A car without fuel cannot be driven; a mobile, a laptop or a PC without power cannot be used; a website without feeding won't have any visitors; likewise, an organization without data will not stand and cannot be survived. The data quickly becoming one of the most important resources for any country, company, or organizations. It is the data that enables organizations to explain the past and guess the future through data science and business intelligence tools. This presentation demonstrates how the Kankor data can be used as a resource in the context of Afghanistan, particularly, the candidates’ names that organizations in Afghanistan do not use for anything. Read the following paper for more information and examples: https://www.researchgate.net/publication/322695084_Data_is_the_Fuel_of_Organizations_Opportunities_and_Challenges_in_Afghanistan

1. Data is the Fuel of Organizations:
Chances and Challenges in Afghanistan
Abdul Rahman, Sherzad
Ph.D. Student at Technical University of Berlin
13th National IT Conference at Ministry of Higher Education, Afghanistan
December 18, 2017

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https://www.xero.com/blog/2016/02/data-fuel-of-our-future/

3. Data is P o w e r
• Data Analyses
• Knowledge Discovery in Data
• Data Mining
• Data Science
• Big Data
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4. Top Male and
Female Names
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Top 20 male and female names in Afghanistan

5. Autofill Missing
Data
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Autofill missing gender values from existing data using identical names.

6. Generate Fake
Data
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Persistent Fake Data generated to test the database performance

7. Match
High School Data
with
Kankor Data
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There is no primary and foreign key for these two datasets for
matching!

8. Name Entity
Recognition
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Name Entity Recognition identifying names of people, locations, and
organizations.

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10. Data Mining Applicability in Afghanistan
Education Systems?
■ In the context of Afghanistan,
– there are large amounts of data available for mining
purposes in the education domain,
– but the methods that the educational institutions use to
store and produce their data only enable them to achieve
basic insights.
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11. Scenario: Data Transaction in General Education
10,000,000 (students)
12 (years of study)
13 (subjects in average)
2 (exams)
2,340,000,000 (records)
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NOTE: The calculation indicates (only) marks
data. Of course there are different entities
exist in the university and school systems e.g.
teachers, students, students dependents,
subjects, activities, classes, etc.

12. Scenario: Data Storage in General Education
# of students Storage/student Total storage (MB) Total storage (GB) Total storage (TB)
~ 10,000,000 2 MB 20,000,000 MB 20,000 GB 20 TB
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NOTE: Consider how much data is generated/day (explicitly, implicitly)
when there are Learning Management Systems (LMSs) available at
schools and universities!

13. Opportunities
■ There are interesting opportunities for the application of
educational data mining in Afghanistan.
– Help policy makers in shaping the education system
– Allow an early warning systems to identify university
students who are at risk of failure or attrition.
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14. Limitations
■ Lack of data availability and accessibility in e-
formats!
■ Lack of detailed data!
■ Lack of experts in the domain of Data Mining
and Educational Data Mining!
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Thank you ☺