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EVALUATING PERFORMANCE AND DETECTING UNDESIRABLE
STUDENT BEHAVIOUR USING CLUSTERING APPROACH
ABSTRACT:
Evaluating student’s performance and undesirable behavior in educational
environments is an important task. Student’s academic Education details &
performance is based upon various factors like personal details or demographic,
social, psychological details etc.The data mining techniques are more helpful in
classifying educational database and help us in evaluating the performance and
undesirable behavior of a student.
OBJECTIVE:
The main objective of higher education institutions is to provide quality
education to their students. One way to achieve highest level of quality in higher
education is by discovering student’s performance and undesirable behavior for
students who need special attention and allow the teacher to provide appropriate
advising/counseling
Existing system:
 As of now, existing system take only performance into consideration which is
not sufficient for having system, which can help us to evaluate performance
of a student
 We are not having a system which would help us to integrate the performance
and undesirable into consideration.
Disadvantages:
 Existing system miss the undesirable data for the students.
 It do not take into consideration the demographic, social, psychological data
for the student.
Proposed system:
 The work aims to develop a trust model using data mining techniques which
mines required information, so that the present education system may adopt
this as a strategic management tool.
 The proposed system use educational data mining techniques to evaluate
performance and identify undesirable behavior.
 The approach may assist educational managers in supervising the
development of students at the end of each academic term, identifying the
ones with difficulties to fulfill their requirements.
Advantages:
 Educational database contain the useful information for Evaluating
a
Students.
 The data mining techniques are more helpful in classifying educational
database and help us in evaluating the performance and undesirable behavior
of a student.
Architecture Diagram:
Staff Review
Depart
ment
Data
Datasets
Data
Preprocesssing
Classifi
cation
Visual
izatio
n
&
Result
Software requirements:
 Operating system : - Windows 7. 32 bit
 Coding Language : C#.net 4.0
 Data Base
: SQL Server 2008
Hardware Requirements:
 System
 Hard Disk
: Pentium IV 2.4 GHz.
: 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor
: 15 VGA Colour.
 Mouse
: Logitech.
 Ram
: 512 Mb.
References:
1. HaoWei; Xingyuan Chen; Chao Wang “User behavior analyses based on
network data stream scenario” Communication Technology (ICCT), 2012
IEEE 14th International Conference on, P: 1017 – 1021, Year: 2012 .
2. Barbosa, L., and Feng, J. Robust sentiment detection on twitter from biased
and noisy data. In Proc. of Coling, 2010.
3. Davidov, D, Tsur, O and Rappoport, A. Enhanced sentiment learning using
twitter hashtags and smileys. In Proceedings of Coling, 2010.
4. Nagy, A., & Stammberger, J.. “Crowd Sentiment Detection during Disasters
and Crises”. Proceedings of the 9th International ISCRAM Conference, (S. 19). 2012, Vancouver, Canada.
5. Saif, H., He, Y., & Alani, H. Alleviating Data Sparsity for Twitter Sentiment
Analysis. Workshop: The 2nd Workshop on Making Sense of Microposts
(#MSM2012): Big things come in small packages at World Wide Web
(WWW) 2012. Lyon, France, 2012.