draft of the presentation at the internal seminar

1. Learning Networks
Neil Rubens
Active Intelligence Group
Knowledge Systems (Okamoto/Ueno) Laboratory
University of Electro-Communications
Tokyo, Japan

2. Evolution of eLearning
eLearning activates Learning Theories through new Technologies
To understand where the eLearning is going, we need to take a quick look at
where it's been
‣ eLearning 1.0:
‣ Web 1.0:
‣ reading: content became easily accessible
‣ logging: user’s activities could be logged and analyzed
‣ Learning Theories:
‣ Behaviorism: learning is manifested by a change in behavior,
environment shapes behavior, contiguity
‣ Cognitivism: how human memory works to promote learning

3. Evolution of eLearning
‣ eLearning 2.0:
‣ Web 2.0:
‣ writing: anybody can easily create content (e.g. blogs, wiki, etc.)
‣ socializing: interaction is easy (e.g. facebook, twitter, etc.)
‣ Learning Theories:
‣ Social Learning: people learn from one another
(enabled through writing and socializing)
‣ Constructivism: constructing one's own knowledge from one's
own experiences
(enabled through writing)

4. Whats Next?
We are on the verge of Web 3.0
‣ What new technologies will become available?
‣ What aspects of Learning Theories could be activated by using and
extending new technologies?

5. Web 3.0
Intelligent

6. New Technologies: AI
‣ Artificial Intelligence AI *
‣ AI has been successful in ‘restricted’ domains e.g. chess
‣ In more open domains (e.g. eLearning) success of AI has been limited:
‣ More Complexity -> More Parameters -> More Data, More
Computational Resources
‣ Large scale data and computational resources have not been
easily available
‣ Things are changing:
‣ Large-scale data is becoming available (BIG/Open data)
‣ Large-scale Computational resources are becoming available (cloud
computing)
* more specifically Machine Learning

7. BIG/Open data
‣ Open data: freely available to everyone to use and republish as they wish;
e.g. wikipedia, twitter, data.gov, etc.
‣ Big data:
‣ amount of data generated is growing by 58% per year (Gantz, 2011)
‣ pieces of content shared on Facebook 30 billion/month (McKinsey, 2011)
‣ Big Data in eLearning
‣ KDD Cup 2010: 36 Million ITS records (PSLC, CMU)
‣ Learning Dataset: > 10 Million tweets* (Rubens & Louvigne et. al., 2011)
‣ includes how users learn outside of the classroom (typically not
available)
* collected with Twitter Streamer (Louvigne & Rubens et. al., 2011)

8. Data Science
Large data sets can potentially provide a much deeper understanding of both nature and society. Social scientists are getting to the point in
many areas at which enough information exists to understand and address major previously intractable problems that affect human society.
(Science, 2011)
‣ Traditional:
‣ Hypothesis -> Model -> Validation (data)
‣ Limitations
‣ It is difficult to discover new/good hypothesis
‣ Time Consuming: model must be explicitly programmed
‣ Correctness: a single hypothesis might not be suitable for all of the cases
‣ learning is very complex and differs from person to person. A single hypothesis does not fit all
‣ Limited Adaptability
‣ learners styles may change, different learning material types might be used, etc.
‣ Data-driven
‣ Data -> Model
‣ Advantages
‣ model is constructed automatically by utilizing AI methods
‣ separate models for different user types
‣ adaptable

9. Learning Analytics
‣ Education is, today at least, a black box. We don't really know:
‣ How our inputs influence or produce outputs.
‣ Which academic practices need to be curbed and which need to be
encouraged.
We are essentially swatting flies with a sledgehammer and doing a
fair amount of peripheral damage.
‣ Once we better understand the learning process — the inputs, the
outputs, the factors that contribute to learner success — then we can
start to make informed decisions that are supported by evidence.
(Siemens, 2011)

10. eLearning 3.0
‣ Automatically discover new Learning Models
‣ by applying AI methods
‣ to BIG data
‣ Use obtained Learning Models to support Learners
‣ Learning Theories activated by Web 3.0
‣ Pragmatism
‣ Connectivism

11. Pragmatism
‣ Pragma&sms
(Pragma&c
Web):
connec&ng
people
and
informa&on
‣ Consists
of
the
tools,
prac+ces
and
theories
describing
why
and
how
people
use
informa+on.
In
contrast
to
the
Syntac&c
Web
and
Seman&c
Web
the
Pragma&c
Web
is
not
only
about
form
or
meaning
of
informa&on,
but
also
about
social
interac&on.
‣ The
transforma-on
of
exis-ng
informa-on
into
informa-on
relevant
to
a
group
of
users
or
an
individual
user
includes
the
support
of
how
users
locate,
filter,
access,
process,
synthesize
and
share
informa-on.
[wiki]

12. Social Constructionism & Constructivism
‣ Social Constructionism
‣ development of knowledge in social contexts
‣ Social Constructivism
‣ individual's making meaning of knowledge within a social context
(Vygotsky 1978)

13. Social Constructionism
‣ A view of learning as a reconstruction rather than as a transmission of
knowledge (Papert).
‣ A major focus of social constructionism is to uncover the ways in
which individuals and groups participate in the construction of their
perceived social reality.
‣ Berger and Luckmann argue that all knowledge, including the most
basic, taken-for-granted common sense knowledge of everyday
reality, is derived from and maintained by social interactions.
[url-ref]

14. Social Constructivism
‣ The process of sharing individual perspectives-called collaborative elaboration
(Meter & Stevens, 2000)-results in learners constructing understanding
together that wouldn't be possible alone (Greeno et al., 1996).
‣ An active process where learners should learn to discover principles, concepts
and facts for themselves (Brown et al.1989; Ackerman 1996).
‣ Individuals make meanings through the interactions with each other and with
the environment (Ernest 1991; Prawat and Floden 1994).
‣ Learners with different skills and backgrounds should collaborate in tasks and
discussions to arrive at a shared understanding of the truth in a specific field
(Duffy and Jonassen 1992).
‣ The social constructivist paradigm views the context in which the learning
occurs as central to the learning itself (McMahon 1997).
‣ Knowledge should not be divided into different subjects or compartments, but
should be discovered as an integrated whole (McMahon 1997; Di Vesta 1987).
[url-ref]

15. Constructionist View
of Communication
“ideas” are constructed or invented
through the social process of
communication.
‣ Noise; interference with effective transmission and reception of a message.
‣ Sender; the initiator and encoder of a message
‣ Receiver; the one that receives the message (the listener) and the decoder of a message
‣ Decode; translates the senders spoken idea/message into something the receiver understands by using their
knowledge of language from personal experience.
‣ Encode; puts the idea into spoken language while putting their own meaning into the word/message.
‣ Channel; the medium through which the message travels such as through oral communication (radio, television,
phone, in person) or written communication (letters, email, text messages)
‣ Feedback; the receivers verbal and nonverbal responses to a message such as a nod for understanding
(nonverbal), a raised eyebrow for being confused (nonverbal), or asking a question to clarify the message (verbal).
‣ Message; the verbal and nonverbal components of language that is sent to the receiver by the sender which
conveys an idea.
(Rothwell, 2011)

16. Connectivism
Knowledge is distributed across a network of connections, and therefore that learning consists of
the ability to construct and traverse those networks [Downes & Siemens 2008]
‣ Principles
‣ Learning is a process of connecting specialized nodes or information sources.
‣ Capacity to know more is more critical than what is currently known
‣ Nurturing and maintaining connections is needed to facilitate continual learning.
‣ Ability to see connections between fields, ideas, and concepts is a core skill.
‣ Currency (accurate, up-to-date knowledge) is the intent of all connectivist
learning activities.
‣ Decision-making is itself a learning process. Choosing what to learn and the
meaning of incoming information is seen through the lens of a shifting reality.
While there is a right answer now, it may be wrong tomorrow due to alterations
in the information climate affecting the decision.

17. Constructivism vs. Connectivism
Connectivism
[Siemens, 2009]

18. Challenges: Is this Social?
(S. Goel, et al. 2011)

19. How learning content is used and distributed by learners might
be more important than how it is designed (Chatti et. al., 2007)
For the knowledge to be utilized and constructed it
[wiki] needs to flow well through the knowledge network.
‣ Problem: Current Knowledge flow is
inefficient
‣ large portion of created content is never
utilized by others*
‣ only 0.05% of twitter messages
attracts attention (Wu et. al., 2011)
‣ only 3% of users look beyond top 3
search results (Infolosopher, 2011)
‣ large parts of created contents are
redundant (Drost, 2011)

20. Proposal: Learning Networks
‣ Learning Networks: Creating Networks in which Learning can flourish.
‣ Networks are crucial for learning, however currently these networks are either
incomplete or non-existent.
‣ Goals:
‣ create needed networks/connections
‣ assist learners/teachers in
‣ utilizing/analyzing networks
‣ making connections
‣ By creating connections/networks we can improve the effectiveness of not only
Connectivism and Pragmatism but also of Behaviorism, Constructivism,
Constructionism.

21. Modeling Approach
‣ Learn Network Model
based on existing networks:
‣ Given nodes
‣ Use Learned Model to
Predict Network

22. Models
Concept concept concept
Extraction concept concept
concept concept
concept concept
Group Formation
concepts
documents
tasks
concept concept
context
(documents)
Semantic concept
concept
Mapping
concept concept
concept concept concept concept
concept concept
concept concept
Influence
concepts
Estimation
interaction log
concept concept
Knowledge Level concept concept
Estimation
concept concept
concept concept
concept concept
concept concept concept concept
concepts

23. Example Application Flow
user system user system user system
I want to know about term t_i I think t_i and t_k are similar ..
I want to know about term t_i and t_k
social
semantics semantics
t_i
t_i u_i: I think t_i is same as t_j …
t_i
t_i u_j: no t_is is more like t_p ...
u_j: no t_is is more like t_p ...
t_i t_i
t_i t_i u_m: I think t_i and t_k are similar ..
u_i: you are right
t_i
t_i
t_i
t_i
t_i semantics t_i
t_i
contents contents
t_i t_i
t_i
t_i
contents t_i
social social
social
u_i: I think t_i is same as t_j … u_i: I think t_i is same as t_j …
u_j: no t_is is more like t_p ... u_j: no t_is is more like t_p ...
u_k: you are both wrong t_i is ... u_j: no t_is is more like t_p ...
u_i: I think t_i is same as t_j …
u_j: no t_is is more like t_p ...
u_j: no t_is is more like t_p ...
u_m: I think t_i and t_k are similar ..
u_i: you are right

24. System Flow Model Layer Link Layer
docs
want to learn about:
concept Search Engine
Concept concept concept
docs
Extractor concept concept
concept concept
concept concept
concept concept
concept concept Semantic
concept concept concept concept
concept concept Mapping
concept concept
concept concept
concept concept
concept concept Search Engine
concept concept concept concept
…
discussions
… concept
… concept
… concept
…
concept concept

25. * Skills supported by Learning Networks
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26. Potential Applications
‣ Learners’ tools
‣ examine existing networks
‣ create new connections (w/ knowledge, people,tasks)
‣ Teachers’ Tools
‣ analyze connections created between: people, knowledge, etc
‣ monitor individual production as well as group production
‣ curriculum construction