linkages across levels and time scales, heterochronical thinking 8) Focus variability in particular and stability in general to understand development
  40. 40. Research Methodologies Attempts to investigate the potential of a system rather than its state To describe the inter-connected web of factors influencing change Investigate processes of coadaptation in response to changed pedagogical goals
  41. 41. Research Methodologies Ethnography “attempt to honor the profound wholeness and situatedness of social scenes and individuals-in-the-world” (Atkinson, 2002, p.539), by studying real people in their human contexts and interactions Ethnography is itself a complex adaptive system, that evolves and adapts as the researcher uses it (Agar ,2004)
  42. 42. Agar suggested: ―ethnography is a fractal generating process. What ethnographers are looking for are processes that apply iteratively and recursively at different levels to create patterns, variations that emerge from adaptation to contingencies and environment.‖
  43. 43. RESEARCH METHODOLOGIES • Formative Experiments • a formative experiment focuses on the dynamics of implementation,using the ideas of soft assembly and co-adaptation. (Jacob, 1992, as cited in Reinking & Watkins, 2000). • ―In a formative experiment, the researcher sets a pedagogical goal and finds out what it takes in terms of materials, organization, or changes in the intervention in order to reach the goal‖ (Newman, 1990, as cited in Reinking & Watkins, 2000, p. 388).
  44. 44. NEO-VYGOTSKYAN IDEA • To describe the inter-connected web of factors influencing change • Attempts to investigate the potential of a system rather than its state • Investigate processes of coadaptation in response to changed pedagogical goals
  45. 45. DESIGN BASED EXPERIMENTS/RESEARCHER 1. Focuses learning processes(Lobato,2003) 2.Does not follow some experimental treatment protocol rather –for example-studies the learning environment overtime, collects evidence of the effects of variations and feeds it recursively for the future design(Barab ,2006)
  46. 46. ACTION RESEARCH • Concerned with possibility rather prediction • Choosing problem • Application of Lewinian Cycle (Baskerville and Wood – Harper,1996) • Deeper understanding of the system‟s dynamics • Application of Lewinian cycle
  47. 47. LEWINIAN CYCLE Diganosing Specifying Learning Evaluating Action Planning Taking Actions
  48. 48. LONGITUDINAL, CASE-STUDY, TIMESERIES APPROACH • enables connections to be made across levels and timescales. In contrast, often interlanguage studies tend to be cross-sectional, denying us the idiographic description of individual growth and variability
  49. 49. SUGGESTED COMBINATIONS OF METHODOLOGIES • Discourse Analysis and Corpus Linguistics • corpora of language a static collection but can serve to some extent as representative of the language resources of members of the speech community where it was collected • can combine corpus linguistics with close analysis of actual discourse, to trace the genesis and dynamics of language patterns, such as the conventionalization and signaling of metaphors (Cameron &Deignan, 2003, 2006).
  50. 50. SUGGESTED COMBINATIONS OF METHODOLOGIES • Second Language Acquisition and Corpus Linguistics . • The use of Child Language Data Exchange System (CHILDES) tools for SLA research (Rutherford and Thomas ,2001 and Myles 2005) • A corpus of adult English as a second language (ESL) learners in a classroom setting is also a powerful aid in helping us to understand adult language learning better (Reder,Harris, & Setzler, 2003)
  51. 51. SUGGESTED COMBINATIONS OF METHODOLOGIES • Second Language Acquisition and conversation Analysis • means of connecting synchronic dynamism to its over-time counterpart • (CA) attends to the dynamics of talk on the microlevel timescale of seconds and minutes • A long-term view of language development holds great promise (Larsen-Freeman, 2004; Kelly Hall, 2004).
  52. 52. CAS IN LANGUAGE CLASS-ROOM • In ELT, complexity thinking have slowly resonated among research methods,for example in the work of Diane Larsen-Freeman;and in classroom practice, for example Dogme ELT,whose proponents focus on language emergence(as opposed to acquisition).
  53. 53. TOWARD A NEW MIND SET • If teachers and learners are willing to move away from determinism (language learning as linear cause-effect events) and reductionism(understand the parts to understand the whole) • focuses on language and learning primarily as innovative and transformative processes • barriers between self and others and self and worlds begin to dissolve • control is distributed and shared
  54. 54. EMERGENT CURRICULUM • Content is selected as learners‘ needs arise • an on-going process • No predetermined sequence/syllabus to be followed(unless learners themselves want to design and frequently revise one) • Learning is not linear and predictable(hence cannot be thoroughly planned for) • More room for the ‗unplanned‘ is needed. • Unplanned situations or unstructured activities can sometimes create more effective ,natural ,and memorable communicative opportunities than well-planned communicative activities(Cadorath&Harris,1998)
  55. 55. A Linear Model of Language Learning(Harshbarger, 2007)
  56. 56. A Dynamic Model of Language Learning (Harshbarger, 2007)
  57. 57. system development and strong attractors
  58. 58. FOCUS ON IDENTIFYING TYPICAL ATTRACTOR CONGLOMERATE the concept of „interest‟ (cf.D¨ornyei &Ushioda2011),  motivational,  cognitive and affective factors,  the emotional enjoyment experienced
  59. 59. FOCUS ON IDENTIFYING AND ANALYSING TYPICAL DYNAMIC OUTCOME PATTERNS:  recognizable outcomes or behavioral patterns
  60. 60. RETRODICTIVE QUALITATIVE MODELLING we reverse the order of things and pursue „retro-diction‟: by tracing back the reasons why the system has ended up with a particular outcome option we produce a retrospective qualitative model of its evolution.
  61. 61. The idea behind RETRODICTION is that by identifying the main emerging system prototypes we can work „backwards‟ and pinpoint the principal factors that have led to the specific settled states.
  62. 62. A classroom illustration of retrodictive qualitative modeling: A three-step research template by Zoltan Dornyie
  63. 63. Step 1: Identifying salient student types in the classroom Use a range of possible sources of information about the specific class: classroom observation, interviews with teachers and students, focus group discussions with teachers and students and even questionnaires processed by cluster analysis
  64. 64. Step 2: Identifying students who are typical of the established prototypes and conducting interviews with them interviews that focus on factors shaping their L2 learning behavior, a detailed characterization of the interviewee‘s place (a) the first was a standard interview conducted in the L2 (English); (b) for the second interview he invited a female native-speaking cointerviewer, and the (female) participants were allowed to choose the language of the interview (c) the third interview was conducted in the L1 (Japanese) by the cointerviewer alone(Hamish Gillies).
  65. 65. special interest in motivational aspects in Gillies‟ study • Attitudes towards L2 learning; L2 learning •Aptitude and L2 proficiency • L2 learning goals and desires; vision of being future L2 speakers; • external influences such as those of family and friends; career considerations; • experience of learning L2 at school; various situation-specific „pushes‟ and „pulls‟; impact • of L2 teacher(s).
  66. 66. Step 3: Identifying the most salient system components and the signature dynamic of each system (a) identifying the system‟s main components E.g. the content analysis of the interviews should be able to generate a full list of the factors that affect the students‟ learning behavior in their class (b) understanding the main underlying dynamic patterns – or the system‟s SIGNATURE DYNAMICS – that produced the observed system outcomes
  67. 67.  This is the phase where we create a proper model by going beyond merely identifying and listing the important learner/classroom factors  we wish to understand why a particular student ended up in one attractor state (learner type) and not another
  68. 68. May not be straight forward to elicit •From retrospective learner self-reports answers to the complex question of how the overall system changed and evolved over time (particularly from the perspective of DST‘s decentralized causality), since a great deal of the data will most likely focus on individual components (Mercer, personal communication, 17 June, 2011).
  69. 69. Cont… Drawing up holistic patterns and interactions from data segments and fragments offers some hope in this respect can be represented in a visually accessible manner by means of „data displays‟ or „schematic representations
  70. 70. SOME OF THE RESEARCHERS Cameron (1999) has applied complex systems theory in a study of the use of tasks in language teaching and concluded that: The constructs and tools of complex systems theory offer new possibilities for theorizing and researching classroom language use and learning.
  71. 71. In Paulson‘s (2005) recent study of eye movements during reading he concluded: “Through the lens of chaos theory, reading can be described as a self-similar, non-linear dynamical system sensitive to reader and text characteristics throughout the process. (p.356)
  72. 72. In language learning: sometimes even a great deal of effort by the teacher will not produce any results, while at some other times something quite small – the right word of praise or necessary recognition of some kind – will make the student blossom; the various interlinked components of the system can moderate – both in a positive and negative way – the impact of any input.
  73. 73. Unequal learning experiences may occur in very similar situations. When we turn our observation to language teaching practices, we see that no matter how much teachers plan and develop their classes, students will react in different ways and unforeseen events will inevitably be part of their learning experiences. The seemingly orderly world of acquisition is in fact chaotic and chaos seems to be fundamental in such a process.
  74. 74. conclusion Difficult to control  subject to influences.  The exact impact cannot be predicted but general trends can be expected over time. A hurricane……….  Predictions of complex systems and ways to influence such systems‟ outcomes are also getting better as more is learned about complex systems behavior. (Harshbarger, 2007) 
  75. 75. REFERENCES Agar, M. (2004). We have met the other and we‟re all nonlinear: Ethnography as a nonlinear dynamic system. Complexity, 10(2), 16– 24. Atkinson, D. (2002). Toward a sociocognitive approach to second language acquisition. Modern Language Journal, 86, 525–545. Barab, S. (2006). Design-based research: Amethodological toolkit for the learning scientist. In R. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 153–169). Cambridge: Cambridge University Press. Baskerville, R., &Wood-Harper, T. (1996). A critical perspective on action research as a method for information systems research. Journal of Information Technology, 11, 235–246.
  76. 76. REF…….. Cadorath, J and Harris, S (1998) „Unplanned classroom language and teacher training‟ ELT Journal, 52 Cameron, L. (1999). The complex dynamics of language use on tasks. Paper presented at the British Association for Applied Linguistics Annual Meeting, University of Edinburgh. Cameron, L., & Deignan, A. (2003). Using large and small corpora to investigate tuning devices around metaphor in spoken discourse. Metaphor and Symbol,18, 149– 160. Ellis, N. (1998) Emergentism, connectionism and language learning. Language Learning 48:4, pp. 631–664 Gaddis, J. L. (2002). The landscape of history. Oxford: Oxford University Press. Gleick, J. 1987 Chaos Making a New Science New York Penguin Books Harshbarger, B. (2007). Chaos, complexity and language learning. Language Research Bulletin, 22. Kelly Hall, J. (2004). Language learning as an interactional achievement. Modern Language Journal, 88, 607–612.
  77. 77. REFE…… Lobato, J. (2003). How design experiments can inform a rethinking of transfer and vice versa. Educational Researcher, 32, 17–20. Reder, S., Harris, K., & Setzler, K. (2003). A multimedia adult learner corpus. TESOL Quarterly, 37, 546–557. Van Geert, P., & Steenbeek, H. (in press). A complexity and dynamic systems approach to development assessment,modeling and research. In K.W. Fischer,A. Battro, & P. Lena (Eds.), The educated brain.Cambridge: Cambridge University Press.
  78. 78. REFE…….. Larsen-Freeman, D. (2004). CA for SLA? It all depends. Modern Language Journal, 88, 603–607. Larsen-Freeman, D., Long. M. H. (1991) An Introduction to Second Language Acquisition Research. New York: Longman. Larsen-Freeman, D. (1997) 'Chaos/complexity science and second language acquisition', Applied Linguistics, 18, 141-65 _______________. (2000) 'Second language acquisition and applied linguistics', Annual Review of Applied Linguistics, 20: 165-181 _______________. (2002) Language acquisition and language use from a chaos/complexity theory perspective. In Kramsch, C. (Ed.) Language acquisition and language socialization. London, New York: Continuum, 2002. p.3-46