Educación Superior e Innovación Tecnológica para el Desarrollo San Salvador, 22 de noviembre de 2006

Educacin Superior e Innovacin Tecnolgica para el Desarrollo San Salvador, 22 de noviembre de 2006

el futuro de la educacin superior?

estn listas las instituciones de educacin superior?

esquema de la presentacin

el conocimiento para el desarrollolas nuevas necesidades de educacin e investigacinel paisaje cambiante de la educacin superiorlos desafos

el desarrollo econmico est cada da ms vinculado a la capacidad de adquisicin y aplicacin de los conocimientos

Conclusin

conocimientos y crecimientoBrazil y Korea K4D program

aplicacin del conocimiento para combatir el hambreWheat Yields in Argentina and India 1885-1995Adapted from: Pardey, Chang-Kang and Alston

Sheet:

Argentina

India

resolucin de problemas de medio ambiante

vivir con los terremotos

el conocimiento para la seguridad

aceleracin de la creacin de nuevos conocimientos ...

el desarrollo econmico no es secuencialsaltos cualitativos gracias a la tecnologa

mercado pequeo o malo clim de inversin?

calidad de los productos exportados

el desarrollo econmico no es secuencialen la mayora de los casos, la prioridad es adoptar y adaptar tecnologas existentes, y no inventar cosas nuevas

pero no se trata simplemente de comprar maquinas sino de aprender a usar y adaptar la tecnologa


el conocimiento para el desarrollolas nuevas necesidades de educacin e investigacin

cambios en los requerimientos de aprendizajeniveles ms altos de calificacin

FemaleMaleRelative Earning Gaps Are Increasinglos individuos se benefician de la inversin en educacin

Males

-221636

-154036

-14737

-46038

-124541

-12543

-182950

-132053

-282457

-201660

-191464

-257778

-122979

-405090

-729100

-3125102

-19105152

% index260

100 40

Below upper secondary

Tertiary-type B

Tertiary-type A and advanced research

Females

-39015

-101425

-164536

-191846

-273148

-203258

-93458

-261659

-311860

-253763

-42-570

-301570

-282772

-12476

-234380

-332683

-373388

-3042106

100

Source: Table A14.1 in OECD 2003

200

40


Tertiary-type B


Figure 1. Relative Earnings with Income from Employment (2001)-Femalesby level of educational attainment and gender for 25 to 64-year-olds (upper secondary education=100)

Sheet1

Table A14.1

Relative earnings of the population with income from employment by level of educational attainment and gender for 25 to 64-year-olds and 30 to 44-year-olds (upper secondary education =100)

Below upper secondaryPost-secondary nontertiaryTertiary-type BTertiary-type A and advanced researchAll tertiary educationBelow upper secondaryTertiary-type BTertiary-type A and advanced researchAll tertiary education

25-6430-4425-6430-4425-6430-4425-6430-4425-6430-4425-6425-6425-6425-64

(1)(2)(3)(4)(5)(6)(7)(8)(9)-10

Italy1998Females6156mm0x(8)115114115114-39n/a15-15

Denmark2000Females908992109114112125122123121-101425-23

Norway2000Females8488121118145151136138137139-164536-37

Australia1999M+F8179112118118118146146136136-191846-36

Netherlands1997Females7373120124131136148154146152-273148-46

France1999Females8081133108132139158165145152-203258-45

Australia1999Females9189116113134132158158150148-93458-50

Germany2000Females7473128127116118159158141142-261659-41

Korea1998Females6975mm118138160181141164-311860-41

Switzerland2002Females7576122124137146163171154162-253763-54

Ireland1998Females585980829581170166140133-42-570-40

Canada1999Females70679889115115170184139144-301570-39

Czech Republic1999Females7275aa127124172176170174-282772-70

Finland1999Females9996mm124123176172145141-12476-45

Hungary2001Females7780128124143128180174179174-234380-79

United States2002Females6766120123126129183189176180-332683-76

Portugal2000Females6358mm133139188206170185-373388-70

United Kingdom2002Females7074mm142133206216183183-3042106-83

Denmark2000M+F8785100106110111127123124121-131027-24

Italy1998M+F5857mm0x(8)127126127126-42n/a27-27

Norway2001M+F8590124120155155132133135135-155532-35

Netherlands1997M+F8584121119139131144139144138-153944-44

Korea1998M+F7880mm106113147142135134-22647-35

Ireland1998M+F77796968108114153153138137-23853-38

Canada1999M+F8079102100113113163167136137-201363-36

Germany2000M+F7680115114117116165163145143-241765-45

Switzerland2003M+F7979114116147150167165159159-214767-59

France1999M+F8484130112125133169174150155-162569-50

United Kingdom2003M+F6768mm128124174181159161-332874-59

Czech Republic1999M+F6870aa151151180182179181-325180-79

Finland1999M+F9694mm120115190179153144-42090-53

Portugal2001M+F6258mm141146192202178187-384192-78

United States2003M+F7069121122123122195192186183-302395-86

Hungary2001M+F7778131126164144210203210202-2364110-110

Ireland1998Males78838055116125136142130135-221636-30

Norway1999Males8589118116140143136138136138-154036-36

Denmark2000Males86839194107107137134131128-14737-31

Italy1998Males5455mm0x(8)138142138142-46n/a38-38

Netherlands1997Males8886126121145130141133142132-124541-42

Korea1998Males8890mm105109143136132129-12543-32

Switzerland2001Males8282113109129130150146141139-182950-41

Australia1999Males8785111116120122153152141142-132053-41

United Kingdom2001Males7267mm124126157162147151-282457-47

Canada1999Males8078102101116117160159138137-201660-38

Germany2000Males8188114117114112164163143141-191464-43

Czech Republic1999Males7577aa177182178176178177-257778-78

France1999Males8886130118129137179182159163-122979-59

Portugal1999Males6057mm150155190194180185-405090-80

Finland1999Males9390mm129125200188167159-729100-67

United States2001Males6969123125125125202199193190-3125102-93

Hungary2001Males8181140137205182252253252253-19105152-152

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6

Males

-221636

-154036

-14737

-46038

-124541

-12543

-182950

-132053

-282457

-201660

-191464

-257778

-122979

-405090

-729100

-3125102

-19105152

% index260

100

40



Tertiary-type B


Figure 1. Relative Earnings with Income from Employment (2001)-Malesby level of educational attainment and gender for 25 to 64-year-olds (upper secondary education=100)

Females

-39015

-101425

-164536

-191846

-273148

-203258

-93458

-261659

-311860

-253763

-42-570

-301570

-282772

-12476

-234380

-332683

-373388

-3042106

% index220

100

40



Tertiary-type B


Sheet1

Table A14.1

Relative earnings of the population with income from employment by level of educational attainment and gender for 25 to 64-year-olds and 30 to 44-year-olds (upper secondary education =100)

Below upper secondaryPost-secondary nontertiaryTertiary-type BTertiary-type A and advanced researchAll tertiary educationBelow upper secondaryTertiary-type BTertiary-type A and advanced researchAll tertiary education

25-6430-4425-6430-4425-6430-4425-6430-4425-6430-4425-6425-6425-6425-64

(1)(2)(3)(4)(5)(6)(7)(8)(9)-10

Italy1998Females6156mm0x(8)115114115114-39n/a15-15

Denmark2000Females908992109114112125122123121-101425-23

Norway2000Females8488121118145151136138137139-164536-37

Australia1999M+F8179112118118118146146136136-191846-36

Netherlands1997Females7373120124131136148154146152-273148-46

France1999Females8081133108132139158165145152-203258-45

Australia1999Females9189116113134132158158150148-93458-50

Germany2000Females7473128127116118159158141142-261659-41

Korea1998Females6975mm118138160181141164-311860-41

Switzerland2002Females7576122124137146163171154162-253763-54

Ireland1998Females585980829581170166140133-42-570-40

Canada1999Females70679889115115170184139144-301570-39

Czech Republic1999Females7275aa127124172176170174-282772-70

Finland1999Females9996mm124123176172145141-12476-45

Hungary2001Females7780128124143128180174179174-234380-79

United States2002Females6766120123126129183189176180-332683-76

Portugal2000Females6358mm133139188206170185-373388-70

United Kingdom2002Females7074mm142133206216183183-3042106-83

Denmark2000M+F8785100106110111127123124121-131027-24

Italy1998M+F5857mm0x(8)127126127126-42n/a27-27

Norway2001M+F8590124120155155132133135135-155532-35

Netherlands1997M+F8584121119139131144139144138-153944-44

Korea1998M+F7880mm106113147142135134-22647-35

Ireland1998M+F77796968108114153153138137-23853-38

Canada1999M+F8079102100113113163167136137-201363-36

Germany2000M+F7680115114117116165163145143-241765-45

Switzerland2003M+F7979114116147150167165159159-214767-59

France1999M+F8484130112125133169174150155-162569-50

United Kingdom2003M+F6768mm128124174181159161-332874-59

Czech Republic1999M+F6870aa151151180182179181-325180-79

Finland1999M+F9694mm120115190179153144-42090-53

Portugal2001M+F6258mm141146192202178187-384192-78

United States2003M+F7069121122123122195192186183-302395-86

Hungary2001M+F7778131126164144210203210202-2364110-110

Ireland1998Males78838055116125136142130135-221636-30

Norway1999Males8589118116140143136138136138-154036-36

Denmark2000Males86839194107107137134131128-14737-31

Italy1998Males5455mm0x(8)138142138142-46n/a38-38

Netherlands1997Males8886126121145130141133142132-124541-42

Korea1998Males8890mm105109143136132129-12543-32

Switzerland2001Males8282113109129130150146141139-182950-41

Australia1999Males8785111116120122153152141142-132053-41

United Kingdom2001Males7267mm124126157162147151-282457-47

Canada1999Males8078102101116117160159138137-201660-38

Germany2000Males8188114117114112164163143141-191464-43

Czech Republic1999Males7577aa177182178176178177-257778-78

France1999Males8886130118129137179182159163-122979-59

Portugal1999Males6057mm150155190194180185-405090-80

Finland1999Males9390mm129125200188167159-729100-67

United States2001Males6969123125125125202199193190-3125102-93

Hungary2001Males8181140137205182252253252253-19105152-152

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6

Corea y El Salvador

19601980200080%3%17%49%42%9%18%55%26%

Chart8

40-40

8.7-8.7

1.3-1.3

20.8-20.8

24.6-24.6

4.6-4.6

9.2-9.2

27.6-27.6

13.15-13.15

DATA_15

IndiaKoreaArgentina

Primary48.4-48.4Primary40-40Primary41-41

Secondary1.4-1.4Secondary8.7-8.7Secondary7.4-7.4

Tertiary0.15-0.15Tertiary1.3-1.3Tertiary1.6-1.6

Primary39.6-39.6Primary20.8-20.8Primary33.55-33.55






PakistanMalaysiaBarbados






Tertiary0.85-0.85Tertiary1-1Tertiary2.1-2.1




BangladeshPhilippinesBelize





Secondary7.25-7.25Secondary13-13Secondary11-11


Primary41.5-41.5Primary18.1-18.1Primary00

Secondary7-7Secondary20.3-20.3Secondary00

Tertiary1.5-1.5Tertiary11.6-11.6Tertiary00

IndonesiaSingaporeBolivia



Tertiary0.05-0.05Tertiary00Tertiary1.2-1.2






Tertiary2.25-2.25Tertiary5-5Tertiary6.5-6.5

Hong KongFinlandBrazil










FranceGermany, WestChile










ItalyJapanColombia


Secondary8.3-8.3Secondary22.4-22.4Secondary7-7








PolandNetherlandsCosta Rica







Primary18-18Primary14.6-14.6Primary33.2-33.2



RomaniaSwedenDominica



Tertiary1-1Tertiary3.2-3.2Tertiary0.25-0.25

Primary22.95-22.95Primary19-19Primary45.05-45.05




Secondary34-34Secondary30.7-30.7Secondary8.3-8.3


NorwayUnited KingdomDominican Rep.

Primary37.85-37.85Primary33.65-33.65Primary48-48









SpainUnited StatesEcuador




Primary33.1-33.1Primary3-3Primary33.9-33.9






ZimbabweUruguayEl Salvador










ZambiaVenezuelaGuatemala






Tertiary0.15-0.15Tertiary3.2-3.2Tertiary1-1




GhanaSt.Kitts& NevisGuyana




Primary36.7-36.7Primary00Primary36.25-36.25

Secondary13-13Secondary00Secondary13-13



Secondary12.85-12.85Secondary00Secondary20.05-20.05


TaiwanSt.LuciaHaiti










CanadaSt.Vincent & G.Honduras










U.S.S.R.ChinaJamaica










South AfricaDenmarkMexico



Tertiary00Tertiary7.35-7.35Tertiary0.65-0.65







KenyaPeruNicaragua


Secondary1.15-1.15Secondary6-6Secondary2.6-2.6








UgandaTrinidad & Tob.Panama



Tertiary00Tertiary0.35-0.35Tertiary1.1-1.1







SenegalParaguay

Primary47.55-47.55Primary44.95-44.95

Secondary2.2-2.2Secondary4.5-4.5

Tertiary0.25-0.25Tertiary0.5-0.5

Primary46.7-46.7Primary37.85-37.85



Primary44.7-44.7Primary34.05-34.05



DATA_15

00

00

00

00

00

00

00

00

00

00

00

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6


flexibilidad para adaptarse al cambio

Cambios en las abilidades requeridas en EEUU(1960 1998)

Source: Autor, Levy, and Murnane (2003) The Skill Content of Recent Technological Change: An Empirical Exploration, Quarterly Journal of Economics.

Chart1

00000

21.40.80-1

42.71.7-0.2-2

6.64.11.1-1.8-3.2

9.25.80.2-3.3-4.5

11.87.3-1.4-5.9-5

13.48.3-2.7-7.7-5.5

Expert Thinking

ComplexCommunication

Routine Manual

Routine Cognitive

Non-Routine Manual

Expert Thinking

Complex Communication

Routine Manual

Routine Cognitive

Non-Routine Manual

Percentile Change

Sheet1

1969197419791984198919941998

Expert Thinking0246.69.211.813.4

Complex Communication01.42.74.15.87.38.3

Routine Manual00.81.71.10.2-1.4-2.7

Routine Cognitive00-0.2-1.8-3.3-5.9-7.7

Non-Routine Manual0-1-2-3.2-4.5-5-5.5

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6

resultados PISAOECD Average

Chart7

12.48262629824.88503762226.8736539926.0279666813.5378560488.0999450405

36.79381405452.0200914250.00062603763.25039954743.9983574521.6998325525

67.69781940379.56703689175.23959665590.56583409178.33716723844.5624607245

88.5457667395.19228968491.75666499398.789980308295.37667787371.7980725805

96.226981218699.528136861998.0793625199.946177233399.517271448291.8841354775

99.999999999799.9999999998100.0000000006100.000000000299.9999999999100.0000000001

Turkey

Mexico

Brazil

Indonesia

Thailand

OECD total

PISA Proficiency Level

Cumulative % of Learners

old_graph

updated_graph

Table 6.1

Percentage of students at each level of proficiency on the reading scale

CountryProficiency levels

Below Level 1(below 335 score points)Level 1(from 335 to 407 score points)Level 2(from 408 to 480 score points)Level 3(from 481 to 552 score points)Level 4(from 553 to 625 score points)Level 5(above 625 score points)

%%%%%%

Mexico24.927.127.515.64.30.5

Turkey12.524.330.920.87.73.8

Brazil26.923.125.216.56.31.9

Indonesia26.037.227.38.21.20.1

Thailand13.530.534.317.04.10.5

OECD total8.113.622.927.220.18.1

CountryCumilative Proficiency Levels


flexibilidad para adaptarse al cambio

educacin a lo largo de la vida

de la inocencia

a la sabiduria

responder a las necesidades locales y regionales

ventas de Nokia

responder a las necesidades locales y regionales

resultados de la investigacintransferencia de la tecnologapreparacin de graduados competentes capaces de facilitar la adaptacin de nuevas tecnologas dentro de la empresa


el conocimiento para el desarrollolas nuevas necesidades de educacin e investigacinel paisaje cambiante de la educacin superior

el cambiante paisaje de la educacin superior

nuevas formas de competencia larga distancia (universidades virtuales)

campus remote

nuevas formas de competencia


nuevas formas de competencia larga distancia (universidades virtuales) universidades de franquicia

no es necesario ir a Londres!


nuevas formas de competencia larga distancia (universidades virtuales) universidades de franquicia universidades corporativas


nuevas formas de competencia larga distancia (universidades virtuales) universidades de franquicia universidades corporativasempresas de comunicacin, (radios, TV), bibliotecas, museos



cambios en las institucionesnuevos clientes

universidad del futuro postgradoslicenciaturacursos continuos



cambios en las institucionesnuevos clientessistemas abiertos

el Banco de Crdito de Coreasistema abierto con ms de 10.000 institucionesreconocimiento de los aprendizes adquiridos dentro de instituciones o afueraotorgacin de diplomas oficiales

ceremonia de grado



cambios en las institucionesnuevos clientessistemas abiertosinter y multi-disciplinaridad



cambios en las institucionesnuevos clientessistemas abiertosinter y multi-disciplinaridadnuevas prcticas pedaggicas ms activas e inter-activas

nuevas formas de aprendizaje (I)informacin presentada de multiples formas expresivas mayor motivacin y eficiencia del aprendizaje

superacin de la distancia espacial y temporal entre maestro y alumno

nuevas formas de aprendizaje (II)mayor individualizacin y flexibilizacin del aprendizaje adecuado a las necesidades particulares

redes de trabajo colaborativo (comunidades virtuales) entremaestros y alumnosmaestros y maestrosalumnos y alumnos

En el siglo veinte-uno, la gente podr estudiar lo que quieren, cuando quieren, donde quieren, y usando el idioma que prefieren, de manera electrnica.

Peter Knight, julio de 1994


el conocimiento para el desarrollolas nuevas necesidades de educacin e investigacinel paisaje cambiante de la educacin superiorlos desafos

oportunidades o desafos?

los desafos nivel mundial

sostenabilidad financiera

integracin de las TICs

promocin de la calidad

flexibilidad

que significa ser una institucin de nvel mundial? universidad que hace investigacin o todos tipos de instituciones?

todas las instituciones o algunas selectadas?

computadores personales por cada 1,000 personas, 2004

telephone

3

26

19

86

102

190

203

438

482

590

583

582

685

664

internet

0

0.32

0.48

0.91

3.55

8.84

27.55

48.45

100.65

167.11

233.29

385.73

692.29

981.74

2419.86

Computers

19

44

67

82

96

105

108

133

238

257

487

545

561

574

682

749

Scientist

87

173

2417

2512

2537

2636

2647

3016

3732

data

World Development Report 2000/01

Selected indicators of information and telecommunications

penetration by country income level

Country/GroupTelephone main lines per 1000 people, 1999Country/GroupInternet users per 10,000 people, 2000

Bangladesh3Bangladesh0

Kenya10Kenya0.32

Low-income economies26Low-income economies0.48

Sri Lanka19Sri Lanka0.91

Thailand86Lower-middle-income economies3.55

Lower-middle-income economies102Thailand8.84

Upper middle income economies190Malaysia27.55

Malaysia203Upper middle income economies48.45

Korea438Korea100.65

Singapore482France167.11

Germany590Germany233.29

High income economies583Singapore385.7

France582Denmark692.3

Denmark685High income economies981.74

USA664USA2419.86

Country/GroupPersonal computers per 1000 people, 2004

Guatemala19

El Salvador44

Colombia67

R.B. de Venezuela82

Argentina96

Brazil105

Mexico108

Chile133

Costa Rica238

Espana257

Francia487

Corea545

Alemania561

Economias de alto ingreso574

Holand682

EEUU749

CountryScientists and engineers in R&D, per million people. 1981-95

Malaysia87

Thailand173

UK2,417

Singapore2,512

France2,537

South Korea2,636

Denmark2,647

Germany3,016

USA3,732

34

World Bank User:For France 1994 was not available so I used 1993

World Bank User:1990




World Bank User:For France 1994 was not available so I used 1993



la brecha digital

Sheet7

Chart1

Chart2

65.3

22.4

6.4

5.9

Distribution of Internet Hosts

Chart3

65.3

22.4

6.4

5.9

Series 1

(i) Distribution of Internet Hosts

Developing Countries (5.9%)

Australia, Japan & New Zealand (6.4%)

Europe 22.4%

Canada & United States(65.3%)

Chart4

5.1

12

2.5

80.4

(ii) Distribution of World Population

Canada & United States (5.1%)

Europe (12.0%)



Sheet1

Use of the Internet


Canada & USA65.3

Europe22.4

Australia, Japan, New Zealand6.4

Developing Countries5.9

Source: International Telecommunication Union, 1999.

Distribution of World Population

Canada & USA5.1

Europe12



Source: United Nations Population Fund, 2000.

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6

Sheet7

Chart1

Chart2

65.3

22.4

6.4

5.9


Chart3

65.3

22.4

6.4

5.9

Series 1

(i) Distribution of Internet Hosts



Europe 22.4%

Canada & United States(65.3%)

Chart4

5.1

12

2.5

80.4

(ii) Distribution of World Population

Canada & United States (5.1%)

Europe (12.0%)



Sheet1

Use of the Internet


Canada & USA65.3

Europe22.4



Source: International Telecommunication Union, 1999.

Distribution of World Population

Canada & USA5.1

Europe12



Source: United Nations Population Fund, 2000.

Sheet2

Sheet3

Sheet4

Sheet5

Sheet6

nuevos desafos de calidad

universidades virtuales

e-learning

universidades de franquicia





flexibilidad

flexibilidad

planeacin estratgica para orientar el cambio

vnculos estrechos con el mundo productivo

capacidad de reaccionar y adaptarse rpidamente a los cambios

conclusin

un mundo de ciencia ficcin?

social and economic progress is achieved principally through the advancement and application of knowledge

World Development Report 1998/99

la universidad de ladrillo

la universidad click

compitiendo en la sociedad del conocimiento

Crisis

Peligro

Oportunidad

Crisis = Peligro + Oportunidad

existe una visin hacia el futuro?

In particular universalizing basic education and adapting to the knowledge economyWe are not as involved with clients as we shouldOur capacity to respond to new challenges has been severely eroded for clients to deal with new knowledge or within the institution in moving towards programmatic lendingUncoordinated series of responses WBI,IFC, infodevHumanoids With Attitude Japan Embraces New Generation of Robots By Anthony FaiolaWashington Post Foreign ServiceFriday, March 11, 2005; Page A01 TOKYO -- Ms. Saya, a perky receptionist in a smart canary-yellow suit, beamed a smile from behind the "May I Help You?" sign on her desk, offering greetings and answering questions posed by visitors at a local university. But when she failed to welcome a workman who had just walked by, a professor stormed up to Saya and dished out a harsh reprimand."You're so stupid!" said the professor, Hiroshi Kobayashi, towering over her desk."Eh?" she responded, her face wrinkling into a scowl. "I tell you, I am not stupid!"Truth is, Saya isn't even human. But in a country where robots are changing the way people live, work, play and even love, that doesn't stop Saya the cyber-receptionist from defending herself from men who are out of line. With voice recognition technology allowing 700 verbal responses and an almost infinite number of facial expressions from joy to despair, surprise to rage, Saya may not be biological -- but she is nobody's fool."I almost feel like she's a real person," said Kobayashi, an associate professor at the Tokyo University of Science and Saya's inventor. Having worked at the university for almost two years now, she's an old hand at her job. "She has a temper . . . and she sometimes makes mistakes, especially when she has low energy," the professor said.Saya's wrath is the latest sign of the rise of the robot. Analysts say Japan is leading the world in rolling out a new generation of consumer robots. Some scientists are calling the wave a technological force poised to change human lifestyles more radically than the advent of the computer or the cell phone. Though perhaps years away in the United States, this long-awaited, as-seen-on-TV world -- think "The Jetsons" or "Blade Runner" -- is already unfolding in Japan, with robots now used as receptionists, night watchmen, hospital workers, guides, pets and more. The onslaught of new robots led the government last month to establish a committee to draw up safety guidelines for the keeping of robots in homes and offices. Officials compiled a report in January predicting that every household in Japan will own at least one robot by 2015, perhaps sooner.Scientists and government authorities have dubbed 2005 the unofficial "year of the robot," with humans set to interact with their electronic spawn as never before at the 2005 World Expo opening just outside the city of Nagoya on March 25. At the 430-acre site, 15 million visitors are expected to mingle with some of the most highly developed examples of Japanese artificial intelligence, many of which are already on sale or will be within a year.Greeting visitors in four languages and guiding them to their desired destinations will be Mitsubishi Heavy Industries' yellow midget robot, Wakamaru. A trio of humanoid robots by Sony, Toyota and Honda will be dancing and playing musical instruments at the opening ceremony. Parents visiting the World Expo can leave their children in the care of a robotic babysitter -- NEC's PaPeRo -- which recognizes individual children's faces and can notify parents by cell phone in case of emergency.Also on display: a wheelchair robot now being deployed by the southern city of Kitakyushu that independently navigates traffic crossings and sidewalks using a global positioning and integrated circuit chip system. In June, Expo visitors can enter a robot room -- a more distant vision of the future where by 2020 merely speaking a word from your sofa will open the refrigerator door, allowing your personal robot assistant to deliver the cold beverage of your choice."We have reached the point in Japan of a major breakthrough in the use of robot technology and our society is changing as a result," said Kazuya Abe, a top official at NEDO, the national institute in charge of coordinating science research and development. "People are and will be living alongside robots, which are seen here as more than just machines. This is all about AI" -- artificial intelligence, Abe said -- "about the creation of something that is not human, but can be a complement or companion to humans in society. That future is happening here now."While employing a measure of new technology, many such robots are envisioned merely as new interfaces -- more user-friendly means of combining existing ways of accessing the Internet or reaching loved ones through cell phone networks.In the quest for artificial intelligence, the United States is perhaps just as advanced as Japan. But analysts stress that the focus in the United States has been largely on military applications. By contrast, the Japanese government, academic institutions and major corporations are investing billions of dollars on consumer robots aimed at altering everyday life, leading to an earlier dawn of what many here call the "age of the robot."But the robotic rush in Japan is also being driven by unique societal needs. Confronting a major depopulation problem due to a record low birthrate and its status as the nation with the longest lifespan on Earth, Japanese are fretting about who will staff the factory floors of the world's second-largest economy in the years ahead. Toyota, Japan's biggest automaker, has come up with one answer in moving to create a line of worker robots with human-like hands able to perform multiple sophisticated tasks.With Japanese youth shying from so-called 3-K jobs -- referring to the Japanese words for labor that is dirty, dangerous or physically taxing -- Alsok, the nation's second-largest security guard company, has developed a line of robo-cops. The guard robots, one version of which is already being used by a client in southern Japan, can detect and thwart intruders using sensors and paint guns. They can also put out fires and spot water leaks.It is perhaps no surprise that robots would find their first major foothold in Japan. Japanese dolls and toys, including a moving crab using clockwork technology dating to the 1800s, are considered by some to be among the first robots. Rather than the monstrous Terminators of American movies, robots here are instead seen as gentle, even idealistic creatures epitomized by Astroboy, the 1960s Japanese cartoon about an electronic kid with a big heart."In Western countries, humanoid robots are still not very accepted, but they are in Japan," said Norihiro Hagita, director of the ATR Intelligent Robotics and Communication Laboratories in Keihanna Science City near Kyoto. "One reason is religion. In Japanese [Shinto] religion, we believe that all things have gods within them. But in Western countries, most people believe in only one God. For us, however, a robot can have an energy all its own."A case in point is the Paro -- a robotic baby harp seal, developed with $10 million in government grants, that went on sale commercially this month for $3,500 each. All 200 units sold out in less than 50 hours.The seal is meant to provide therapy for the elderly who are filling Japanese nursing homes at an alarming rate while often falling prey to depression and loneliness.With 30 sensors, the seal begins over time to recognize its master's voice and hand gestures. It coos and flaps its furry white down in delight at gentle nuzzles, but squeals in anger when handled roughly.Researchers have been testing the robot's effect on the elderly at a nursing home in Tsukuba, about 40 miles northeast of Tokyo. During a recent visit by a reporter, the sad eyes of elderly residents lit up as the two resident robot seals were brought out. Tests have shown that the cute newcomers indeed reduce stress and depression among the elderly. Just ask Sumi Kasuya, 89, who cradled a seal robot while singing it a lullaby on a recent afternoon."I have no grandchildren and my family does not come to see me very often," said Kasuya, clutching fast to the baby seal robot wiggling in her arms. "So I have her," she said, pointing to the seal. "She is so cute, and is always happy to see me."Special correspondent Akiko Yamamoto contributed to this report.

2005 The Washington Post CompanyIn particular universalizing basic education and adapting to the knowledge economyWe are not as involved with clients as we shouldOur capacity to respond to equity challenges has been severely eroded for clients to deal with new knowledge or within the institution in moving towards programmatic lendingUncoordinated series of responses WBI,IFC, infodevWHY DO WE SUPPORT HIGHER EDUCATION? IT IS WIDELY RECOGNIZED THAT ECONOMIC DEVELOPMENT IS INCREASINGLY LINKED TO A NATIONS ABILITY TO ACQUIRE AND APPLY TECHNICAL AND SOCIO-ECONOMIC KNOWLEDGE AND THAT THE PROCESS OF GLOBALIZATION IS ACCELERATING THIS TREND.

OUR 1998 WORLD DEVELOPMENT REPORT OBSERVES THAT THE CAPACITY TO ADOPT AND DISSEMINATE RAPID TECHNOLOGICAL ADVANCES IS DEPENDENT ON BETTER PUBLIC SUPPORT FOR tertiary EDUCATION. In this graph we compare the per capita income growth of Brazil and Korea. Forty years ago they had the same per capita income. Now Koreas per capita income is 4.7 times that of Brazil. Why is the such a difference. One reason is that Korea has had a higher rate of investment to GDP. However, Korea has also been better at harnessing knowledgeboth technical and policy knowledgefor its development. In this graph we decompose the per capita income growth for Korea in that which can be explained by increases in the labor force and in capital. The per capita income that would result from simple factor accumulation is shown by the red line. The difference between the red line and the actual per capita income growth in Korea can be attributed broadly to better use of knowledge---both technical and policy knowledge. The key point here is that the effective us of knowledge, which depends on knowledge and skills and innovation, can make a very big impact on growth performance. Brazil needs to do more to improve the effectiveness with which it uses knowledge for its growth and development. (Chart measures average annual yields in 1,000 kilograms per hectare)

Statistics to add:Beginning in the 1930s agricultural yields worldwide began to rise sharply. In areas for which data exist, agricultural productivity remained at a constant (comparatively) low level for hundreds years, before rising sharply with the application of mechanization, and then rising again, even more sharply, after the Second World War with the application of modern scientific knowledge to the processes of cultivation as pictured in the graph that charts the productivity increases (as a percentage of 1967 yields) as they altered with the introduction of various technological breakthroughs.

1.1 Billion people without access to clean, safe drinking water.

Frenchman Alain Gachet pictured as he pinpoints water sources in the Chad/Sudan area with amazing accuracy due, oddly enough, to the space shuttle and the end of the cold war. He fused together an unprecedented set of maps, including newly released topo- graphic ones from the shuttle and previously unavailable radar ones that peer 20 yards underground. Now he's put the data into his GPS device. When he says, "Dig here!" aid workers listen.

A recent World Health Organization survey found that 6,000 to 10,000 people are dying in Darfur each month, citing lack of clean water as one of the major reasons

(From the Christian Science Monitor, September 20, 2004. A Frenchman Who Can See Water Beneath the Sahara.)

1.1 Billion people without access to clean, safe drinking water.

Frenchman Alain Gachet pictured as he pinpoints water sources in the Chad/Sudan area with amazing accuracy due, oddly enough, to the space shuttle and the end of the cold war. He fused together an unprecedented set of maps, including newly released topo- graphic ones from the shuttle and previously unavailable radar ones that peer 20 yards underground. Now he's put the data into his GPS device. When he says, "Dig here!" aid workers listen.

A recent World Health Organization survey found that 6,000 to 10,000 people are dying in Darfur each month, citing lack of clean water as one of the major reasons

(From the Christian Science Monitor, September 20, 2004. A Frenchman Who Can See Water Beneath the Sahara.)

Volcano Nevado del Ruiz

Nevado del Ruiz volcanoLocated in the Andes mountains of South America, Nevado del Ruiz is the northernmost and highest Colombian volcano with historical activity. With a summit elevation of 5,389 m, the volcano is covered with 25 km2 of snow and ice even though it's located only 500 km from Earth's equator. View is from the northeast. Beginning in November 1984, the volcano began showing clear signs of unrest, including earthquakes, increased fumarolic activity from the summit crater, and small phreatic explosions.

Broad summit of Nevado del Ruiz. An explosive eruption from Ruiz's summit crater on November 13, 1985, at 9:08 p.m. generated an eruption column and sent a series of pyroclastic flows and surges across the volcano's broad ice-covered summit. Within minutes, pumice and ash began to fall to the northeast along with heavy rain that had started earlier in the day. The crater was enlarged slightly by the eruption, and the summit area was quickly covered with layers of pyroclastic flow deposits as thick as 8 m. This eruption was preceded by a strong phreatic (steam) explosion from the crater at 3:05 p.m. In this view, the dark pyroclastic-flow deposits are partly covered with fresh snow.

Gual River valley. Flowing downstream from Ruiz at an average speed of 60 km per hour, lahars eroded soil, loose rock debris and stripped vegetation from river channels. By incorporating water and debris from along river channels, the lahars grew in size as they moved away from the volcano--some lahars increased up to 4 times their initial volumes. In some of the narrow canyons downstream from the volcano, as shown here in the Gual River, lahars were as thick as 50 m!

Ro Lagunillas, former location of Armero. Within four hours of the beginning of the eruption, lahars had traveled 100 km and left behind a wake of destruction: more than 23,000 people killed, about 5,000 injured, and more than 5,000 homes destroyed along the Chinchin, Gual, and Lagunillas rivers. Hardest hit was the town of Armero at the mouth of the Ro Lagunillas canyon, which was located in the center of this photograph. Three quarters of its 28,700 inhabitants perished

Accounts from survivors indicate Armero was inundated with several pulses of flowing material. The first arrived at 11:25 p.m. and consisted of a flood of cold relatively clean water that overflowed the Ro Lagunillas channel, sweeping into downtown Armero. Only a few centimeters deep in town, this water was from a lake located just upstream that had been displaced when lahars entered the lake.The second pulse arrived at 11:35 p.m. This was the largest pulse and within 10 to 20 minutes, destroyed most of the buildings and swept away most of the people in Armero. Flow depths of the lahar ranged from 2 to 5 m.The third pulse arrived at 11:50 p.m. with a velocity of about half of the second one. Then, in the next hour or so, a series of smaller pulses (6 to 8) was experienced by survivors trapped in the mud. These pulses lifted people floating in the mud and pushed them a few meters ahead.One last pulse struck Armero a short time after 1 a.m. on November 14.

Lahar generation: key lessonsFor the generation of lahars on ice- and snow-covered volcanoes, the deadly 1985 eruption of Nevado del Ruiz offers several key lessons for scientists, emergency-response professionals, and communities located downstream of such volcanoes: catastrophic lahars can be generated on ice- and snow-capped volcanoes by relatively small eruptions the surface area of snow on an ice cap can be more critical than total ice volume when considereing lahar potential placement of hot rock debris on snow is insufficient to generate lahars -- the two materials must be mechanically mixed together for rapid heat transfer lahars can increase their volumes significantly by entrainment of water and eroded sediment valley-confined lahars can maintain relatively high velocities and can have catastrophic impacts as far as 100 km downstream

Omayra Snchez, 13 year old, 13 November 1985increased rate of innovation/shorter product life cyclesUruguay vs. Sweden (Ikea)Vino y salmn en ChileUpper secondary and non-tertiary education are the threshold levels for earnings in OECD countries. Every year added after this level is associated with higher level of earnings. People with the below upper-secondary level education are in disadvantage. These graphs show evolution on educational attainment of population 15 years and older. The red bar represents the proportion that has achieved primary or lower; the blue bar indicated the proportion that has achieved some secondary education; and the green the proportion with higher education.Both countries were able to close the secondary education gap fast and in a balanced way.Effects of mass introduction of computers in the workplace on demand for skills ==) new division of labor within firmsFigure displays trend for each of five types of tasks.Expert thinking: solving problems for which there are no rule-based solutions, e.g. research, forming and testing hypotheses, medical diagnosis and diagnostic in general (resolving discrepancies).Complex communication: interacting with humans to acquire information, to explain it, or to persuade others of its implications for action, e.g. persuading, selling, legal writing, managing others.Routine cognitive tasks: mental tasks that are well described by logical rules, e.g. maintaining expense reports, record keeping, repetitive customer service, bank teller.Routine manual tasks: physical tasks that can be well described using rules, e.g. counting and packaging pills, repetitive assembly.Non-routine manual tasks: physical tasks that cannot be well described as following a set of If-Then-Do rules instead, they require optical recognition and fine muscle control, e.g. janitorial services, truck driving.Each trend reflects changes in the numbers of people employed in occupations emphasizing that task.Importance of each task in US economy is set to 0 in 1969, value in each subsequent year represents percentile change in importance of each type of task in economy.Rules-based tasks (routine tasks) where computers can substitute for humans, have decline.

In middle income countries few students perform on PISA at the OECD averageBut not all middle income countries are the sameMexico, Thailand, Brazil and Indonesia have large number of students at the lower end of the distributionTurkey does better, with about 20 % at the top.

Transfer from CC to URegistered in one main institution but courses from other universitiesRegular degree with online certificates from professional institutions (Yacine Toronto, U of Dar Es Salaam)Cerebro del tamao de una Finnish prime minister: responsiveness to changeFrance: medicine courses with double shift and no questions asked to equalize opportunitiesDESPITE THE SUSTAINED EFFORTS OF COUNTRIES TO INVEST IN THE DEVELOPMENT OF THEIR tertiary EDUCATION SYSTEM, THERE REMAIN MANY DAUNTING CHALLENGES AHEAD.

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Educación Superior e Innovación Tecnológica para el Desarrollo San Salvador, 22 de noviembre de 2006