Insights from data in Education

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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.
  • Insights from data in Education

    1. 1. INSIGHTS FROM DATA
    2. 2. DETECT ANOMALIES
    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 an external force is applied to the dataset that creates a systematic bias.”
    4. 4. SEE THE REAL IMPACT OF POLICY
    5. 5. ENGLISH 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
    6. 6. 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 SOCIAL SCIENCE
    7. 7. 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 LANGUAGE
    8. 8. 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 SCIENCE
    9. 9. 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 MATHEMATICS
    10. 10. 1 5.5% 2 4.3% 3 2.3% 4 0.9% 5 0.3% 6 0.1% MATHEMATICS 3.08% COMMERCE 0.80% ACCOUNTANCY 0.33% PHYSICS 0.26% ECONOMICS 0.21% HISTORY 0.19% How many subjects do students fail in?What contributes to single failures? PHYSICS MATHEMATICS 0.79% PHYSICS CHEMISTRY 0.77% CHEMISTRY MATHEMATICS 0.55% COMMERCE ACCOUNTANCY 0.29% ENGLISH COMMERCE 0.17% BIOLOGY MATHEMATICS 0.14% Two-subject failures
    11. 11. WHAT DETERMINES PERFORMANCE?
    12. 12. Subject Girs higher by Girls Boys Physics 0 119 119 Chemistry 1 123 122 English 4 130 126 Computers 6 137 131 Biology 6 129 123 Mathematics 11 123 112 Language 11 152 141 Accounting 12 138 126 Commerce 13 127 114 Economics 16 142 126 PERFORMANCE: GIRLS VS BOYS
    13. 13. Based on the results of the 20 lakh students taking the Class XII exams at Tamil Nadu over the last 3 years, it appears that the month you were born in can make a difference of as much as 120 marks out of 1,200. June borns score the lowest The marks shoot up for Aug borns … and peaks for Sep-borns 120 marks out of 1200 explainable by month of birth An identical pattern was observed in 2009 and 2010… … and across districts, gender, subjects, and class X & XII. “It’s simply that in Canada the eligibility cutoff for age-class hockey is January 1. A boy who turns ten on January 2, then, could be playing alongside someone who doesn’t turn ten until the end of the year—and at that age, in preadolescence, a twelve-month gap in age represents an enormous difference in physical maturity.” -- Malcolm Gladwell, Outliers
    14. 14. BIG DATA REQUIRES RICHER VISUALS
    15. 15. MONITORING EFFECTIVELY
    16. 16. MONITORING EFFECTIVELY
    17. 17. Jain Harini Shweta Sneha Pooja Ashwin Shah Deepti Sanjana Varshini Ezhumalai Venkatesan Silambarasan Pandiyan Kumaresan Manikandan Thirupathi Agarwal Kumar Priya
    18. 18. FIND HIDDEN CORRELATIONS
    19. 19. COMPARING PERFORMANCE
    20. 20. EMBRACE AND LEARN FROM DATA USE IT TO DRIVE YOUR DECISIONS

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