Description
The growth of technological development in information technology has caused challenges for many institutions.
CONCEPTUAL FRAMEWORK OF BUSINESS INTELLIGENCE ANALYSIS IN
ACADEMIC ENVIRONMENT USING BIRT
Julaily Aida Jusoh, Norhakimah Endot, Nazirah Abd. Hamid, Raja Hasyifah Raja Bongsu, Roslinda
Muda
Faculty of Informatics,
Universiti Sultan Zainal Abidin,
21300 Kuala Terengganu,
Terengganu
{julaily, nazirah, rajahasyifah, [email protected]}
[email protected]
ABSTRACT
The growth of technological development in
information technology has caused challenges for many
institutions. The enhancements of capabilities to gather
and store data in multiple source system make it more
difficult for the management in academic institutions to
analyze the important data. The objective of this paper
is to analyze students’ academic achievement based on
accommodation factor, in other words, to analyze how
accommodation can affects the students’ academic
achievement. This paper discusses the conceptual
framework for business intelligence analysis in
academic environment using BIRT tool. BIRT tool will
generate a report which illustrate and analyze the
comparison of students’ academic achievement
between the students that stay either in-campus and
out-campus.
KEYWORDS
Student Relationship Management (SRM), Data
Warehouse, Business Intelligence and Reporting Tools
(BIRT)
1 INTRODUCTION
Business Intelligence (BI) comprises of skills,
processes, technologies, applications and practices
that are functioning as leverage to institutions from
internal and external assets and to support and
improve decision making [7]. An effective BI
solution can be used to integrate data sources that
frequently used to complement marketing
initiatives and compliance analysis reporting. BI
applications that apply the concept of aggregated
information are Customer Relationship
Management (CRM) and Student Relationship
Management (SRM).
CRM is a strategy for managing company
interactions with customers, clients and sales
prospects. It involves of using technology to
organize, automate, and synchronize business
processes. The goals of CRM are to find, attract,
and win new clients and reduce the costs of
marketing and client service in different services
such as marketing, customer service, and technical
support. Meanwhile, SRM is a concept that has
been derived from CRM and can be applied to
academic institutions. The goals of SRM are to
improve the student experience, reduce drop-out
rates, and improve institutions efficiency. The
SRM involves automating and synchronizing a
number of different processes such as academic
advising, counseling, and registration. In SRM, the
components of data warehouses have not been
fully exploited by users. Data warehouse (DW) can
be defined as a relational database that is designed
for query and analysis. In this research, DW stores
information about assessment, personal and
allocation. It is usually contains historical data that
has been derived from various sources such as
Oracle, Informix, Access and MySQL database.
In addition of DW is functioning as a relational
database, DW contains environment that includes
extraction, transformation, and loading (ETL) and
Online Analytical Processing (OLAP). Designing
and maintaining ETL steps are often considered as
one of the most difficult and resource-intensive
portions of DW project. ETL meant extract,
transform and load data across various data
sources to create an accurate and quality
information. ETL steps are responsible for the
operations to take place in the DW architecture.
There are three steps involve in ETL as shown in
Table 1. Online Analytical Processing (OLAP) can
be used to analyze data from multiple databases.
390
OLAP is an approach to answer multi-dimensional
queries that encompasses relational reporting and
data mining. OLAP tools enable users to analyze
multi-dimensional data from different sources. The
databases that have been configured for OLAP can
be used for complex analytical and ad-hoc queries
with a rapid execution time.
Table 1: Process by ETL tools
STEP ACTIVITIES
Extracting The data are extracted from
the source data stores that
can be On-Line Analysis
Processing (OLAP), files,
web pages, various kinds of
documents (spreadsheets
and text documents)
Transforming Transported to the target
system or an intermediate
system for further
processing
Loading The data are loaded to the
central data warehouse
(DW)
In this research, we concentrate more on producing
reports by using Business Intelligence and
Reporting Tools (BIRT). BIRT can be recognized
as a part of BI. BIRT tool can be used to produce
reports after data has gone through ETL processes.
BIRT reports can be divided into four main parts:
a) Data: involves databases, web services and java
object that can supply data to produce report.
BIRT provide JDBC, XML, Web Services and
Flat File that can support the code from
different source.
b) Data transform: data is sorted, summarized,
filtered and grouped.
c) Business logic: structure that can be used to
create the reports.
d) Presentation: a range of options to present the
report. It involves tables, charts, texts and
others.
This paper is organized as follows. Section 2
describes related research on what and how far
does business intelligence can assist in academic
environment. Section 3 explains business
intelligence tool called BIRT and conceptual
framework of business intelligence analysis in
academic environment is described in Section 4.
We present experimental testing in Section 5 and
lastly we conclude this work in Section 6.
2 RELATED WORKS
Recently, many researches have been conducted
on fundamental knowledge of the realities in
education. Many institutions have problems in
academic results which related to the students’
activities. In order to overcome these problems,
some strategies have been proposed to improve
education systems.
A web based software using data mining and
Quality Function Deployment (QFD) was
proposed by [2] to assist higher institutions in
processes such as assessing, predicting and
managing issues that related to the student success.
[2] highlighted that the biggest challenge in higher
education was to predict the suitable academic
path for the students. This situation happened
because the institutions had lack of information to
guide the students in selecting a suitable program
and course for them. Consequently, many students
who did not interested in the enrolled courses
could not concentrate and fail to complete their
study.
[1] proposed CRM in education to manage
relationship across student lifecycle. CRM
strategies had been suggested to manage
relationship more effectively between company
and their clients, in this case, between an
institution and their students. CRM strategies
offered solutions with more powerful, different
features and functionality, and also could help to
determine quality of the student across student
lifecycle [1].
SRM were introduced in 2008 to support the BI
system in higher education [3]. [3] used DW and
data mining to analyse the students’ data. System
architecture of SRM was developed to identify
students’ activities while strategies were built to
solve problems among the students. The concepts,
practices and strategies that support education in
acquisition of knowledge to make decision making
process also discussed in [3]. Table 2 describes the
391
processes and activities that involve in SRM
architecture.
Table 2: Phase and activities in SRM architecture
PHASE ACTIVITIES
Concept Discuss process how student
acquired the knowledge
Practice Discuss about the activities
that guarantee a personal
contact with student an
effective, adequate and
closely monitoring of
academic performance.
Strategy Discuss about vision and
mission of the institution.
BI concepts could be adopted to support the
teaching and learning process and also to manage
student and lecturer relationship in higher
institutions environment. The main objectives of
[3] were to prepare the technological tools that
could be used to support the education process and
also to support the decision making process. [3, 4]
mentioned that one of the contribution of student’s
success factor was by closely monitoring the
student’s participation in academic activities.
However this factor did not occur in many higher
institutions. Students’ academic failure often
occurred during the first year because the
institutions unable to monitor their academic
activities [4]. For that reason, a conceptual
framework and technological infrastructure were
proposed by [4] and then integrated them with the
SRM system.
A case study which involved 70 students was done
by [4] to show that the SRM system was suitable
to be used in the learning process. In data
gathering process, student’s information was
collected from presential component, e-learning
system and assessment. The presential components
comprised of theoretical, practical and tutorial
orientation. Several information such as
curriculum contents, exercises and project
guidelines were included in the e-learning process.
The assessment involved were the normal
assessment period and exam assessment period.
The authors used OLAP, data mining, DW and
ETL tools to describe the case study and to
examine relevant data [4]. OLAP was used to
analyze data from different sources while data
mining was used to identify the model and pattern
of the data. Besides that, OLAP cubes were used to
analyze the student’s results verified the teaching
learning process and assessment. The ETL process
extracted data from the database. The data was
cleaned and transformed to the targeted database
or data warehouse.
3 BIRT TOOLS
Business Intelligence and Reporting Tools (BIRT)
has grown successfully since 2004. The successful
of this growing technology is achieved when the
user use the BIRT technology to produce reports in
business environment. The BIRT reports allow
user to recognize trends in data and present the
report in interactive ways and make the reports
look professional [6].
BIRT can be categorized into two categories. The
first category is report development environment
that can be used to design and develop the reports
while report development environment which is
similar to Macromedia Dreamweaver is
functioning to design web pages. This category is
connected with the Java Database Connectivity
(JDBC). JDBC is used to connect with the MySQL
database to produce reports. The Structured Query
Language (SQL) in MySQL consist of different
statement such as select, delete, update and adding
statement. The second category is Java APIs. It
provides Java Server Report for integrating
between BIRT Report Engine and Apache Tomcat
[5].
In this research, the graph generated by BIRT will
be used to produce the students’ report. This report
can be used to analyze the correlation of academic
results between the students that stay in the hostel
provided by the university (in-campus) and those
who are renting outside (out-campus
accommodations). In this study, we anticipate that
accommodation is one of the factors that might
influence students’ academic results.
392
4 CONCEPTUAL FRAMEWORK
The purpose of this framework is to identify flow
of activities involved in generating related reports.
Figure 1 shows the conceptual framework of the
research.
Figure1: Conceptual Framework
The framework can be divided into three phases
consist of input, processing and output. The
descriptions of the framework are as follow:
Phase 1: Input
In this research, the corresponding users are
lecturers, hostel supervisors and students. In
Academic System, we consider 3 existing sub-
systems that consist of assessment, allocation and
personal system. The assessment system contains
information regarding students’ assessments such
as quizzes, assignments, mid-term tests and final
examinations. Lecturers are responsible to key-in
students' marks in the assessment system. Next is
the allocation system that serves the purpose to
store students’ accommodation data (in-campus or
out-campus). This system will be managed by the
supervisors who are in-charge of students’
accommodation. In addition, personal system is
used to record students’ personal information such
as their addresses and contact numbers.
Figure 2: Input Phase
Since the existing systems were usually developed
using different database formats (eg: Oracle,
MySQL and Microsoft Access), there is the need
to process the data from different databases into a
single data format prior to further processing. This
is where the ETL process comes into play where
various data formats can be extracted, transformed
and loaded into a unified data format. The three
steps for ETL are:
Step 1: Extraction
In this process, data will be extracted from the
various sources of the sub-systems and converted
into a single database format which is appropriate
for the next process.
Step 2: Transformation
Next, a series of rules or functions will be applied
to the extracted data from the source sub-systems
for loading into the end target database format.
This step allows the data from multiple database
sources to be joined, filtered and sorted using
specific attributes. In this step, certain fields or
columns from the source sub-systems with
different names but similar attributes (eg : both
Matric_No and No_Mat represent the same
attribute, ie. students’ matric numbers) will be
selected and reformat before being loaded into the
targeted database format. This process allows data
cleansing by removing duplicates and
consequently enforce consistency by using a single
name of the same attribute.
393
In addition, only some attributes deemed necessary
will be selected from the data sources depending
on the type of analysis that is going to be
performed. For example, the examination table
from assessment system consists of Name
(NAME), Matric Number (MATRIC_NO), Course
(COURSE), Semester (SEM), Assessment_marks
(ASSESS_MARK) and Cumulative Grade Point
Average (CGPA). Only important data such as
MATRIC_NO, SEM and CGPA will be selected
for the analysis. Other data in the table such as
NAME, COURSE and ASSESS_MARK will be
ignored.
Step 3: Data Loading
In this research, this process involves writing the
transformed data into MySQL database format
(end target database). Any constraints and indexes
are disabled prior to the start of the process and re-
enabled after the loading process has completed.
Phase 2: Processing
Figure 3: Processing Phase
After the ETL process, the data will be stored in a
data warehouse where it can be used for the
analysis purposes in order to generate appropriate
reports. The data warehouse will be managed by
the technical authority.
Phase 3: Output
The Business Intelligence and Reporting Tool
(BIRT) will be used to represent the necessary
reports. BIRT plays a significant role in the
process of statistical analysis and visualization to
produce the reports by collecting and analyzing
data from the database and displaying students’
academic achievement using graphs. The different
graphs obtained for in-campus and out-campus
students can be compared in order to determine
whether the factor of accommodation influences
students’ academic achievement. Users of the
system (lecturers, deans and academic
management) can view the graphs analysis which
in turn can be used to support the institution in
developing a strategy to improve students’
achievement.
Figure 4: Output Phase
5 EXPERIMENTAL TESTING
To prove the proposed framework, we use dummy
data for analysis simulation from various data
source format. We assume that the data is collected
from two sub-systems consist of assessment and
allocation system. In this research, ETL process is
use to extract data from those databases and then
clean the data and make it uniform to be
transferred into one central repository which is
MySQL database. Table 3 and 4 illustrates the
example of data extracted from academic system.
Table 5 table display the Matric Number
(MATRIC_NO), Semester (SEM) and Cumulative
Grade Point Average (CGPA) for students of
Bachelor of Computer Science (BCS) from
semester II 2010/2011.
Table 3: Example of data extracted from the assessment
system.
394
Table 4: Example of data extracted from the allocation
system.
Table 4 comprises of Matric Number
(MATRIC_NO), Accomodation (ACCM) and
Block Name (ADDRESS). The data contains
information of students that stay either inside or
outside of campus in the same semester.
Figure 1 summarize the entire of analysis process
such as where and how the data are obtained,
extract, transform and load to make the data
accurate in generating reports or graph. This
experimental testing highlight four important data
to be extracted and analyzed such as matric
number (MATRIC_NO), semester (SEM),
cumulative grade point average (CGPA) and
accomodation (ACCM). Those data will be
combined to determine and analyse the correlation
of students’ academic result and accomodation. In
BIRT, the SQL statement was created and used in
producing the graph. Below is the example of the
SQL statement.
Select M.MATRIC_NO, M.CGPA,
C.ACCOMODATION FROM
Examination as M, Allocation as C
WHEREM.MATRIC_NO= C.MATRIC_NO
Figure 5: Analysis Graph of Students’ Academic Result
Figure 5 depicts the example of the correlation
analysis between students’ academic result and
accommodation for semester II 2010/2011. This
figure shows the expected graph of students’
results generated by the BIRT tool. From the
graph, we can conclude that most in-campus
students achieved better results compared to out-
campus students. It is apparent that the
deployment of the tool provides significant
information regarding students’ academic
achievement which can be used further in the
strategizing how to improve the academic result
among out-campus students. We anticipate that a
better analysis will be achieved if real life data is
used in the analysis.
6 CONCLUSIONS AND FUTURE WORK
This research proposed a conceptual framework
employed in an academic environment utilizing BIRT
tool that assists the process of generating reports for
students’ academic achievement. The significance of
this research lies in the process on how to analyze any
factors that could influence students’ academic
achievement. In future, we are going to implement
this framework using real cases in academic
environment. Not only the framework can be used to
help to predict the category of subjects (eg:
programming, database, mathematics) that students
can perform well, it can also be used to find the
correlation in students’ academic achievement for
every semester. Eventually, we are hoping to be able
to use the proposed framework to predict and identify
any patterns associated with students’ academic
achievement.
7 REFERENCES
1. Engelbert, N., Best practice with CRM in
higher education:Managing relationship
across the student lifecycle. 2007: p. 1-13.
2. Sahay, A., Mehta, K. Assisting Higher
Education In Assessing, Predicting, and
Managing Issues Related To Student
Success:A Web Based Software Using Data
Mining And Quality Function Deployment.
Academic and Business Research Institute
Conference, Las Vegas, 2010.
395
3. Maria Beatriz Piedade, M.Y.S., Student
Relationship Management:Concept, Practice
and Technological Support. In Proceedings
of the IEMC-Europe 2008-IEEE International
Engineering Management Conference
(Estoril, Portugal, June 28-30). ISBN 978-1-
4244-2289-0.
4. Maria Beatriz Piedade, M.Y.S., Business
Intelligence Supporting The Teaching
Learning Process. Proceedings of the 5th
WSEAS International Symposium on Data
Mining and Information Processing
(Budapest, Hungary, September 3-5). WSEAS
Press, pages 292-297, ISBN:978-960-474-
113-7.
5. Ward, J., Practical Data Analysis And
Reporting With BIRT. 2008.
6. Sheldon Lee-Loy, J.F., Advance Charting In
Birt. 2008: p. 1-43.
7. Dave Sharman., An Introduction to Business
Intelligence for Higher Education. Collegiate
Project Srvices, 2010.
8. Maria Beatriz Piedade, M.Y.S., Promoting
Students Success with a Business Intelligence
System. Proceedings of CSEDU - 2nd
International Conference on Computer
Supported Education (Valencia, Spain, April
7-10). INSTICC Press 2010, pages 46-52,
ISBN 978-989674-024-5.
396
doc_613943428.pdf
The growth of technological development in information technology has caused challenges for many institutions.
CONCEPTUAL FRAMEWORK OF BUSINESS INTELLIGENCE ANALYSIS IN
ACADEMIC ENVIRONMENT USING BIRT
Julaily Aida Jusoh, Norhakimah Endot, Nazirah Abd. Hamid, Raja Hasyifah Raja Bongsu, Roslinda
Muda
Faculty of Informatics,
Universiti Sultan Zainal Abidin,
21300 Kuala Terengganu,
Terengganu
{julaily, nazirah, rajahasyifah, [email protected]}
[email protected]
ABSTRACT
The growth of technological development in
information technology has caused challenges for many
institutions. The enhancements of capabilities to gather
and store data in multiple source system make it more
difficult for the management in academic institutions to
analyze the important data. The objective of this paper
is to analyze students’ academic achievement based on
accommodation factor, in other words, to analyze how
accommodation can affects the students’ academic
achievement. This paper discusses the conceptual
framework for business intelligence analysis in
academic environment using BIRT tool. BIRT tool will
generate a report which illustrate and analyze the
comparison of students’ academic achievement
between the students that stay either in-campus and
out-campus.
KEYWORDS
Student Relationship Management (SRM), Data
Warehouse, Business Intelligence and Reporting Tools
(BIRT)
1 INTRODUCTION
Business Intelligence (BI) comprises of skills,
processes, technologies, applications and practices
that are functioning as leverage to institutions from
internal and external assets and to support and
improve decision making [7]. An effective BI
solution can be used to integrate data sources that
frequently used to complement marketing
initiatives and compliance analysis reporting. BI
applications that apply the concept of aggregated
information are Customer Relationship
Management (CRM) and Student Relationship
Management (SRM).
CRM is a strategy for managing company
interactions with customers, clients and sales
prospects. It involves of using technology to
organize, automate, and synchronize business
processes. The goals of CRM are to find, attract,
and win new clients and reduce the costs of
marketing and client service in different services
such as marketing, customer service, and technical
support. Meanwhile, SRM is a concept that has
been derived from CRM and can be applied to
academic institutions. The goals of SRM are to
improve the student experience, reduce drop-out
rates, and improve institutions efficiency. The
SRM involves automating and synchronizing a
number of different processes such as academic
advising, counseling, and registration. In SRM, the
components of data warehouses have not been
fully exploited by users. Data warehouse (DW) can
be defined as a relational database that is designed
for query and analysis. In this research, DW stores
information about assessment, personal and
allocation. It is usually contains historical data that
has been derived from various sources such as
Oracle, Informix, Access and MySQL database.
In addition of DW is functioning as a relational
database, DW contains environment that includes
extraction, transformation, and loading (ETL) and
Online Analytical Processing (OLAP). Designing
and maintaining ETL steps are often considered as
one of the most difficult and resource-intensive
portions of DW project. ETL meant extract,
transform and load data across various data
sources to create an accurate and quality
information. ETL steps are responsible for the
operations to take place in the DW architecture.
There are three steps involve in ETL as shown in
Table 1. Online Analytical Processing (OLAP) can
be used to analyze data from multiple databases.
390
OLAP is an approach to answer multi-dimensional
queries that encompasses relational reporting and
data mining. OLAP tools enable users to analyze
multi-dimensional data from different sources. The
databases that have been configured for OLAP can
be used for complex analytical and ad-hoc queries
with a rapid execution time.
Table 1: Process by ETL tools
STEP ACTIVITIES
Extracting The data are extracted from
the source data stores that
can be On-Line Analysis
Processing (OLAP), files,
web pages, various kinds of
documents (spreadsheets
and text documents)
Transforming Transported to the target
system or an intermediate
system for further
processing
Loading The data are loaded to the
central data warehouse
(DW)
In this research, we concentrate more on producing
reports by using Business Intelligence and
Reporting Tools (BIRT). BIRT can be recognized
as a part of BI. BIRT tool can be used to produce
reports after data has gone through ETL processes.
BIRT reports can be divided into four main parts:
a) Data: involves databases, web services and java
object that can supply data to produce report.
BIRT provide JDBC, XML, Web Services and
Flat File that can support the code from
different source.
b) Data transform: data is sorted, summarized,
filtered and grouped.
c) Business logic: structure that can be used to
create the reports.
d) Presentation: a range of options to present the
report. It involves tables, charts, texts and
others.
This paper is organized as follows. Section 2
describes related research on what and how far
does business intelligence can assist in academic
environment. Section 3 explains business
intelligence tool called BIRT and conceptual
framework of business intelligence analysis in
academic environment is described in Section 4.
We present experimental testing in Section 5 and
lastly we conclude this work in Section 6.
2 RELATED WORKS
Recently, many researches have been conducted
on fundamental knowledge of the realities in
education. Many institutions have problems in
academic results which related to the students’
activities. In order to overcome these problems,
some strategies have been proposed to improve
education systems.
A web based software using data mining and
Quality Function Deployment (QFD) was
proposed by [2] to assist higher institutions in
processes such as assessing, predicting and
managing issues that related to the student success.
[2] highlighted that the biggest challenge in higher
education was to predict the suitable academic
path for the students. This situation happened
because the institutions had lack of information to
guide the students in selecting a suitable program
and course for them. Consequently, many students
who did not interested in the enrolled courses
could not concentrate and fail to complete their
study.
[1] proposed CRM in education to manage
relationship across student lifecycle. CRM
strategies had been suggested to manage
relationship more effectively between company
and their clients, in this case, between an
institution and their students. CRM strategies
offered solutions with more powerful, different
features and functionality, and also could help to
determine quality of the student across student
lifecycle [1].
SRM were introduced in 2008 to support the BI
system in higher education [3]. [3] used DW and
data mining to analyse the students’ data. System
architecture of SRM was developed to identify
students’ activities while strategies were built to
solve problems among the students. The concepts,
practices and strategies that support education in
acquisition of knowledge to make decision making
process also discussed in [3]. Table 2 describes the
391
processes and activities that involve in SRM
architecture.
Table 2: Phase and activities in SRM architecture
PHASE ACTIVITIES
Concept Discuss process how student
acquired the knowledge
Practice Discuss about the activities
that guarantee a personal
contact with student an
effective, adequate and
closely monitoring of
academic performance.
Strategy Discuss about vision and
mission of the institution.
BI concepts could be adopted to support the
teaching and learning process and also to manage
student and lecturer relationship in higher
institutions environment. The main objectives of
[3] were to prepare the technological tools that
could be used to support the education process and
also to support the decision making process. [3, 4]
mentioned that one of the contribution of student’s
success factor was by closely monitoring the
student’s participation in academic activities.
However this factor did not occur in many higher
institutions. Students’ academic failure often
occurred during the first year because the
institutions unable to monitor their academic
activities [4]. For that reason, a conceptual
framework and technological infrastructure were
proposed by [4] and then integrated them with the
SRM system.
A case study which involved 70 students was done
by [4] to show that the SRM system was suitable
to be used in the learning process. In data
gathering process, student’s information was
collected from presential component, e-learning
system and assessment. The presential components
comprised of theoretical, practical and tutorial
orientation. Several information such as
curriculum contents, exercises and project
guidelines were included in the e-learning process.
The assessment involved were the normal
assessment period and exam assessment period.
The authors used OLAP, data mining, DW and
ETL tools to describe the case study and to
examine relevant data [4]. OLAP was used to
analyze data from different sources while data
mining was used to identify the model and pattern
of the data. Besides that, OLAP cubes were used to
analyze the student’s results verified the teaching
learning process and assessment. The ETL process
extracted data from the database. The data was
cleaned and transformed to the targeted database
or data warehouse.
3 BIRT TOOLS
Business Intelligence and Reporting Tools (BIRT)
has grown successfully since 2004. The successful
of this growing technology is achieved when the
user use the BIRT technology to produce reports in
business environment. The BIRT reports allow
user to recognize trends in data and present the
report in interactive ways and make the reports
look professional [6].
BIRT can be categorized into two categories. The
first category is report development environment
that can be used to design and develop the reports
while report development environment which is
similar to Macromedia Dreamweaver is
functioning to design web pages. This category is
connected with the Java Database Connectivity
(JDBC). JDBC is used to connect with the MySQL
database to produce reports. The Structured Query
Language (SQL) in MySQL consist of different
statement such as select, delete, update and adding
statement. The second category is Java APIs. It
provides Java Server Report for integrating
between BIRT Report Engine and Apache Tomcat
[5].
In this research, the graph generated by BIRT will
be used to produce the students’ report. This report
can be used to analyze the correlation of academic
results between the students that stay in the hostel
provided by the university (in-campus) and those
who are renting outside (out-campus
accommodations). In this study, we anticipate that
accommodation is one of the factors that might
influence students’ academic results.
392
4 CONCEPTUAL FRAMEWORK
The purpose of this framework is to identify flow
of activities involved in generating related reports.
Figure 1 shows the conceptual framework of the
research.
Figure1: Conceptual Framework
The framework can be divided into three phases
consist of input, processing and output. The
descriptions of the framework are as follow:
Phase 1: Input
In this research, the corresponding users are
lecturers, hostel supervisors and students. In
Academic System, we consider 3 existing sub-
systems that consist of assessment, allocation and
personal system. The assessment system contains
information regarding students’ assessments such
as quizzes, assignments, mid-term tests and final
examinations. Lecturers are responsible to key-in
students' marks in the assessment system. Next is
the allocation system that serves the purpose to
store students’ accommodation data (in-campus or
out-campus). This system will be managed by the
supervisors who are in-charge of students’
accommodation. In addition, personal system is
used to record students’ personal information such
as their addresses and contact numbers.
Figure 2: Input Phase
Since the existing systems were usually developed
using different database formats (eg: Oracle,
MySQL and Microsoft Access), there is the need
to process the data from different databases into a
single data format prior to further processing. This
is where the ETL process comes into play where
various data formats can be extracted, transformed
and loaded into a unified data format. The three
steps for ETL are:
Step 1: Extraction
In this process, data will be extracted from the
various sources of the sub-systems and converted
into a single database format which is appropriate
for the next process.
Step 2: Transformation
Next, a series of rules or functions will be applied
to the extracted data from the source sub-systems
for loading into the end target database format.
This step allows the data from multiple database
sources to be joined, filtered and sorted using
specific attributes. In this step, certain fields or
columns from the source sub-systems with
different names but similar attributes (eg : both
Matric_No and No_Mat represent the same
attribute, ie. students’ matric numbers) will be
selected and reformat before being loaded into the
targeted database format. This process allows data
cleansing by removing duplicates and
consequently enforce consistency by using a single
name of the same attribute.
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In addition, only some attributes deemed necessary
will be selected from the data sources depending
on the type of analysis that is going to be
performed. For example, the examination table
from assessment system consists of Name
(NAME), Matric Number (MATRIC_NO), Course
(COURSE), Semester (SEM), Assessment_marks
(ASSESS_MARK) and Cumulative Grade Point
Average (CGPA). Only important data such as
MATRIC_NO, SEM and CGPA will be selected
for the analysis. Other data in the table such as
NAME, COURSE and ASSESS_MARK will be
ignored.
Step 3: Data Loading
In this research, this process involves writing the
transformed data into MySQL database format
(end target database). Any constraints and indexes
are disabled prior to the start of the process and re-
enabled after the loading process has completed.
Phase 2: Processing
Figure 3: Processing Phase
After the ETL process, the data will be stored in a
data warehouse where it can be used for the
analysis purposes in order to generate appropriate
reports. The data warehouse will be managed by
the technical authority.
Phase 3: Output
The Business Intelligence and Reporting Tool
(BIRT) will be used to represent the necessary
reports. BIRT plays a significant role in the
process of statistical analysis and visualization to
produce the reports by collecting and analyzing
data from the database and displaying students’
academic achievement using graphs. The different
graphs obtained for in-campus and out-campus
students can be compared in order to determine
whether the factor of accommodation influences
students’ academic achievement. Users of the
system (lecturers, deans and academic
management) can view the graphs analysis which
in turn can be used to support the institution in
developing a strategy to improve students’
achievement.
Figure 4: Output Phase
5 EXPERIMENTAL TESTING
To prove the proposed framework, we use dummy
data for analysis simulation from various data
source format. We assume that the data is collected
from two sub-systems consist of assessment and
allocation system. In this research, ETL process is
use to extract data from those databases and then
clean the data and make it uniform to be
transferred into one central repository which is
MySQL database. Table 3 and 4 illustrates the
example of data extracted from academic system.
Table 5 table display the Matric Number
(MATRIC_NO), Semester (SEM) and Cumulative
Grade Point Average (CGPA) for students of
Bachelor of Computer Science (BCS) from
semester II 2010/2011.
Table 3: Example of data extracted from the assessment
system.
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Table 4: Example of data extracted from the allocation
system.
Table 4 comprises of Matric Number
(MATRIC_NO), Accomodation (ACCM) and
Block Name (ADDRESS). The data contains
information of students that stay either inside or
outside of campus in the same semester.
Figure 1 summarize the entire of analysis process
such as where and how the data are obtained,
extract, transform and load to make the data
accurate in generating reports or graph. This
experimental testing highlight four important data
to be extracted and analyzed such as matric
number (MATRIC_NO), semester (SEM),
cumulative grade point average (CGPA) and
accomodation (ACCM). Those data will be
combined to determine and analyse the correlation
of students’ academic result and accomodation. In
BIRT, the SQL statement was created and used in
producing the graph. Below is the example of the
SQL statement.
Select M.MATRIC_NO, M.CGPA,
C.ACCOMODATION FROM
Examination as M, Allocation as C
WHEREM.MATRIC_NO= C.MATRIC_NO
Figure 5: Analysis Graph of Students’ Academic Result
Figure 5 depicts the example of the correlation
analysis between students’ academic result and
accommodation for semester II 2010/2011. This
figure shows the expected graph of students’
results generated by the BIRT tool. From the
graph, we can conclude that most in-campus
students achieved better results compared to out-
campus students. It is apparent that the
deployment of the tool provides significant
information regarding students’ academic
achievement which can be used further in the
strategizing how to improve the academic result
among out-campus students. We anticipate that a
better analysis will be achieved if real life data is
used in the analysis.
6 CONCLUSIONS AND FUTURE WORK
This research proposed a conceptual framework
employed in an academic environment utilizing BIRT
tool that assists the process of generating reports for
students’ academic achievement. The significance of
this research lies in the process on how to analyze any
factors that could influence students’ academic
achievement. In future, we are going to implement
this framework using real cases in academic
environment. Not only the framework can be used to
help to predict the category of subjects (eg:
programming, database, mathematics) that students
can perform well, it can also be used to find the
correlation in students’ academic achievement for
every semester. Eventually, we are hoping to be able
to use the proposed framework to predict and identify
any patterns associated with students’ academic
achievement.
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