Predicting Instructor Performance Using Data Mining Techniques in Higher Education

ABSTRACT

Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. Generally, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception.

EXISTING SYSTEM

One of the biggest challenges of higher education institutions is the proliferation of data and how to use them to improve quality of academic programs and services and the managerial decisions. A variety of “formal and informal” procedures based on “qualitative and quantitative” methods is used by higher education institutions

DIS ADVANTAGES

  • One of the common problems in higher education is the evaluation of instructors’ low performances in a course.

PROPOSED SYSTEM

In this study, four different classification techniques, –decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis– are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics.

ADVANTAGES

  • All the classifier models show comparably high classification performances

MODULES

  • Decision tree algorithms
  • Support vector machines
  • Artificial neural networks
  • Discriminant analysis

SYSTEM REQUIREMENTS

H/W System Configuration:-

Processor - Pentium –III

  • RAM - 256 MB (min)
  • Hard Disk - 20 GB
  • Key Board - Standard Windows Keyboard
  • Mouse - Two or Three Button Mouse
  • Monitor - SVGA

S/W System Configuration:-

  • Operating System : Windows95/98/2000/XP
  • Application Server : Tomcat5.0/6.X
  • Front End : HTML, Jsp
  • Scripts : JavaScript.
  • Server side Script : Java Server Pages.
  • Database : MySQL 5.0
  • Database Connectivity : JDBC

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