learning networks: e-learning 3.0

Learning NetworksNeil RubensActive Intelligence GroupKnowledge Systems (Okamoto/Ueno) LaboratoryUniversity of Electro-Communications

Tokyo, Japan

Evolution of eLearningeLearning 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

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)

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?

Intelligent

Web 3.0

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

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)

http://www.sciencemag.org/content/331/6018/717.full


https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Data ScienceLarge 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



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)

http://livepage.apple.com/

http://livepage.apple.com/

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

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]

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)

http://en.wikipedia.org/wiki/Social_constructionism


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]



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]

http://en.wikipedia.org/wiki/Constructivism_(learning_theory)

http://en.wikipedia.org/wiki/Constructivism_(learning_theory)

Constructionist View 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)

“ideas” are constructed or invented through the social process of communication.

http://en.wikipedia.org/wiki/Communication_theory

http://en.wikipedia.org/wiki/Communication_theory

ConnectivismKnowledge 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.

ConnectivismConstructivismConstructivism vs. vs. ConnectivismConnectivism

(Siemens, 2009)

[Siemens, 2009]

Challenges: Is this Social?

(S. Goel, et al. 2011)

http://messymatters.com/2011/07/31/viral/

http://messymatters.com/2011/07/31/viral/

[wiki]

‣ 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)

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 needs to flow well through the knowledge network.

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.

Modeling Approach‣ Learn Network Model

based on existing networks:

‣ Given nodes

‣ Use Learned Model toPredict Network

Models

Concept Extraction

conceptconcept

conceptconcept

conceptconcept

conceptconcept

documentsconcepts

conceptconceptconceptconcept

conceptconceptconceptconcept

SemanticMapping

context(documents)

concepts

concept concept

concept concept

concept concept

Knowledge LevelEstimation

conceptconcept

conceptconcept

conceptconcept

conceptconcept

concepts

concept concept

concept concept

concept concept

Group Formation

tasks

Influence Estimation

interaction log

Example Application Flow

userI want to know about term t_i

t_i

t_i t_i

t_i

t_it_i

semantics

contents

social

u_i: I think t_i is same as t_j … u_j: no t_is is more like t_p ...u_k: you are both wrong t_i is ...

system user

I want to know about term t_i and t_k

system

semantics

contents

social

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 ...

t_i

t_i t_i

t_i

t_it_i

t_i

user

I think t_i and t_k are similar ..

system

social

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

semantics

contents

t_i

t_i t_i

t_i

t_it_i

t_i

social

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

Search Engineconceptwant to learn about:

docs

Concept Extractor

conceptconcept

conceptconcept

conceptconcept

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docs

conceptconcept

conceptconcept

conceptconcept

conceptconcept

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concept concept

concept

concept concept

Search Engine

concept

concept

concept

concept

concept concept

Model Layer Link Layer

discussions

……

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concept

concept

concept

concept concept

concept

concept

concept

System Flow

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* Skills supported by Learning Networks

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