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Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
Mentoring through analytics   dr. s jayaprakash
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Mentoring through analytics dr. s jayaprakash

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  • 1. Mentoring through analytics Dr. S. Jayaprakash Co-founder &VP nanoBI analytics nanobi data and analytics private limited
  • 2. Let us start with some examples• Jobless MBA students• Engineers with skill shortages• Girls disallowed to pursue good opportunities• Some schools and Parents remain disintegrated.Any other incidents? COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 3. Education in Contemporary World• Creates “Bent of Mind”• Enables high competitions• Creates Crowded Market Place• Assimilate Knowledge and Value Creation• Leverages Technology• Innovative Teaching Methods• Gap between aspirations and opportunities!Have we leveraged technology completely? COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 4. Influencing Factors• Place of Education • Attendance and Culture. • Preparedness• Infrastructure and • DNA (Genes) reputation of the school • Distortion • Peer Pressure &• Education level of Competitive Environment the parents • Aspiration of the students• Extracurricular activities • Ability • Mentoring and Long Term• Affluence level of the relationship parents/family • Trusted and Wise COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 5. Challenges• Quantification?• Complexity?• Cost?• Pervasiveness?• Public Data? COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 6. Stakeholder framework COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 7. Strategic Questions for Parents• What are the strengths of their son/daughter?• How does the trend of the marks scored by the student look like?• What is the likelihood of the marks that can be scored in the same trend?• What is the attendance trend of the student?• What is his participation level in the extra- curricular activities? COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 8. Strategic Questions for Teachers• How does the profile of the top rankers look like in terms of parental qualification, income, and aspiration at the state level? How does it match with the top rankers of the school?• What are the key areas of weakness for the top rankers that can face the hurdle?• How many clusters of students can be formed on the basis of Marks Vs. Extra Curricular activities?• How does the attendance of the students look like? COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 9. Parental Income and Marks COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 10. Clustering Academic marks wise COPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 11. Time SeriesCOPYRIGHT (C) 2012 nanobi data and analytics private limited
  • 12. Monthly Attendance
  • 13. Monthly Attendance-School level
  • 14. Mentor Performance – Location wise
  • 15. Mentor Performance –Grade wise on 50 points scale
  • 16. Classwise performance – 50 point scale
  • 17. Thank youjayaprakash@nanobianalytics.com+91 988 000 6505 nanobi data and analytics private limited Simple Agile Pervasive

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