Information aggregation in

networked learning: The human factor and serendipity

Rita Kop Institute for Information Technology

Learning and Collaborative Technologies Group

What to expect

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‘The circumstances, conditions and the very

status of knowledge, learning, teaching and

researching are currently in a state of

profound upheaval under the impact of rapid

and far-reaching technological change’.

Lankshear and Knobel 2000

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Changes in the

nature of the Web

http://www.flowtown.com/blog/have-we-reached-a-world-of-infinite-information?display=wide Rita Kop

‘This new reality presents challenges for the

existing higher education system. In

particular, how do people learn and solve

complex problems in social, global networks

in climates of information abundance?’

Siemens, 2012

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The changing learning

environment

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Changes in student

behaviour - Research

using a virtual computer

University

resources used:

28.51%

Web resources

used: 71.49%

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Pardo and Kloos (2011)

Changes in learning:

Open Networked Learning • A new theoretical

paradigm

• Transforms relations and communication

• Challenges existing educational structures

• Asks why we should continue to believe preconceived definitions of knowledge, learning, or collectively built knowledge?

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How neutral is

the Web?

Barabási (2003)

http://bit.ly/gsiqF7

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Critical Literacies for

educators and learners

Level of complexity: • Change

• Context

• Presence

• Networking

• Participation

• Attention

• Remixing

• Intercultural

New

semantics: •Text

• Hypertext

• (Social) Media

• Technical

• Searching

• Tagging

• Filtering

•Aggregating

• Analytics

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Recommender systems

and serendipity

Serendipity in the

information stream?

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Information aggregation

Moving from:

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To:

Rita Kop http://davidwarlick.com/colearners/?p=73

and

Factors affecting

serendipity

• Autonomy

• Openness

• Interactivity

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Stephen Downes

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The human factor

and serendipity

Level of communication

Diversity

Degree of distance

RESEARCH

ON MOOCS

Challenges in

capturing Big Data

meatmeter.blogspot.com abovethelaw.com

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Who were the

participants?

Participants’ age

Participants’ residence

Participants’ professional background

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What did

participants do?

PLENK participation rates Rita Kop

21

• Twitter posts

• Discussion posts

• Blog posts

• Concept maps

• Google map

Wordles

• Pearltrees networks

• Presentations

• Animations

• S.Network groups

• Second Life area

http://zaidlearn.blogspot.com

/2010/09/plenk-2010-most-awesome-course-

on.html

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What did people

share on Twitter?

How did people

communicate on Twitter?

Twitter PLENK

connections to hash-

tag networks

#tags related to Twitter posts in the PLENK Daily - six weeks duration

Factors influencing serendipity on

the PLENK2010 Twitter network

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• Number of incoming messages

• The balance between messages: noise to signal

ratio

• Tweets or re-tweets

• RSS streams shared

• # Networks

Conclusions

• Small study

• Serendipity and predictive analytics systems

• Learning Analytics

• Human factor

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Further research

• Results need comparing with the

effectiveness of more traditional information

filtering strategies

• Serendipity Index

Rita Kop, PhD.

Frederika.Kop@nrc-cnrc.gc.ca

www.you-learn.org

@ WelshCloggy

Links to MOOC and PLE

research publications:

http://bit.ly/AEgnnp