11th International Academy of CIO (IAC) Annual Meeting and Forum Forum 2: IAC Conference on E-government, CIO and ICT June 27-28, 2016 Bocconi University, Milan Italy

1. Smart learning for education:
transformation life, business,
and the global economy
Prof. Alexander Ryjov
Lomonosov Moscow State University
Russian Presidential Academy of National Economy and Public Administration,
School of IT Management, Russia
alexander.ryjov@gmail.com
11th International Academy of CIO (IAC) Annual Meeting and Forum
Forum 2: IAC Conference on E-government, CIO and ICT
June 27-28, 2016
Bocconi University, Milan Italy

2. http://www.mckinsey.com/business-functions/business-technology/our-
insights/disruptive-technologies
• Scope:
• started with more than 100 possible
candidates
• Sources:
• academic journals,
• business and technology press,
• analysis of published venture capital
portfolios,
• hundreds of interviews with relevant
experts and thought leaders.
• Сriteria:
• the technology is rapidly advancing or
experiencing breakthroughs
• the potential scope of impact is broad
• significant economic value could be
affected
• economic impact is potentially
disruptive

7. Why now?
• Technologies are moving so quickly, and in so many
directions, that economy needs in mass education and
retraining for millions of peoples
• Learning technologies are not changing during last 500
years
• «Pythagoras»
• «Monastery»
• «Parochial school»
• Result: modern learning technologies for education is a
real stopper/ brake for modern economy

9. «Pythagoras» - The Teacher/
«Pythagoras» is here
Greece Sumerians
Rome India
China

10. «Monastery» - The Teacher/
«Supervisor + Book» are here.
Book is unique and is VERY expensive.

11. «Parochial school» - The Teacher/ «Supervisor
+ schoolbook» are here.
Schoolbook is standard and is cheap.
No difference with modern school:
• schoolbook —> iPad
• woody board —> plastic board
• piece of chalk —> felt pen
That’s all !

12. Why now?
• Technologies are moving so quickly, and in so many
directions, that economy needs in mass education and
retraining for millions of peoples
• Learning technologies are not changing during last 500
years
• «Pythagoras»
• «Monastery»
• «Parochial school»
• Result: modern learning technologies for education is
a real stopper/ brake for modern economy

13. EdTech market landscape

20. EdTech geo
E-Learning ($US Billions)
Ref: Edxus Group, IBIS Capital, GSMA, McKinsey & Company, Doceba
North
America
$23,8B
2013 Revenues
4,4%
Annual growth rate
9,0%
Cloud based authoring
tools and learning
platforms growth rate
$27,1B
Revenue by 2016
Western
Europe
$6,8B
2013 Revenues
5,8%
Annual growth rate
$8,1B
Revenue by 2016
Eastern
Europe
$728,8M
2013 Revenues
16,9%
Annual growth rate
$1,2B
Revenue by 2016
Asia
$7,1B
2013 Revenues
17,3%
Annual growth rate
$11,5B
Revenue by 2016
Middle
East
$443M
2013 Revenues
8,2%
Annual growth rate
$560,7M
Revenue by 2016
Africa
$332,9M
2013 Revenues
15,2%
Annual growth rate
$512,7M
Revenue by 2016
South
America
$1,4B
2013 Revenues
14,6%
Annual growth rate
$2,2B
Revenue by 2016

21. EdTech trends and
challenges
• Dying of old/ appearance of new professions; the time is
compressing
• The nature of learning technology has no changed since the
17th - 18th centuries
• The development of ICT/ Internet, the possibility of storing and
processing large amounts of data (big data)
• The success of data sciences/ machine learning in finance,
manufacturing, etc
• Main Challenge: adaptivity/ personalization/ individualization of
learning

22. Mindset for smart learning
• The control system
• The control object
• Environment
• Criteria
22

23. Mindset for smart learning
• The control system (CS)
• The control object (CO)
• Environment (E)
• Criteria (C)
23
Goal/ Criteria

24. Mindset for smart learning
• The control system (CS)
• The control object (CO)
• Environment (E)
• Criteria (C)
24
Goal/ Criteria

25. Tracking/ Measurement
25
Goal/ Criteria
There is no smart
learning without
measurement
• What we can measure?
• Time
• Number of right/ wrong answers
• Style (playing with mouse, etc)
• Gadgets, health trackers *)
• Audio/ video environment
• …

26. Content management
26
Goal/ Criteria
There is no smart
learning without
variety
• What we can change?
• Presentation of the content (color,
etc.)
• Sequence/ navigation of the content
• Level of complexity
• Time for break/ express-tests
• Turbo-regime
• …

27. Content management
27
Goal/ Criteria
There is no smart
learning without
smart criteria
• Different criteria are
possible (for example,
for different countries)
• We use «Minimal time
with minimal number of
mistakes»

28. Smart learning in education:
uchi.ru case

29. Minimal high-level
architecture
Measurements
Testing
System for
evaluation and
monitoring of
classification
System for
evaluation and
monitoring of
learning process
Very easy
Regular
Very difficult
System for
scenario
generation
Type of content
Navigation
…
System for
evaluation and
monitoring of
learning quality
Very good
Good
Fair
Poor
Information processing: Audio/ Video
Speed characteristics: fast/ slow
Attentiveness
Endurance

30. Extended high-level
architecture
Measurements
Testing
System for
evaluation and
monitoring of
classification
System for
evaluation and
monitoring of
learning process
Very easy
Regular
Very difficult
System for
scenario
generation
Type of content
Navigation
…
System for
evaluation and
monitoring of
learning quality
Very good
Good
Fair
Poor
Information processing: Audio/ Video
Speed characteristics: fast/ slow
Attentiveness
Endurance
System for evaluation
and monitoring of
psychophysical
status
System for
evaluation and
monitoring of the
environment
Special devises Express tests Gadgets PC/ Tablet Sensors

31. Specification of minimal
architecture
System for
evaluation and
monitoring of
classification
System for
evaluation and
monitoring of
learning process
Very easy
Regular
Very difficult
System for
scenario
generation
Type of content
Navigation
…
System for
evaluation and
monitoring of
learning quality
Very good
Good
Fair
Poor
Information processing: Audio/ Video
Speed characteristics: fast/ slow
Attentiveness
Endurance
Initial measurements (numbers)
Linguistic tier (membership functions)
A
X=x*; Y=y*
Xx*
small big
If A=small и B=big then Z1
If С=medium then Z2
… Logical tier (fuzzy rules)

32. Education XIX vs. Education XXI

33. Summary
Smart learning technologies are changing dramatically
the core functions of the society - education
Using Smart learning systems we can solve the main
challenge for modern economy - mass education and
retraining people
These technologies can reduce costs and improve
quality of service, lifestyle for a number of people. The
potential is enormous - but as in business, it will not be
realized without substantial investments in capabilities.

34. References
A gallery of disruptive technologies -
http://www.mckinsey.com/assets/dotcom/mgi/slideshows/disruptive_tech/index.html#
James Manyika, Michael Chui, Jacques Bughin, Richard Dobbs, Peter Bisson, Alex Marrs. Disruptive
technologies: Advances that will transform life, business, and the global economy. McKinsey Global
Institute (MGI), May 2013, 176 p. -
http://www.mckinsey.com/insights/business_technology/disruptive_technologies
James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, Jacques Bughin, Dan
Aharon. THE INTERNET OF THINGS: MAPPING THE VALUE BEYOND THE HYPE. McKinsey Global
Institute (MGI), June 2015, 144 p. -
http://www.mckinsey.com/insights/business_technology/the_internet_of_things_the_value_of_digitizing_th
e_physical_world
Ryjov A. Basic principles and foundations of information monitoring systems. In: Monitoring, Security, and
Rescue Techniques in Multi-agent Systems. Barbara Dunin-Keplicz, Andrzej Jankowski, etc. (Eds.).
Springer-Verlag, 2005, ISBN 3-540-23245-1, ISSN 16-15-3871, pp. 147-160.
Alexander Ryjov. Towards an optimal task-driven information granulation. In: Information Granularity, Big
Data, and Computational Intelligence. Witold Pedrycz and Shyi-Ming Chen (Eds.). Springer International
Publishing Switzerland 2015, pp. 191-208.
Alexander Ryjov. Personalization of Social Networks: Adaptive Semantic Layer Approach. In: Social
Networks: A Framework of Computational Intelligence. Witold Pedrycz and Shyi-Ming Chen (Eds.).
Springer International Publishing Switzerland 2014, pp. 21-40.

35. Thank You!

36. Backups

38. Alexander Ryjov. Personalization of Social Networks: Adaptive Semantic Layer Approach. In:
Social Networks: A Framework of Computational Intelligence. Ed. by Witold Pedrycz and Shyi-
Ming Chen. Springer Verlag, 2014 p. 21-40.
http://link.springer.com/chapter/10.1007%2F978-3-319-02993-1_2