Education: Insights from data

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  • We took the results of class 12th state board examination in Tamil Nadu and looked at the most popular names -- the top 5,000 names to be precise -- and plotted them based on their marks. The visualisation you see plotslarge boxes for the popular names. For example the big rectangle on the top left indicates people who have the name Kumar and the colour of the boxes indicate the average percentages scored by these students. The darker the blue, the higher the marks. The closer it is to white, the lower the marks. There are some fairly interesting patterns here. For example the names Jain, Shah, Agarwal and Gupta tend to score fairly high marks. These are typical north Indian names. Names like Ashwin, Shweta, Sneha, Pooja, Harini, Sanjana, Varshini, Deepti, etc they tend to score high marks as well. These are classic urban names and you’ll also notice that vast majority of them are girl’s names. Names such as Manigandan, Venkatesan, Ezhumalai, Silambarasan, Pandiyan, Kumaresan, Tirupathi, they tend to score relatively low marks. If you notice these are classic rural names and predominantly male. This is NOT an indication of marks being predicted by the names -- but rather both marks and names are a consequence of socio economic and cultural background of students.
  • Education: Insights from data

    1. 1. INSIGHTS FROM DATA
    2. 2. DETECTANOMALIES
    3. 3. Richard Quinn Strategic Management, UCF “The exam was running at a grade and a half higher than it had ever run before... You don’t see that kind of grade improvement by chance.” Summer 2010 Mid-term Fall 2010 Mid-term“A bimodal distribution exists when anexternal force is applied to the datasetthat creates a systematic bias.”
    4. 4. SEE THE REALIMPACT OFPOLICY
    5. 5. ENGLISH40,00035,00030,00025,00020,00015,00010,000 5,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    6. 6. SOCIAL SCIENCE40,00035,00030,00025,00020,00015,00010,000 5,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    7. 7. LANGUAGE40,00035,00030,00025,00020,00015,00010,000 5,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    8. 8. SCIENCE40,00035,00030,00025,00020,00015,00010,000 5,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    9. 9. MATHEMATICS40,00035,00030,00025,00020,00015,00010,000 5,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    10. 10. How many subjects do students fail in? to single failures? What contributes1 5.5% MATHEMATICS 3.08%2 4.3% COMMERCE 0.80%3 2.3% ACCOUNTANCY 0.33%4 0.9% PHYSICS 0.26%5 0.3% ECONOMICS 0.21%6 0.1% HISTORY 0.19% Two-subject failures PH Y SIC S M AT H E M AT IC S 0.79% PH Y SIC S C H E M IST R Y 0.77% C H E M IST R Y M AT H E M AT IC S 0.55% C O M M E R C E AC C O U N T AN C Y 0.29% E N GL ISH C O M M E R C E 0.17% B IO L O GY M AT H E M AT IC S 0.14%
    11. 11. WHATDETERMINESPERFORMANCE?
    12. 12. PERFORMANCE: GIRLS VS BOYS S u b jec t Girs h ig h er b y Girls Boys Physic s 0 119 119 C he m istry 1 123 122 E nglish 4 130 126 C o m pute rs 6 137 131 B io lo gy 6 129 123 M a the m a tic s 11 123 112 L a ngua ge 11 152 141 Ac c o unting 12 138 126 C o m m e rc e 13 127 114 E c o no m ic s 16 142 126
    13. 13. … and peaks forBased on the results of the 20 lakh Sep-bornsstudents taking the Class XII exams The marks shootat Tamil Nadu over the last 3 up for Aug bornsyears, it appears that the month youwere born in can make a differenceof as much as 120 marks out of 120 marks out of 1200 explainable1,200. by month of birth June borns score the lowest An identical pattern was observed in 2009 and 2010…“It’s simply that in Canada the eligibilitycutoff for age-class hockey is January 1. Aboy who turns ten on January2, then, could be playing alongsidesomeone who doesn’t turn ten until theend of the year—and at that age, inpreadolescence, a twelve-month gap inage represents an enormous difference inphysical maturity.” … and across districts, gender, subjects, and class X & XII. -- Malcolm Gladwell, Outliers
    14. 14. BIG DATAREQUIRESRICHER VISUALS
    15. 15. MONITORINGEFFECTIVELY
    16. 16. MONITORINGEFFECTIVELY
    17. 17. Kumaresan Kumar Silambarasan Venkatesan Pandiyan Jain Priya Ezhumalai Shweta Ashwin Sneha Pooja Harini SanjanaManikandan Shah Varshini Deepti Thirupathi Agarwal
    18. 18. FIND HIDDENCORRELATIONS
    19. 19. COMPARINGPERFORMANCE
    20. 20. EMBRACE AND LEARN FROM DATAUSE IT TO DRIVE YOUR DECISIONS

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