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Predicting Instructor Performance Using Data
Mining Techniques in Higher Education
Abstract:
• Data mining applications are becoming a more common tool in considerate
and cracking educational and administrative problems in higher education .
• One of the common problems in higher education is the evaluation of
instructors’ performances in a course. The most widely applied tool to
evaluate the instructors’ performance in a course is through surveying
students’ responses about the course and its instructor through a
questionnaire.
• In classification, there are many different methods and algorithms possible
to use for building a classifier model.
• In this system Bayesian classification approach is proposed.
• Different dimensions of course and instructor effectiveness are measured
with course evaluation questionnaires in higher education institutions and
these findings may be used to improve measurement instruments .
• The performances of these models are evaluated on the test data in terms
of accuracy, precision, recall, and specificity .
Existing System:
• In Existing system artificial neural networks (ANN), discriminant analysis
(DA) method is used predict the instructor performance.
• ANNs are generally defined by three parameters: 1) The interconnection
pattern and weights between the nodes and different layers of neurons, 2)
The learning process for updating the weights, and 3) The activation
function that converts a neuron’s weighted input to its output .
• Discriminant analysis (DA), technique used in this system . It is a statistical
data mining technique used to predict categorical response variable .
• DA has the underlying assumptions of multivariate normality
and all groups having equal covariance structures
Disadvantages:
• ANN needs long training time to construct a well-suited model.
• It is hard to Interpret.
• It is less efficient.
Proposed System:
• In this proposed system Bayesian classification approach is used to predict
the instructors performance.
• However, most of the educational mining research focuses on
modeling student’s performance.
•
To estimate the instructors’ performance in a course is through measuring
students’ reactions about the course and its instructor performance through a
questionnaire .
• Decision tree algorithms,support vector machines– and seven different
classifiers are used and all have performance measures approximately 90%
and above in test dataset.
• This finding indicates the effectiveness of using data mining techniques in
course evaluation data and higher education mining.
Advantages:
• It is more efficient.
• it can tolerate noisy data
• It can be used even if when there is no relationship between variables and
classes
Software Requirements:
• Operating system: - Windows XP.
•
Front End
: - C#
•
Back End
: - SQL Server2012
Hardware Requirements:
•
System
: Pentium IV 2.4 GHz.
•
Hard Disk
•
Floppy Drive : 1.44 Mb.
•
Monitor
: 15 VGA Color.
•
Mouse
: Logitech.
•
RAM
: 2GB.
: 160 GB.
References:
• A. M. Abaidullah, N. Ahmed, and E. Ali, ‘‘Identifying hidden patterns in
students’ feedback through cluster analysis,’’ Int. J. Comput. Theory Eng.,
vol. 7, no. 1, pp. 16–20, 2015.
•
N. Delavari, S. Phon-Amnuaisuk, and M. R. Beikzadeh, ‘‘Data mining
application in higher learning institutions,’’ Inform. Edu.-Int. J., vol. 7, no.
1, pp. 31–54, 2007.
•
M. Goyal and R. Vohra, ‘‘Applications of data mining in highereducation,’’
Int. J. Comput. Sci. Issue, vol. 9, no. 2, pp. 113–120,2012.
• J. Luan, ‘‘Data mining and its applications in higher education,’’ in New
Directions for Institutional Research, vol. 113. New York, NY, USA:Wiley,
2002.
• J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques.
Waltham, MA, USA: Morgan Kaufmann, 2012.
•
J. Luan, ‘‘Data mining and knowledge management in higher education potential applications,’’ in Proc. AIR Forum, Toronto, ON, Canada,
2002,pp. 1–16.
• B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch,
‘‘Predicting student performance: An application of data mining methods
with an educational Web-based system,’’ in Proc. 33rd Annu. IEEE
Frontiers Edu., vol. 1. Nov. 2003, p. T2A-13.