Networks and network structure
social network analysis
centrality
Degree
high number of direct connections
hub
Betweenness
connections between constituencies
brokers
boundary spanner
Closeness
shortest paths to others
monitor information flow
evolutionary view
David Ronfeldt
post-industrial
web-like
social equity
justice
civil society
collective goods
information overload
deception
technology - internet
body analogy - sensory system
network configurations
random networks
binomial or Poisson degree distributions
scale-free networks (Barabási)
"scale-free" = same characteristics at any scale
self-similarity
e.g. Cantor set
finite properties
infinite properties
e.g. fractals
Barabási's properties
they grow and must keep growing
preferential attachment
stochastic urn process
hubs preferred for new attachments
e.g. growth of airport network
obey a power law
e.g. 6 degrees
e.g. small world
Erdős–Bacon number
signals travel faster in scale-free networks
viruses in society
computer viruses
great ideas
feature of many (but not all) real networks
internet
cells
society
neural level map of connections possibly but ...
data (the connectome) lacking
generative models lacking
helps describe complexity
network properties
structure
connections
nodes / entities
signals
flow / volume
speed
human dynamics
time in complex systems
Barabási
plasticity
Downes' design principles
decentralise
centralised
star shape
broadcast network
de-centralised
mesh
balanced / stable
distribute
different physical locations
emphasis on 'sharing' not 'copying'
disintermediate
mediation is a barrier
where possible provide direct access
disaggregate
content should be small
do not bundle
allows integration
e.g. learning objects
dis-integrate
open standards
platform / tool independence
democratize
control is impossible
diversity is an asset
entities are autonomous
dynamize
without change adaptation is not possible
creation of connections is a core function
desegregate
learning not a specific domain
part of living
uses same tools as work / play
network as infrastructure
e.g. ubiquitous web
network semantics
how meaning is created
how people use networks
how networks create knowledge
elements
context
salience
emergence
memory
stability
weighting
connective knowledge
What connectivism is
distinct from other learning theories
behaviourism
operant conditioning
Skinner
classical conditioning
Pavlov
other theorists
Thorndike
Watson
humanist
theorists
Maslow
Rogers
Knowles
self-actualisation
teacher-as-facilitator
Maslow's heirarchy of needs
Kolb's experiential learning
experiential learning cycle
concrete experience
observation and reflection
forming abstract concepts
testing in new situations
Dewey's experiential education
'hands on teaching'
problem-based learning
constructivism
discussion and interaction
Social constructivism
Vygotsky
zone of proximal development
language
cognitivist
Piaget
stages of development
sensory-motor
pre-operational
concrete operational
formal operational
"Knowledge constructed by learners as they attempt to make sense of experiences" Driscoll
constructionism
theorists
Seymour Papert
co-founded MIT AI lab
creative experimentation
connectionism
learning neural networks
not symbol processing
social learning theories
communities of practice
Lave & Wenger
Bandura
cognitivism features as well
learn by observing others
cognitivism
information processing
metacognition
theorists
Piaget
Bruner
Ausubel
Gagne
Vygotsky
thought process
knowledge is organised
motivation
attribution
to what do I attribute my learning?
learned helplessness
outcomes are outside my actions
I don't have the skills
Weiner
ARCS
described by John Keller
components
Attention
Relevance
Confidence
Satisfaction
activity theory
Marx, Popper, Vygotsky, Engestrom
actor-network theory (ANT)
actors
material
semiotic
theorists
Latour
Callon
Law
agency
without pre-supposing intentionality
agency is of heterogeneous associations of humans and nonhumans
distributed cognition (D-cog)
Hutchings
Biological views of learning
principles
learning is process of connecting entities
nurturing connections is needed
ability to see connections is a core skill
capacity to know is more important than what is currently known
decision making itself is a learning process
practice
publishing
weblogs
Content Management Systems (CMS)
audio
visual media
manipulate
aggregate
e.g. RSS aggregation
sequence
syndicate
identity
synchronous
chatting
telephone
conferencing
collaboration
taxonomy
Awareness and receptivity
Connection-forming
Contribution and involvement
Pattern recognition
Meaning-making
Praxis
Why have a learning theory?
explain & predict
advance a discipline
prepare for future needs
knowledge
levels
neuronal
conceptual
social
epistemology
nature of knowledge
in connectivism it is not propositional
constellation of connections
pedagogy "network pragmatics"
using networks to support learning
distributed knowledge
Evaluation
strengths
describes what is observed
face validity
use of social media
captures diversity of learning
contemporary
describes current tools
active (but small) current debate e.g. CCK08 / CCK09
weaknesses
reliance on open resources
decision-making poorly explained
metaphors used widely as argument
few concrete examples of connectivist decision-making
engagement / motivation assumed
not widely known or discussed
has perhaps not attracted critical attention
assumptions about network properties
not all networks obey the same complexity rules
very little is known about the brain's neural network
there is no connectome
can't reliably extrapolate from brain to social network or vice versa
information overload
Connectives and Collectives
Groups and networks
sameness
unity (groups)
people united
mass media (group technology)
newspapers
television
group internet technology
corporate email
portal
diversity (networks)
to each his own
personal media (network technology)
telephone
letters
personal email
network internet technology
personal webpages
blogs
member qualities (groups) ... connections (network)
elemental (group) ... ecosystem (network)
togetherness
group coordination
leadership
values
vision
standards
technologies
learning management systems
network diversity
autonomy
independence
interaction
technologies
e-portfolios
personal learning environments
openness
group boundaries
borders
logins
membership
technologies
enterprise computing
federated search
user IDs
Copyright
network openness
free choice of communication
technologies
RSS
APIs
Creative Commons
centralness
group centredness
authority
flow of knowledge from centre
network sharing
little or no centre
knowledge flows in many directions
knowledge
group
transmitted
simple
network
emergent
complex
similar but not connectivist models
Wenger's "proto-communities"
between 'community' and 'network'
"amorphous, networked collections"
people
places
artifacts
early development of identity
e.g. community around 'NPtech' tag
lack properties of community of practice
joint enterpise
well defined domain
has properties of network
connectivity
nodes
links
Ronfeldt's TIMN framework
forms
Tribes
Institutions
Markets
Networks
features
purpose and effect
motivation, strength and weakness
structure
ties and boundaries
tech needs
philospophy
development of forms
social evolution
monoform
coexistence
quadriform
Complexity and chaos
the reality of learning?
non-linear learning
Renata Phelps
chaos
sensitivity to initial conditions
unintended / unpredictable outcomes
complexity
learning is irreducible to its parts
dynamic interactions
criticality of feedback
emergence
e.g.
Conway's "life" algorithm
fractals
Power and Authority
Dutton's "Fifth Estate"
democratic politics
Governments online
e.g. tax returns
'work'
distributed networks
e.g. wikipedia
outside of a 'firm'
education and research
increased collaboration
within and between institutions
internet central to many societies
centrality of the internet to everyday activity
A place to go for information
Trust of the internet
changing balance?
teacher vs. learner
individual vs. group