DATA MINING TECHNIQUES IN ANALYSIS OF STUDENT COURSE OF STUDY
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DATA MINING TECHNIQUES IN ANALYSIS OF STUDENT COURSE OF STUDY
DATA MINING TECHNIQUES IN ANALYSIS OF STUDENT COURSE OF STUDYIn recent years, the technology of database has
become more advanced where large amount of data is required to be
stored in the databases. Data mining then attract more attention to
extract valuable information from the raw data that institution can use
for decision-making process. It applies modern statistical and
computation technologies to expose useful information hidden within the
large database to remain competitiveness among educational field, the
institution need deep and enough knowledge for a better assessment,
evaluation, planning and decision-making. Data mining helps institution
to use their current reporting capabilities to discover and identity
the hidden patterns in database and hence can be used to predict
performance of the student.
Data mining can be viewed as a result of the natural evolution of
information technology because before 1960 when database and
information technology had not evolved, analysis of data was basically
the primitive file processing which would not give the appropriate
useful information despites the huge amount of time consumed. The
evolutionary path of data mining has been witnessed in the database
industry in the development of the following database and information
technology.
- Data collection and data creation
- Data management (including data warehouse and data preparation)
- Data analysis and understanding (involving data mining and data interpretation)
Moreover, data mining is also known as knowledge
discovery in large database (KDD). Consequently, data mining consist of
more than collecting and managing data; it also includes analysis and
predictions. Important decision are often made based not on the
information rich data stored in database but rather on decision maker’s
institution, simply because maker does not have the tools to extract
the valuable knowledge embedded in the vast amount of data.
1.2 Statement of the problem
It is not feasible for people to analyze great amounts of data
without the assistance of appropriate computational tools. Therefore,
the development of tools of an automatic and intelligent nature becomes
essential for analyzing, interpreting, and correlating data in order
to develop and select strategies in the context of each application. To
serve this new context, the area of Knowledge Discovery in Databases
(KDD), came into existence with great interest within the scientific,
industrial, and commercial communities. The popular expression “Data
Mining” is actually one of the stages of the Discovery of Knowledge in
Databases. The term “KDD” was formally recognized in 1989 in reference
to the broad concept of procuring knowledge from databases. One of the
most popular definitions was proposed in 1996 by a group of
researchers. According to Fayyad, et al. (1996): “KDD is a process with
many stages, non-trivial, interactive, and iterative, for the
identification of comprehensible, valid, and potentially useful
patterns from large data sets”. It is of utmost desire to extract
valuable information from large databases.
This research work therefore addresses the intelligent prediction
of students’ course of study in higher institution based on the
historical student academic data. This will facilitate better
performance of students in high institutions.
1.3 Aim and Objectives of the Project
1.3.1 Aim
The aim of the research work is to develop a computer application
software that will be able to predict student course of study in higher
institution using classification algorithm.
1.3.2 Objectives
The following are the set of objectives addressed by the project work:
- To develop and populate student academic database
- To develop a computer application program that will be able
to mine knowledge from the students’ academic database using
Classification algorithm.
- To predict student course of study according to their Post UTME cutoff.
- To reduce the rate at which student admission is fortified.
1.4 Research Methodology
The executive of execution of research work includes the following;
- analysis of some data mining techniques i.e. data mining
techniques yield the benefit of automation on existing software and
hardware platforms, can be implemented on new system as existing
platform are upgraded and new products developed.
- consideration of sources data record i.e. the admission office student database and the department student database.
- consultation with some database developers or technologist.
- browsing on internet to get access to some websites for relevant information.
- consultation with some professional statistical analy.st
1.5 Significant of the Study
The use of data mining technique in predicting student course of
study is very significant and relevant in any academic institution
where record of each student has been collected and stored in a
database e.g system the need for knowledge discovery in academic
environment may be at admission level or faculty level. The institution
may want to know from which mode of admission does they have student
with better result. The institution may want to know the student
performance in general courses and reason for such performance. The
institution may want to predict the number of student that is to be
admitted to specific department and faculty so as to allocate
reasonable amount of resources to various departments for the session
1.6 Scope of the Study
The research work has been centered on only ‘O’ level, pre degree or UTME science courses only.
1.7 Limitation of the study
The research work is limited to the Faculty of Science and Engineering of Osun State Polytechnic, IREE.
1.8 Data mining review
Data mining is process of extract hidden pattern from data. As
more data is gathered, with the amount of data doubling every three
years, data mining is becoming an increasingly important tool to
transport this data into information. It is commonly used in a wide
range of profiling practices, while data mining can be used to uncover
patterns in data samples, it is important to be aware that the use of
non representative sample of data may produce results that are not
indicative of domain.
Similarly, data mining will not find pattern that may be present
in the domain, if those was mined, there is a tendency for
insufficiently knowledge consumer of the result to attribute magical
abilities to data mining, treating the techniques as a sort of all
seeing crystal.
DATA MINING TECHNIQUES IN ANALYSIS OF STUDENT COURSE OF STUDY
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In recent years, the technology of database has become more advanced where large amount of data is required to be stored in the databases. Data mining then attract more attention to extract valuable information from the raw data that institution can use for decision-making process. It applies modern statistical and computation technologies to expose useful information hidden within the large database to remain competitiveness among educational field, the institution need deep and enough knowledge for a better assessment, evaluation, planning and decision-making. Data mining helps institution to use their current reporting capabilities to discover and identity the hidden patterns in database and hence can be used to predict performance of the student... computer science project topics
DATA MINING TECHNIQUES IN ANALYSIS OF STUDENT COURSE OF STUDY