This document discusses the evolution of e-learning and predictions about the future of e-learning, referred to as e-learning 3.0. It begins by reviewing e-learning 1.0 and 2.0, noting how technologies and learning theories have progressed. Predictions for e-learning 3.0 include learning becoming contextual and personalized through intelligent technologies that are integrated everywhere. Challenges with current e-learning models are also discussed, such as information overload and an inefficient knowledge cycle. The document suggests artificial intelligence will play a larger role in e-learning 3.0 by helping address these challenges as data and computing resources increase.

1. E-Learning 3.0
anyone, anywhere, anytime, and AI
Learning Networks
Neil Rubens
Active Intelligence Group
Knowledge Systems Lab /
University of Electro-Communications
Tokyo, Japan
http://ActiveIntelligence.org
http://www.flickr.com/photos/lifeinverted/5651315924/
SPeL 2011: International Workshop on Social and Personal Computing for Web-Supported
Learning Communities

2. Evolution of eLearning: eLearning 1.0
eLearning uses technology to enhance Learning
To understand where the eLearning might be 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
‣ 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:
‣ Constructivism: constructing one's own knowledge from one's
own experiences
(enabled through writing)
‣ Social Learning: people learn from one another
(enabled through socializing)

4. eLearning 3.0?
http://www.flickr.com/photos/christian78/2960519381

5. Review of Evolution of Systems
Predictions: eLearning 3.0
Contextual
Personal Intelligent
E-Learning 2.0 Connected Integrated
Collaborative
Social/Communicative
Dynamic
Databases/RSS
E-LearningInteractive
1.0
Multimedia
Static
HTML- or Text Greller, 2011

6. Review of Predictions: eLearning 3.0
consume( create,(form,(share( socialise(
transfer( par.cipate(( connect(
transmit( reflect( create((together)(
cer.fy( evidence( collaborate(
recognise(
Content( Process( m(
vis
m( ( m( uc
.
ris m vis str m(
vio vis m( c. on vis
ha uc. vis tru ec
.
Be tr i. s i o ?c n
gn on oc on
Ins C o C S C
Greller,(2011(
Greller, 2011

7. Review of Predictions: eLearning 3.0
e-Learning 1.0 e-Learning 2.0 e-Learning 3.0
Meaning is Dictated Socially constructed Socially constructed and
Contextually reinvented
Technology is Confiscated at the Cautiously adopted Everywhere
classroom door (digital immigrants) (ambient, digital universe)
(digital refugees)
Teaching is Teacher to student Teacher to student and Teacher to student, student
student to student to student, student to
(progressivism) teacher, people-­‐technology-­‐
people (co-constructivism)
Classrooms are In a building In a building or online Everywhere (thoroughly
located (brick) (brick and click) infused into society: cafes,
bowling alleys, bars,
workplaces, etc.)
Teachers are Licensed Licensed professionals Everybody, everywhere
professionals
Hardware and Are purchased at Are open source and Are available at low cost
software supply great cost and available at lower cost and are used purposively
ignored
Industry views Assembly line As ill-­‐prepared assembly As co-­‐workers or
graduates as workers line workers in a entrepreneurs
knowledge economy
(adopted from Moravec 2009: 33) (Ogorshko, 2011)

8. Our Predictions: eLearning 3.0
Typical predictions of eLearning 3.0:
Learning -> Technologies
Limitation: Needed technologies may not be available
Our Predictions:
Technologies -> Learning
‣ What new technologies will become available?
‣ What aspects of Learning Theories could be activated by using and
extending new technologies?

9. Why do we need eL 3.0?
Whats Wrong with 2.0?
http://etc.usf.edu/clipart/28000/28015/tower_pisa_28015.htm

10. Challenges: Is this6=Social?
People Talking ; Social
(S. Goel, et al. 2011)

11. Limitations: Broken Knowledge Cycle
‣ Problem: The current cycle of knowledge creation/utilization 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)
‣ Peak Social – the point at which we can gain no new advantage from social
activity (Siemens 2011)
utilize
U0lized
d ge
no wle
K
is ting
Ex
Redundant
create
Knowledge Novel
*there are some personal benefits e.g. externalization, crystallization, etc.

12. Information
Overload

13. Web 3.0
Radar Networks & Nova Spivack, 2007
http://www.technodiscoveries.com/2010/01
Web$3.0$ Web$x.0$
Degree(of(Informa&on(Connec&vity(
Seman&c(Web( Meta(Web(
Connects$knowledge$ Connects$intelligence$
Web$1.0$ Web$2.0$
The(Web( Social(Web(
Connects$informa6on$ Connects$people$
Degree(of(Social(Connec&vity( Steve(Wheeler,(University(of(Plymouth,(2011(

14. AI is poised to Play a Major Role
‣ 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 accessible
(cloud computing)
* more specifically Machine Learning

15. 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: > 30 Million tweets (Rubens & Louvigne et. al., 2011)
‣ includes data on how users learn outside of the classroom
(not typically available)

16. 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. (Science, 2011)
‣ Traditional:
‣ Hypothesis -> Model -> Validation (data)
‣ Limitations
‣ Sometimes is disconnected from the reality
‣ Validation data is often biased by the initial hypothesis
‣ Time Consuming: model must be explicitly programmed
‣ Data-driven
‣ Data -> Model
‣ Advantages
‣ model is constructed automatically by utilizing AI methods
‣ large number of dimensions could be analyzed
‣ can handle complexity well

17. Learning Theories
(Ireland, 2007, link)

18. 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)

19. Analysis of Large-scale Distributed Collaborative Learning
Audi reached out to public to help to
define what Progress IS.
What is Progress: faster, cheaper, eco,
comfortable, beautiful?
People could collaborate, discuss, and
vote for each others definition of progress.
> 100,000 tweets
In collaboration with:

20. eLearning 3.0
‣ Automatically discover new Learning Models
‣ by applying AI methods
‣ to BIG data