The increase in enrollment in education and higher education institutions, the increase in the use of the Internet as well as the emergence of technology in educational systems have led to the aggregation of large amounts of student data at educational institutions (schools, colleges, and universities), which makes it vital to use data mining methods to improve the educational settings. Although educational institutions collect an enormous amount of student data, this data is utilized to produce basic insights and is not used for decisions to improve the educational settings. To get essential benefits from the data, powerful techniques are required to extract the useful knowledge which is valuable and significant for the decision and policy makers.

1. 1
1. https://www.researchgate.net/publication/314942684_Applicability_of_Educatio
nal_Data_Mining_in_Afghanistan_Opportunities_and_Challenges
2. http://www.educationaldatamining.org/EDM2016/proceedings/paper_1.pdf
For more information read the following papers:

2. Applicability of Educational Data Mining
in Afghanistan: Opportunities and Challenges
Abdul Rahman Sherzad
Lecturer at Computer Science Faculty of Herat University, Afghanistan
Ph.D. Student at Technische Universität Berlin, Germany
October 11, 2016 (Technische Universität Berlin)

3. The Key Terms
Data Mining
Educational Data Mining (EDM)
EDM Applications
EDM in Afghanistan
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4. Data Mining
Data
Mining
Machine
Learning
Artificial
Intelligence
Database &
Data
Warehouse
Statistics
4

5. Data Mining
Data
Mining
Explain the
past
Exploration
Techniques
Predict the
future
Modeling
Techniques
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6. Educational Data Mining
It is an emerging discipline to develop methods for
exploring the data that come from educational
settings to better understand students and the
settings which they learn in.
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7. EMD Application
EDM is useful in many different areas including
• Providing feedback for supporting instructors,
• Detecting undesirable student behaviors,
• identifying at-risk students,
• increasing graduation rates,
• effectively evaluating institutional performance,
• recommending the right courses for the students,
• and helping students in Major selection.
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8. 1. Does Afghanistan
Education Systems
need EMD?
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.
2. What are the EDM
Applicability in
Afghanistan
Education Systems?
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9. Transaction of schools data in Afghanistan (scenario)
General education students facts
# of students years of study # of subjects (average) exams (mid and final) Total Records
~ 10,000,000 9 13 2 2,340,000,000
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NOTE: The above-mentioned calculations indicate (only) marks data. Of course there are
different entities exist in the university and school systems i.e. teachers, students, students
dependents, subjects, activities, classes, etc.
Public High school students facts
# of students years of study # of subjects (average) exams (mid and final) Total Records
~ 1,000,000 3 15 2 90,000,000
Public High school graduates/year facts
# of students years of study # of subjects (average) exams (mid and final) Total Records
~ 200,000 3 15 2 18,000,000

10. Storage of schools data in Afghanistan (scenario)
Primary & Secondary schools data storage (estimation)
# 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!
High schools data storage (estimation)
# of students Storage/student Total Storage (MB) Total Storage (GB) Total Storage (TB)
~ 1,000,000 2 MB 2,000,000 MB 2,000 GB 2 TB

11. The Problem
Inappropriate choice of Major
◦ students are not offered
specialized studies at high
schools,
◦ family literacy rates are low so
parents cannot help students
with choosing a career,
◦ high schools lack basic career
counseling services,
◦ and there are no academic
advisory organizations outside
educational institutions to
guide the students on this
critical career decision.
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NOTE: The data shows one-third of the students admitted into Herat
public university could not continue higher education studies!

12. EDM Application I:
Major prediction
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In Afghanistan, there are no academic advisory
organizations to guide the Kankor candidates on
choosing proper fields of study.
• Design a classification model to predict/recommend proper
Major to high school graduates prior attending the Kankor.
In Afghanistan, students are not offered specialized
studies.
• Design a learning model to automate student placement into
sciences or social sciences while specialized studies is offered at
school level.

13. EDM Application II:
Support student at
risk of attrition and
failure
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In Afghanistan, most of the students have no
knowledge of the requirements and
prerequisites of the offered fields of study,
and select randomly.
◦ The results are poor performance or high rates
of dropout in higher education.

14. Challenges prevent the applicability
of EDM in Afghanistan
• Lack of data availability and accessibility!
• Lack of experts in the domain of Data Mining and
Educational Data Mining!
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15. 15
1. https://www.researchgate.net/publication/314942684_Applicability_of_Educatio
nal_Data_Mining_in_Afghanistan_Opportunities_and_Challenges
2. http://www.educationaldatamining.org/EDM2016/proceedings/paper_1.pdf
For more information read the following papers: