(Draft 1: Conservative estimates)

Transcription

1 Summary of Dongchan Lee s new discoveries after the release of the TIMSS 2015 and PISA 2015 results on math, science, and readings & the historic impacts for the global math education and economy of all the nations Prepared by Dongchan Lee Date: Janaury 13 th, 2017 (Draft 1: Conservative estimates) All rights reserved by Dongchan Lee

2 GDP PER CAPITA BASED ON PPP IN 2015 s s NORMALIZED COMPOSITE MATH SCORES OF ALL TIMSS AND PISA MATH VS. GDP PER CAPITA BASED ON PPP IN 2015 AFTER 12 OUTLIERS (8 TOP OIL -RICHEST COUNTRIES & VIETNAM) & 4 OTHERS (FROM THE TOTAL 84 COUNTRY DATA SET). TAIPEI MISSING LIE $82,700 $72,700 SGP y = e x R² = An amazing power of reducing the math poverty in the developed nations (the OECD level nations) by 20-30%. $62,700 $52,700 $42,700 $32,700 $22,700 $12,700 DOM SLV KGZ USA CHE IRL AUT NLD DEU SWEAUS DNK CAN ISL BEL GBR FIN FRA NZL ITA ISRESP MLT CZE CYP SVN PRT LTU SVK EST GRC MYS KAZ POL HUN RUS LVA CHL PAN URY HRV ARG ROM MUS TUR IRN MEX AZE BGR BRA MNE CRI THA COL DZAMKD LBN SRB PER TUN ALB MNG IDN JOR KSV GEO UKR MAR PHL $2, JPN HKG KOR Counttries in the regression Expon. (Counttries in the regression) You empower the math poorest out of their stangnations. The result is that you all get richer than all you richer friends that you envy. Except that you make this happen in a few or several decades instead of years. NORMALIZED COMPOSITE MATH SCORES OF ALL TIMSS AND PISA When the log is converted to the normal linear space growths.

3 LOG OF GDP PER CAPITA BASED ON PPP IN 2015 $143,489 NORMALIZED COMPOSITE MATH SCORES OF ALL TIMSS AND PISA MATH VS. LOG OF GDP PER CAPITA BASED ON PPP IN 2015 AFTER 12 OUTLIERS (8 TOP OIL-RICHEST COUNTRIES & VIETNAM) & 4 OTHERS (FROM THE TOTAL 84 COUNTRY DATA SET). TAIPEI MISSING MAC The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests $53,144 $19,683 $7,290 DOM SLV KGZ CHE USA IRL DEU NLD DNK AUT ISL SWEAUSCAN BEL FRA GBR FIN NZL ITA ISR ESP CYP MLT SVN CZE PRT LTU SVK EST MYS GRC KAZ POL HUN RUS LVA CHL PAN URY HRV ARG ROM MUS TUR IRN MEX AZEBGR BRA THA MNE CRI DZA LBN COL MKD SRB PER TUN MNG JOR ALB IDN MAR PHL KSV GEO UKR MMU1 math boost ~ 1.35 STDEV, which occupy more than ½ of the entire PISA-TIMSS math range worldwide. LIE JPN HKG KOR SGP y = e x R² = Counttries in the regression Expon. (Counttries in the regression) $2, To reduce the math poverty (about PISA math 420 cut off point) by 20-30% in the developed nations ~ is roughly the arrow growth you see here. NORMALIZED COMPOSITE MATH SCORES OF ALL TIMSS AND PISA

4 Summary of Dongchan Lee s new discoveries after the release of the TIMSS 2015 and PISA 2015 results on math, science, and readings 1) Math skills Income per capita: Math 1 STDEV of math growth ~ times GDP per capita growths in PPP 2) All developed Englihs speaking countries and most of the Latin American countries have the stronger reading scores than math scores by large margins. 3) The difference of the math scores reading scores can explain the income growths better than the mean school years if we exclude 3-6 outliers (5-10% of the participating number of nations). 4) All developed English speaking countries and most of the Latin American countries have the stronger reading scores than math scores by large margins. 5) As years go by, the impact of the relative strength of math over reading skills tend to impact the GDP per capita more strongly, about 50-75% of the overall impact levels of the math average scores to the income per capita.

5 Summary of Dongchan Lee s new discoveries after the release of the TIMSS 2015 and PISA 2015 results on math 1) The results of the PISA 2015, TIMSS 2015, and the overall patterns of the math score growths past years show that most developed nations are stagnating. 2) The overall lost years of math growth in the USA, South Korea, etc. for instance, show that the estimated GDP loss to these nations are at least 2-5 years of the annual GDP over the next half a century. (in upcoming paper). 3) In spite of the fast rise of the technology-based education, including that for mathematics in most of the developed countries, obviously the technology can t solve the math EDU stagnations. There must be alternatives. NOTE: if the governments stay as they have been, the history of the past years clearly show that there are little chances for them to raise their national math average. The technology has limits. Only MMU1 can show the first signs of evidences so that all can move forward after the initial more evidences in the developed nations rise.

6 MMU1 (Mini Mini USL1) proposals to Americas 2017 Ending Math Poverty With Dongchan Lee To raise nationally this takes years normally. 25% 25% 25% 25% Very quickly The Best math 50% (with the school teachers) as appetizers ~ 1.35 STDEV advances The Worst math 50% with Dongchan Lee

7 PISA & TIMSS math (the 2 Olympics or World Cup of math and science education assessments in the world) growth stagnations or even collapses : the average math and percentile distributions of PISA (15 years) & TIMSS (20 years) & why MMU1 by Dongchan Lee can change these all quickly 7 English-speaking developed countries, Latin American countries, and 3 of Asian Tigers Dongchan Lee, All rights reserved

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9 MMU1 is all about following the yellow arrow. MMU1 pilots are to boost the math poverty to end the math poverty: from the low 25 percentile to about 25 percentile to get the first flavor of what this is going to be like to the national governments that can fully commit and support the MMU1 initiatives. As such, this will focus first on the math poorest % of the student population in each participating cities, states, or counries.

10 See the quasi-flat growths of these nations for 12 years in PISA overall. The yellow arrow is roughly the efficiency slope boost in MMU1 level operations.

11 Qatar Albania Georgia Moldova Romania Peru Israel Malta Bulgaria Portugal Italy Montenegro Brazil Russia Colombia Mexico Poland Macao (China) Tunisia Indonesia Chile Estonia Turkey Trinidad and Tobago Slovenia Germany Singapore Norway Greece Japan Hong Kong (China) Thailand Spain Ireland Latvia Croatia Chinese Taipei United Kingdom Switzerland Jordan Luxembourg OECD average-30 Austria Denmark United States Lithuania Uruguay Korea France Hungary Canada Belgium Sweden Slovak Republic Costa Rica Netherlands Czech Republic United Arab Emirates Iceland Australia New Zealand Finland Viet Nam Score-point difference Years it take to growth 1 Standard Deviation Average three-year trend in mathematics across PISA assessments The largest math EDU collapse in 2015 for the developed nations, including 100% of the Asian Tigers and most of the English speaking nations

12 Quasi-horizontal TIMSS math growths past 20 years and what MMU1 is equivalent to do if implemented (Yellow Arrows) TIMSS Math grade 4 th slow growths TIMSS Math grade 8 th slow growths

13 Years it tkes to grow math average by 1 Standard Deviation Annualized PISA math growths till PISA 2015 Years it tkes to grow math average by 1 Standard Deviation Annualized PISA math growths till PISA These show how many generations are needed to even boost the national math by 40-80% of what MMU1 can do. Years it take to have the national math average growth by 0.5 Standard Deviation (PISA ) in English, Spanish, Portuguese, or Korean speaking countries Years it take to have the national math average growth by 0.5 Standard Deviation (PISA ) Average Annual Math score change (as % of 1 Standard Deviation or PISA 100 ponts) Years it take to have the national math average growth by 1 Standard Deviation (PISA ) in English, Spanish, Portuguese, or Korean speaking countries Years it take to have the national math average growth by 1 Standard Deviation (PISA ) Average Annual Math score change (as % of 1 Standard Deviation or PISA 100 ponts) PISA countries for math (for the average math growth trends ) PISA countries for math (for the average math growth trends )

14 The stagnations of the math growths of TIMSS grades 4 and 8 in all English speaking developed countries and some others in the next page. They are all vertical. The YELLOW ARROW is what MMU1 focuses on: to empower the math poorer 25 percentile to the 75 percentile very rapidly for the fully supporting, committed nations.

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16 The stagnations of the math growths of PISA math in all English speaking developed countries and some others in the next page. They are all horizontal. The YELLOW ARROW is what MMU1 focuses on vertically: to empower the math poorer 25 percentile to the 75 percentile very rapidly for the fully supporting, committed nations.

17 PISA math scores PISA math scores 700 United States: PISA math trajectories: Math poverty levels & percentile distributions (entire history) 700 AUSTRALIA: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile 600 math 95 percentile Years (15 years of PISA) math 75 percentile Average PISA math scores over time math 25 percentile math 5 percentile Years (15 years of PISA) math 75 percentile Average PISA math scores over time math 25 percentile (Math Poverty) math 5 percentile (Extreme Math Poverty) The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests

18 PISA math scores PISA math scores United Kingdom: PISA math trajectories: Math poverty levels & percentile distributions (entire history) CANADA: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile math 95 percentile math 75 percentile math 75 percentile Average PISA math scores over time math 25 percentile math 5 percentile Average PISA math scores over time math 25 percentile math 5 percentile The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests Years (15 years of PISA) Years (15 years of PISA)

19 PISA math scores NEW ZEALAND: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile math 75 percentile Average PISA math scores over time math 25 percentile math 5 percentile The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests Years (15 years of PISA)

20 PISA math growths : Latin American countries

21 PISA math scores 600 Brazil: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile math 75 percentile Average PISA math scores over time math 25 percentile The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests math 5 percentile Years (15 years of PISA)

22 How much can the dominance of math average over that of reading average can impact the GDP per capita as time goes on?

23 PISA math scores PISA math scores CHILE: PISA math trajectories: Math poverty levels & percentile distributions (entire history) MEXICO: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile math 75 percentile Average PISA math scores over time math 25 percentile math 5 percentile math 95 percentile math 75 percentile Average PISA math scores over time math 25 percentile math 5 percentile The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests Years (15 years of PISA) Years (15 years of PISA)

24 PISA math scores PISA math scores 550 COSTA RICA: PISA math trajectories: Math poverty levels & percentile distributions (entire history) 600 URUGUAY: PISA math trajectories: Math poverty levels & percentile distributions (entire history) 550 math 95 percentile math 95 percentile math 75 percentile Average PISA math scores over time math 75 percentile Average PISA math scores over time math 25 percentile The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests 350 math 25 percentile 300 math 5 percentile 300 math 5 percentile Years (15 years of PISA) Years (15 years of PISA)

25 Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile (math 5 percentile (E Australia Australia Australia Australia Australia Australia Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Canada Canada Canada Canada Canada Canada Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Ireland Ireland Ireland Ireland Ireland Ireland Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile United Kingdom United Kingdom United Kingdom United Kingdom United Kingdom United Kingdom Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile United States United States United States United States United States United States Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Trinidad and To Trinidad and To 2012 Trinidad and To Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile United Arab Em United Arab Em Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Singapore Singapore Singapore Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Hong Kong SAR Hong Kong SAR Hong Kong SAR Hong Kong SAR Hong Kong SAR Hong Kong SAR Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Argentina Argentina 2001 Argentina 2003 Argentina Argentina Argentina Argentina 2015 Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Brazil Brazil Brazil Brazil Brazil Brazil Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Chile Chile 2003 Chile Chile Chile Chile Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Colombia 2000 Colombia 2003 Colombia Colombia Colombia Colombia Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Costa Rica 2000 Costa Rica 2003 Costa Rica 2006 Costa Rica Costa Rica Costa Rica Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Dominican Repu Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Mexico Mexico Mexico Mexico Mexico Mexico Country Time math 95 percentile math 75 percentile Average PISA math smath 25 percentile math 5 percentile Uruguay 2000 Uruguay Uruguay Uruguay Uruguay Uruguay Source: OECD s PISA data for PISA math growths :

26 PISA math growths : Asian Tigers: developed countries

27 Years it tkes to grow math average by 1 Standard Deviation Annualized PISA math growths till PISA 2015 Years it take to have the national math average growth by 1 Standard Deviation (PISA ) of the Eastern Asian countries Years it take to have the national math average growth by 1 Standard Deviation (PISA ) Average Annual Math score change (as % of 1 Standard Deviation or PISA 100 ponts) Macao (China) Indonesia Singapore Japan Hong Kong (China) Thailand Chinese Taipei OECD average-30 Korea Viet Nam -6.0 PISA countries for math (for the average math growth trends )

28 Years it tkes to grow math average by 1 Standard Deviation Annualized PISA math growths till PISA 2015 Years it take to have the national math average growth by 0.5 Standard Deviation (PISA ) of the Eastern Asian countries Years it take to have the national math average growth by 1 Standard Deviation (PISA ) Average Annual Math score change (as % of 1 Standard Deviation or PISA 100 ponts) Macao (China) Indonesia Singapore Japan Hong Kong (China) Thailand Chinese Taipei OECD average-30 Korea Viet Nam -6.0 PISA countries for math (for the average math growth trends )

29 PISA Math scores & percentile distributions 750 South Korea's PISA math score trajectories and the math poverty distributions PISA: Distribution of Mathematics Scores: 95th Percentile Score [LO.PISA.MAT.P95] The red arrow is the math chasm between the top math countries and poorest math countries in the entire PISA and TIMSS tests PISA math years PISA: Distribution of Mathematics Scores: 75th Percentile Score [LO.PISA.MAT.P75] PISA: Mean performance on the mathematics scale [LO.PISA.MAT] PISA: Distribution of Mathematics Scores: 25th Percentile Score [LO.PISA.MAT.P25] PISA: Distribution of Mathematics Scores: 5th Percentile Score [LO.PISA.MAT.P05]

30 PISA math scores PISA math scores SINGAPORE: PISA math trajectories: Math poverty levels & percentile distributions (entire history) 750 HONG KONG: PISA math trajectories: Math poverty levels & percentile distributions (entire history) math 95 percentile 700 math 95 percentile math 75 percentile 600 math 75 percentile Average PISA math scores over time Average PISA math scores over time math 25 percentile math 25 percentile 450 math 5 percentile math 5 percentile Years (15 years of PISA) Years (15 years of PISA)

31 How much can the dominance of math average over that of reading average can impact the GDP per capita as time goes on?

32 reading score PISA 2015 reading score PISA 2015 science score PISA Math vs. Science. pisa 2015 y = x R² = Science vs. Reading. pisa y = x R² = Math vs. Reading. pisa y = x R² = math scores PISA Science scores PISA math test PISA 2015 Source: OECD, PISA 2015 Database, Tables I.2.4a, I.2.6, I.2.7, I.4.4a and I.5.4a.

33 PISA 2015 average of Science & Reading 600 Correlations between PISA 2015 Math vs. the average of Reading & Science y = x R² = Chile Uruguay Trinidad and Tobago Peru Brazil Mexico Costa Rica Colombia Finland New Zealand Australia United Kingdom Portugal United States Spain CABA (Argentina) Canada Singapore Japan Hong Kong (China) Macao (China) Chinese Taipei Korea B-S-J-G (China) 400 2) All developed Englihs speaking countries and most of the Latin American countries have the stronger reading scores than math scores by large margins. 350 Dominican Republic 0.5 STDEV PISA 2015 Math score

34 PISA 2015 average of Science & Reading Correlations and dominance between PISA 2015 Math vs. Reading in Americas vs. Eastern Asia 600 Finland Canada y = x R² = New Zealand 500 Uruguay Trinidad and Tobago Chile Australia United Kingdom Portugal United States Spain CABA (Argentina) Singapore Hong Kong (China) Korea Japan Macao (China) Chinese Taipei B-S-J-G (China) 450 Mexico Costa Rica Colombia Peru Brazil 400 Dominican Republic STDEV PISA 2015 Math score Source: OECD, PISA 2015 Database, Tables I.2.4a, I.2.6, I.2.7, I.4.4a and I.5.4a.

35 Math crisis: English-speaking countries

36 2) All developed Englihs speaking countries and most of the Latin American countries have the stronger reading scores than math scores by large margins.

37 In PISA (2012), the shares of math poverty (low performers) are much higher than that of Reading poverty Indonesia Peru Colombia Qatar Jordan Brazil Tunisia Argentina Albania Costa Rica Peru Qatar Kazakhstan Indonesia Argentina Malaysia Albania Colombia Brazil Jordan Tunisia Uruguay Montenegro Mexico Bulgaria Romania United Arab Serbia Chile Thailand Costa Rica Slovak Republic Israel Sweden Greece Russian Federation Luxembourg Turkey Lithuania Slovenia Iceland Hungary Italy Austria France Portugal Croatia Spain OECD average Latvia Czech Republic United Kingdom United States New Zealand Norway Belgium Denmark Germany Australia Netherlands Switzerland Liechtenstein Chinese Taipei Macao-China Finland Canada Poland Singapore Japan Ireland Viet Nam Estonia Korea Hong Kong-China Shanghai-China Montenegro Uruguay Mexico Malaysia Chile Thailand United Arab Emirates Kazakhstan Bulgaria Turkey Romania Serbia Greece Israel Croatia Hungary Slovak Republic Sweden Lithuania United States Portugal Italy Luxembourg Russian Federation Spain OECD average New Zealand France Norway United Kingdom Iceland Czech Republic Slovenia Latvia Australia Belgium Austria Germany Ireland Denmark Netherlands Poland Viet Nam Liechtenstein Canada Chinese Taipei Switzerland Finland Japan Macao-China Estonia Korea Hong Kong-China Singapore Shanghai-China % Math poverty (Low performers Below Level 1) of 40% or more: 22 countries Below level 1 Level 1 Percentage of low performers (Level 1 or below) in Mathematics Less math poverty than 20%: 22 countries / economies Source: Figure % Reading poverty of 40% or more: 14 countries Below Level 1b Level 1b Level 1a Much less Reading poverty of 20% or less: 34 countries / economies Percentage of low performers (Level 1a or below) in reading

38 In the following regressions, you have to be aware that the typical correlation or growth coefficients of GDP per capita vs. the Mean school years is about R^2 ~ For instance, according to Hanushek & Woessmann s regressions about the linear correlation between these 2 factors without including cognitive skills at all, the growth coefficient is about 0.35 and the R^2 ~ 0.25 (meaning that the mean school year alone can explain only about 25% of the GDP per capita grwoths (at least based on their regression based on 50 countries between ). Keeping this in mind that the PISA math reading score difference against the GDP per capita based on PPP leads to about similar magnitude of R^2 when about 3-5 outliers are taken out.

39 How much can the dominance of math average over that of reading average can impact the GDP per capita as time goes on?

40 Math overall average of PISA math impacts on GDP per capita $132,860 PISA MATH READING S OVERALL SCORE ( ) VS. LOG OF GDP PER CAPITA, PPP (CURRENT INTERNATIONAL $) 2015 AFTER REMOVING 6 OUTLIER COUNTRIES (OUT OF THE 77 TOTAL COUNTRIES) QAT LUX $132,860 PISA math - Reading overall scores ( ) vs. log of GDP per capita, PPP (current international $) 2015 after removing 6 outliers (out of the 77 total countries) y = 15832e x R² = $49,208 y = 26466e x R² = $49,208 y = 26466e x R² = $18,225 LBN AZE $18,225 y = 35261e x R² = $6,750 $6,750 KGZ $2, $2,

41 After eliminating typically 3, 4, or 6 outliers out of about participating nations over the last 12 years ( PISA math and reading)

42 . Log of GDP per capita, PPP (current international $) in 2015 Time lag in 0 years No significant changes PISA 2015 difference of Math - Reading vs. Log of GDP per capita, PPP (current international $) in 2015 after excluding 5 outliers $78,732 $29,160 y = 27658e x R² = $10,800 $4, Math - Reading score difference

43 log of GDP per capita, PPP (current international $) in 2015 Time lag in 3 years PISA 2012's difference betwen math - readingscores vs. log of GDP per capita, PPP (current international $) in 2015 after 4 outliers exludded out of the 59 countries Qatar Luxembourg $98,415 y = 31066e x R² = $36,450 Kazakhstan $13,500 Vietnam $5, PISA 2012's difference betwen math - reading scores

44 log of GDP per capita based on PPP 2015 Time lag in 6 years PISA 2009's difference between math - reading scores vs. log of GDP per capita, PPP (current international $) in 2015, after removing 4 outliers $148,803 $55,112 y = 28685e x R² = $20,412 $7,560 $2, PISA 2009's difference between math - reading scores

45 GDP per capita based on PPP in 2015 GDP per capita based on PPP in 2015 Time lag in 9 years PISA 2006 difference between the math - Reading vs. GDP per capita, PPP (current international $) in 2015 after removing 4 outliers $149,591 PISA 2006 difference between the math - Reading vs. GDP per capita, PPP (current international $) in 2015 after removing 3 outliers $148,803 Qatar y = 28324e x R² = $55,404 $55,112 y = 28324e x R² = $20,412 Serbia Azerbaijan $20,520 $7,560 Kyrgyz Republic $7, PISA 2006 Math - Reading score s $2, PISA 2006 Math - Reading score difference

46 LOG OF GDP PER CAPITA, PPP Time lag in 12 years with the district GDP per capita without 3 outliers in 2015 PISA 2003'S DIFFERENCE BETWEEN MATH - READING VS. LOG OF GDP PER CAPITA, PPP (CURRENT INTERNATIONAL $) IN 2015, EXCLUDING 3 OUTLIERS Macao SAR, China Luxembourg Liechtenstein y = 32032e x R² = $58,320 $21,600 Brazil United States Australia United Kingdom Greece Norway Switzerland Hong Kong SAR, China Ireland Austria Netherlands Sweden Canada Germany Denmark Belgium Iceland Finland France New Zealand Italy Japan Spain Korea, Rep. Czech Republic Portugal Uruguay Turkey Mexico Poland Latvia Thailand Hungary Serbia Slovak Republic Russian Federation Tunisia Indonesia $8, PISA'S MATH - READING SCORE DIFFERENCE IN PISA 2003

47 Key conclusions

48 Solutions: USL (Unified Super Learning), starting with MMU1 (Mini Mini USL1 as a series of pilot studies to convince all to end the math poverty rapidy)

49 MMU1 (Mini Mini USL1, Sep-Oct 2016) USL original pilot studies (Oct 2013, Jan-Feb 2014) By Dongchan Lee Far beyond the efficiencies of the best math apps, the best math average nations The best way to end the global math poverty in 2-5 years The best way to end the global poverty in years, not just extreme poverty in years

50 % of correct answers in the exams % of score gains per x days: Learning speed comparisons: the typical USL pilot study results (10-50x faster than usual) vs. the typical school math gains 30% -->61% in 1.5 month 30%-->71% in 1.5 month 30%-->80% in 1.5 month 35% -->60% in 1 month 35%-->70% in 1 month 34-->80% in 1 month 40%-->60% 40%-->70% USL slower USL average USL faster 100% 90% 1 class minutes USL 80% A A A Evidences 70% C C C 60% F F F 50% 40% 30% 20% months of the regular math classes 10% 0% Days needed for the regular school math tests

51 Rule of thumb for MMU1: very rapid rise of the worst math students to the best half students (Pilot studies summary) 25% 25% The Best 50% (with the school teachers) MMU1 (Mini Mini USL1) from 2 good private schools with Dongchan Lee USL 1.5: basic advances MMU1 in El Alba (a good private school in Momostenango) for the grades 3, 4, and 5 USL 1.0 ITEC of UVG (the best school of the state of Solola, Guatemala) for the grade 1 Still using only ~ 4-8% of the original original USL capacity of 2014 USL 0.5: the best scenario without USL 25% 25% The Worst 50% with Dongchan Lee ~ years needed normally, nationally The rise of the school average: ~ 1.35 STDEV: Guatemala F Average of the CA o NY of the USA. Grade 3, 5 Grade 4 ~ years needed normally, nationally Test 1 Test 2 The rise of the school average: ~ 1 STDEV: Guatemala F the average of the highest average 4 Latin American countries ~ years needed normally, nationally The rise of the school average: ~ 0.5 STDEV: Colombia, Argentina, Brazil, Ecuador D the average of the highest average 4 Latin American countries

52 Ending Math Poverty MMU1 (Mini Mini USL1) proposals to Americas 2017 Ending Poverty With Dongchan Lee Sustainable Growths 25% Very quickly The Best math 50% (with the school teachers) Evidence-based only 25% as appetizers Massive Math boosts by MMU1 Public-Private Partnership 25% 25% ~ 1.35 STDEV advances The Worst math 50% with Dongchan Lee 100% internet-based (~100% tablet or computer-based) Charged from Solar Panels Purified Water from Solar panels With the national & state governments & MOEs of the English, Spanish, Portuguesespeaking countries first. with the UN

53 Invite Dongchan Lee To run MMU1 Pilot studies In your country With your Ministries Of Edu Support him. Fund him. Help him end the math poverty. Give him media publicities. Bring him philanthropists. To end the poverty. To make USL go global. Bring him Investors. Faster than anyone else can.. In the entire world. Make him demonstrate to the UNESCO To the General Assembly of the UN. Help him create his own 1 NGO per country Help him accelerate the SDGs of the UN far faster than currently possible.

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