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

1. Presenters;
MEHWISH NOOR
SARA AZAM
FAKHRA MUHABAT

2. RESEARCHING LANGUAGE AS A
COMPLEX ADAPTIVE SYSTEM

4. 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

5. 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

6. CHAOS
• 'To some physicists chaos is a science of process
rather than state, of becoming rather than being'
(Gleick 1987: 5)

7. A COMPLEX SYSTEM
• Emerges from the interactions of its
components.
• (LARSEN–FREEMAN & CAMERON,2008)

8. COMPLEX SYSTEMS
• Often heterogeneous, being made up of both agents
and elements. (LARSEN–FREEMAN & CAMERON,2008)

9. APPLIED LINGUISTIC
COMPLEX SYSTEMS
• Contain many subsystems, nested one within another.
(LARSEN–FREEMAN & CAMERON,2008)

10. 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

11. 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).

12. 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)

13. 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. FEATURES OF COMPLEX
NONLINEAR SYSTEMS
DYNAMIC
CHAOTIC
COMPLEX
OPEN
NONLINEAR
SELF
ORGANISING

15. FEATURES OF COMPLEX
NONLINEAR SYSTEMS
Adaptive
Feedback
sensitive
Sensitive to
initial
conditions

16. 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)

17. 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)

18. 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)

19. (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)

20. 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)

21. 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)

22. 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)

23. 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‖.

24. 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.

25. INTER-LANGUAGE AS A STRANGE ATTRACTOR
• Highly sensitive to initial conditions.
• Small changes in the initial conditions result in
unpredictable shifts in language development.

26. 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.

27. 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)

28. 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).

29. MAIN STANDPOINT
• Researchers' grammars
containing static rules do not
do justice to the everchanging character of
learners' internal L2
grammars.

30. 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)

31. 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.

32. 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.

33. 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.

34. 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

35. 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)

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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)

43. 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.‖

44. 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).

45. 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

46. 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)

47. 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

48. LEWINIAN CYCLE
Diganosing
Specifying
Learning
Evaluating
Action
Planning
Taking
Actions

49. 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

50. 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).

51. 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)

52. 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).

53. 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).

54. 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

55. 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)

56. A Linear Model of Language
Learning(Harshbarger, 2007)

59. A Dynamic Model of Language
Learning (Harshbarger, 2007)

60. system development and strong
attractors

61. FOCUS ON IDENTIFYING TYPICAL
ATTRACTOR CONGLOMERATE
the concept of „interest‟ (cf.D¨ornyei &Ushioda2011),
 motivational,
 cognitive and affective factors,
 the emotional enjoyment experienced

62. FOCUS ON IDENTIFYING AND ANALYSING
TYPICAL DYNAMIC OUTCOME PATTERNS:
 recognizable outcomes or behavioral patterns

63. 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.

64. 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.

65. A classroom illustration of
retrodictive qualitative modeling:
A three-step research
template
by
Zoltan Dornyie

66. 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

67. 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).

68. 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).

69. 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

70.  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

71. 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).

72. 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

73. 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.

74. 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)

75. 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.

76. 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.

77. 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)


78. REFERENCES
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Ethnography as a nonlinear dynamic system. Complexity, 10(2), 16–
24.
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79. REF……..
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80. REFE……
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81. REFE……..
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