Predicting Instructor Performance in Higher Education Using Data Mining CART Technique
Author(s):
P.Ankamma, N.Vinayasree
Keywords:
Data mining, CART Technique, Classification algorithm, Decision tree, Artificial neural networks.
Abstract
Data mining techniques and methods are analyzes the students data to build a analytical model for students performance in higher education. The data mining is a process of result patterns in the fields of relative databases. Data mining is used for arrangement of educational problems by using techiniques for measuring the student and instructor performance. In this paper we are used different classification techniques, decision tree algorithms, artificial neural networks and CART technique. These performances are very high over a datasets of composed the responses of the students to course evaluation questionnaire using precision, exactness, and specificity performance. In this system get high results compare to other techniques over CART technique. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The conclusion of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments.
Article Details
Unique Paper ID: 145505

Publication Volume & Issue: Volume 4, Issue 10

Page(s): 491 - 494
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