EVALUATING PERFORMANCE AND DETECTING UNDESIRABLE STUDENT BEHAVIOR USING CLUSTERING APPROACH

EVALUATING PERFORMANCE AND DETECTING UNDESIRABLE STUDENT BEHAVIOR 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:
EVALUATING PERFORMANCE AND DETECTING UNDESIRABLE STUDENT BEHAVIOR USING CLUSTERING APPROACH
EVALUATING PERFORMANCE AND DETECTING UNDESIRABLE STUDENT BEHAVIOR USING CLUSTERING APPROACH
Software requirements:
Operating system : - Windows 7. 32 bit
Coding Language : C#.net 4.0
Data Base : SQL Server 2008 

Hardware Requirements:
System      : Pentium IV 2.4 GHz.
Hard Disk        : 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. 1-9). 2012, Vancouver, Canada.

1 comment:

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