Smart learning for education: transformation life, business,

and the global economy

Prof. Alexander RyjovLomonosov 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, 2016Bocconi University, Milan Italy

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

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

«Pythagoras» - The Teacher/ «Pythagoras» is here

Greece Sumerians

Rome India China

«Monastery» - The Teacher/ «Supervisor + Book» are here.

Book is unique and is VERY expensive.

«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 !

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

EdTech market landscape

EdTech geo

E-Learning ($US Billions)

Ref: Edxus Group, IBIS Capital, GSMA, McKinsey & Company, Doceba

NorthAmerica

$23,8B2013 Revenues

4,4%Annual growth rate

9,0%Cloud based authoring tools and learning platforms growth rate

$27,1BRevenue by 2016

Western Europe

$6,8B2013 Revenues

5,8%Annual growth rate

$8,1BRevenue by 2016

Eastern Europe

$728,8M2013 Revenues

16,9%Annual growth rate

$1,2BRevenue by 2016

Asia

$7,1B2013 Revenues

17,3%Annual growth rate

$11,5BRevenue by 2016

Middle East

$443M2013 Revenues

8,2%Annual growth rate

$560,7MRevenue by 2016

Africa

$332,9M2013 Revenues

15,2%Annual growth rate

$512,7MRevenue by 2016

SouthAmerica

$1,4B2013 Revenues

14,6%Annual growth rate

$2,2BRevenue by 2016

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

Mindset for smart learning• The control system

• The control object

• Environment

• Criteria

22

Mindset for smart learning• The control system (CS)

• The control object (CO)

• Environment (E)

• Criteria (C)

23

Goal/ Criteria

Mindset for smart learning• The control system (CS)

• The control object (CO)

• Environment (E)

• Criteria (C)

24

Goal/ Criteria

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

• …

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• …

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»

Smart learning in education:uchi.ru case

Minimal high-level architecture

Measurements

TestingSystem for

evaluation and monitoring of classification

System for evaluation and monitoring of

learning process

Very easyRegularVery difficult

System for scenario

generation

Type of contentNavigation…

System for evaluation and monitoring of

learning quality

Very goodGoodFairPoor

Information processing: Audio/ VideoSpeed characteristics: fast/ slowAttentiveness Endurance

Extended high-level architecture

Measurements

TestingSystem for

evaluation and monitoring of classification

System for evaluation and monitoring of

learning process

Very easyRegularVery difficult

System for scenario

generation

Type of contentNavigation…

System for evaluation and monitoring of

learning quality

Very goodGoodFairPoor

Information processing: Audio/ VideoSpeed characteristics: fast/ slowAttentiveness 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

Specification of minimal architecture

System for evaluation and monitoring of classification

System for evaluation and monitoring of

learning process

Very easyRegularVery difficult

System for scenario

generation

Type of contentNavigation…

System for evaluation and monitoring of

learning quality

Very goodGoodFairPoor

Information processing: Audio/ VideoSpeed characteristics: fast/ slowAttentiveness Endurance

Initial measurements (numbers)

Linguistic tier (membership functions)A

X=x*; Y=y*

Xx*

small big

If A=small и B=big then Z1If С=medium then Z2… Logical tier (fuzzy rules)

Education XIX vs. Education XXI

SummarySmart 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.

ReferencesA 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_the_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.

Thank You!

Backups

AlexanderRyjov.PersonalizationofSocialNetworks:AdaptiveSemanticLayerApproach.In:SocialNetworks:AFrameworkofComputationalIntelligence.Ed.byWitoldPedryczandShyi-MingChen.SpringerVerlag,2014p.21-40.http://link.springer.com/chapter/10.1007%2F978-3-319-02993-1_2