Educational Data Mining for Secondary and Higher Secondary Education in Bangladesh

1
Educational Data Mining for Secondary and Higher
Secondary Education in Bangladesh
Md. Shafiqul Islam
d
Introduction
Implementations
A typical implementation of our data warehouse
schema is illustrated in the figure below. A
three-dimensional data cube (3D Cube), is
generated from data warehouse, which should look
graphically like the following diagram. The data
here is grouped into individual cubes according
to Location, Group and Time. Any relevant data
cube can be generated from the data available in
the data warehouse, similarly. These cubes should
be observed to get information of educational
records.
Educational data is one of the huge resources of
big data in current world. This data record for
our country is a great source to extract
information about our educational process,
progress and also an essential source of elements
to predict about the future learning behavior.
But these utilities cannot be gained merely
looking over the raw data of education. A proper
procedure of analysis is the prerequisite to get
valuable information from these raw data, which
is known as Educational Data Mining (EDM).
Educational Data Mining refers to the
techniques, tools, and researches, designed for
automatically extracting meaning from large
repositories of data generated by or related to
people's learning activities in educational
settings.
Sylhet
Khulna
Location (Board)
Chittagong
Rajshahi
Dinajpur
Barishal
Comilla
Dhaka
Humanity
Group
Science
Business Studies
2014
2010
2005
2004
2001
2013
2009
2011
2006
2003
2012
2008
2002
2007
Objective
Time (Year)
Figure-2 A 3-D data cube representation of
SSC/HSC data according to Time, Location and
Group. The result is shown with a time domain of
2001-2014. Though Madrasha and Technical board
does not shown, the corresponding result could be
gain accordingly.
Our objective is to design and implement an
Educational Data Warehouse repository, which may
further be used to extract useful information for
Knowledge Discovery from Data for educational
data records (KDD). We have only focused over the
two public examination one is Secondary School
Certificate (SSC) and the other is Higher
Secondary School Certificate (HSC) examination,
in Bangladesh.
Expected Findings

• Although educational data both in SSC and HSC are
  extensive, these are much precise than many other
  source of big data to examine for mining. Using
  the above schema, we can perform the following
  tasks.
• Analyzing and visualization of student data
• Grouping students according to specific
  properties
• Detecting undesirable student behaviors
• Predicting student performance
• The distilled data can also be used to plot into
  curve, bar chart or into statistical regression
  analysis for human judgment.

Methodology
The first phase of the EDM process is to discover
relationships among data. This involves searching
through a repository of data from an educational
environment with the goal of finding consistent
relationships between variables. Our designed
data warehouse repository is shown below. Since
both the repositories for SSC and HSC are almost
similar, here is only the schema for SSC is shown
for simplicity.
Dimension Table
Dimension Table
Student
Student_key (pk)
Roll
Reg
birth_day
birth_month
birth_year
sex
group
Fact Table
Conclusion
Time
Session_start (pk)
session_end
passing_year
Fact_all
Student_key (fk)
Session_start (fk)
Subject_key (fk)
Institution_code(fk)
grade_point
In this thesis, we have focused on designing a
data warehouse to make the knowledge discovery
from educational data available for secondary and
higher secondary school examination data sources
more easy. Since the data is extensive and
increasing with time, challenges are associated
with implementing educational data mining. As a
developing country Bangladesh also needs to pay
attention over the hidden information exists into
educational data resources for educational
progress and success.
Dimension Table
Location
Institution_code (pk)
institution
thana
area/district
sub_area/division
centre
Dimension Table
Subject
Subject_key (pk)
subject_codes
subject_numbers
References
1. Data Mining Concepts and Techniques(Third
Edition) - by Jiawei Han, Micheline Kamber, Jian
Pei 2. http//www.educationaldatamining.org 3.
http//en.wikipedia.org/wiki/Educational_data_mini
ng 4. http//http//www.educationboard.gov.bd
Figure-1 Star Schema of SSC/HSC Data Warehouse.
Department of Computer Science and Engineering
(CSE), BUET
Department of Computer Science and Engineering
(CSE), BUET