Download logical framework design of online educational system (oes)

Online Education Guidelines System for Students
Hrishik Bhusari
Mayuri More
Neelam Bhatia
Trushant Lohakare
Prof. P. S. Hanvate
Student,
NBN Sinhgad
School Of
Engineering, Pune
Student,
NBN Sinhgad
School Of
Engineering, Pune
Student,
NBN Sinhgad
School Of
Engineering, Pune
Student,
NBN Sinhgad
School Of
Engineering, Pune
Assistant Professor,
NBN Sinhgad
School Of
Engineering, Pune
hrishikbhusari@g
mail.com
mayurimore08@g
mail.com
neelam.bhatia101
@gmail.com
lohakare.tushar@g
mail.com
poonamkumar.han
[email protected]
ABSTRACT
E-learning is becoming a popular alternative to textbook
learning. With a rapid development of Internet Technologies,
the number of online book selling and educational guideline
websites has increased, which enhanced the competition
among them. It is not feasible for a user to go through each
and every website and search most suitable data according to
his requirement. This system proposes one platform for
student’s education which will facilitate various reference
books (arranged according to year, branch), question papers,
exams, etc. It also helps user to have their useful information
using recommendation system via ratings, which recommends
the information/data that can be beneficial to the user.
KEYWORDS
recommendation, online education, data mining
INTRODUCTION
In 21st century, the information technology changes the whole
world and thinking of human society. It provide powerful
technological basis for the revolution and development
conventional education system. Combination of internet and
education would create the education system in the future
which connects the teachers and students between thousand
mails and realize the face-to-face communication. This
changes education a lot so, the online education system
becomes a popular theme which has been taken attention by
varies of countries.
Unfortunately data of existing online education system is very
tangled up by education material without considering
student’s demand and area of interest. Therefore major
problem is inefficiency of systems [1]. The main objective of
the system is providing educational information to students.
This is a totally web-based search engine, its main aim is to
provide education services in one place, user does not need to
search different sites to get information about any subject or
any educational material.
RELATED WORK
Significant amount of work has been dedicated to developing
projects similar to the one being discussed in this paper. The
existing system may not have the information which the user
wants. For his information, the user needs to search for other
systems also. Qingsong Tu presents a framework in [1] for the
education system which helps students to find their
educational material.
DATA ANALYSIS
The online education system can be divided into various
stages such as intelligent studying, analysis and
recommendation. First the system records all information of
studying phase and test the result and so on to get the interest,
ability technique of user. Then the system turns on analysis of
this knowledge and makes the suitable study technique,
respectively. Finally the online education system recommends
the study content and resources according to intellective
ability and intelligence level of the user. This process forms a
circulation to renovate the system.
When the user enters the system for the first time, a
registration is required. This phase requires user’s username,
password, mail id, name, branch, year, etc. In the next phase,
the system will present the study related content dynamically,
according to user’s information and related search history.
During the studying process, user can choose
various study techniques/facility which he likes. For example,
if the user can’t get/like the content in reading, then see videos
by video links. This is one of the most useful benefits of
online educational system over traditional studying mode. The
dynamic technique of the system helps the user to surf the
newly related knowledge or further information of the subject.
LOGICAL FRAMEWORK DESIGN OF
ONLINE EDUCATIONAL SYSTEM
(OES)
The OES consists of resource database layer, extract module,
data analysis layer i.e. mining/association rules and
application layer. A student carries an activity in application
layer. The data of browsing and downloading are collected to
extract module. The user requirements are extracted in the
extract module and proceeds with data mining of
information and store in the user database layer. Meanwhile
the data from the user database is analyzed in data analysis
layer & data process layer takes part in scheduling and
matching the study resource in database. Then the analysis
center recommends the study material according to the
user’s information and analyzed data.
Confidence indicates the number of times the if/then
statements have been found to be true.
Result Generation
This is where students can see the result which is nothing but
the predicted and recommended books or any other study
material like video links, question papers and so on. The
student can also rate via up-like or down-like according to his
interest. This likes will be saved in the database for further
recommendations.
Application Layer
Fig 1. Logical Framework of the proposed model
Application layer is the user friendly layer. It provides many
system functions such as log-in, registration, studying,
navigation, etc. Besides data analysis layer, it represents the
list of recommendations for the user.
Database Layer
KEY TECHNOLOGIES
The database Layer is an application programming interface
which allows the communication between a computer and
application. The database consists of study database and study
material database. The user stores the user information like
username, password, mail id, name, branch, year, etc. The
study material database includes various kinds of informative
books, video links, university question papers, Association
rule learning is a popular and well researched method for
discovering interesting relations between variables in large
databases. It is intended to identify strong rules discovered in
databases using different measures of interestingnes.
The user characteristic modeling is the way of summing up
the user model that can be read and calculated from the user
information which can be collected from many aspects. For
instance, the keywords input by the user for inquiring the
information, the browsing history, system log and automatic
records of the system server and so on. The data mining
techniques like k-means is based on searching action of user
always shows extract characteristic from the document
content which the user has browsed according to the
frequency characteristic of the entry. K-means clustering is a
data mining/machine learning algorithm used to cluster
observations into groups of related observations without any
prior knowledge of those relationships. K-means clustering
aims to partition n observations into k clusters in which each
observation belongs to the cluster with the nearest mean,
serving as a prototype of the cluster. K-means is one of the
simple unsupervised learning algorithms that solve the wellknown clustering problem. The procedure follows a simple
and easy way to classify a given data set through a certain
number of clusters fixed a priori.
Extract Module
Extract module layer includes user information extraction and
organization. The user information extraction extracts
information from the study history records, visiting and
downloading records, keyword inquiring records, etc.
Function of organization center is to pre-process this
information before retrieval and store the results into the
database of metadata.
Steps for K-means clustering:-
Mining (Association Rules)
This is where association rules are applied. By taking
recommended values from database we are going to apply
association rules. Association rules are if/then statements that
help uncover relationships between seemingly unrelated data
in a relational database or other information repository. An
example of an association rule would be "If a customer buys a
dozen eggs, he is 80% likely to also purchase milk."
Association rules are created by analyzing data for frequent
if/then patterns and using the criteria support and confidence
to identify the most important relationships. Support is an
indication of how frequently the items appear in the database.
Assume X={x1,x2,x3,……..,xn}set of data points and
V={v1,v2,…….,vc} be the set of centers.
1) Randomly select ‘c’ cluster centers.
2) Calculate the distance between each data point and
cluster centers.
3) Assign the data points to the cluster center whose distance
from the cluster center is minimum of all the cluster centers.
4) Again calculate the new cluster center using:
REFERENCES
[1]
Quingsong Tu & Jian Liu. Research on Autonomous
Online Education System based on Intelligent
Recommendation
[2]
B. Lee (Volume 1) Introducing System Analysis and
Design.
Where ‘ci’ represents the number of data points in ith
cluster.
[3]
James. A. Senn (Second Edition) Analysis and Design of
Information Systems.
5) Again the distance between each data point and new
obtained cluster centers are calculated.
[4]
Roger. S. Pressman Software Engineering
6) If no data point was reassigned then stop, else repeat from
step 3.
[5]
http://www.hindustantimes.com/comment/india-seducation-system-needs-to-get-online-with-access-forall/article1-1385860.aspx
The Advantage of k-means is, it is easy to understand, fast
and robust. It also gives better result when data set are well
separated from each other.
Fig 1.Showing the result of k-means for ‘N’ = 60 and ‘c’=3
By analysis to many mark or position information existing in
the information resource document, such as hypertext mark in
HTML, subjects, keywords, abstracts of the scientific and
technological literature resource, the weight and importance
of the entry can be confirmed.
CONCLUSION
The OES provides a new relevant way to improve the current
education system. It uses the recommendation technique
which helps user to learn the thing better. By recording and
analyzing various kinds of information during the study of the
user, the OES provides interesting value. Then according to
this value it recommends the study techniques, suggestions
and resources intelligently. The application of the OES faces
many technological difficulties. We proposed a logical
framework of the system, and introduced the key
technologies, and we should carry on further discussion on its
application in our real education situation in our country.