Predicting Instructors Performance in Higher Education Systems

Journal Title: EAI Endorsed Transactions on Energy Web - Year 2018, Vol 5, Issue 18

Abstract

In recent years, knowledge mining has become one of the effective tools for data analysis and information management systems. Educational sector is the recent research endeavors that make use of data mining algorithms. Prior works carried in data mining algorithms like J48 Decision Tree, Multilayer Perception, Naïve Bayes, and Sequential Minimal Optimization impose issues like the curse of dimensionality, cardinality and imbalance attributes. In this paper, we have proposed FA-Paired t-test method which is a novel knowledge discovery process to predict the performance of the instructors. The aim of the study is to find the factors that associated for the prediction of teaching quality. Thus, the contribution of the factor analysis method helps to find the relevant attributes from a set of attributes. Then, the selected attributes are fed as input to paired t-test model which find the associations between those linked attributes. The selected attributes are experimenting using SPSS modeler. Many attributes test for the performance evaluation. It is strongly found that content arrangement, delivery of speech, effective class hours and completion of the course helps to predict the quality of the teaching. In addition to, the proposed model is compared to prior two classifiers, named, J48DT and Naïve Bayes which shows our proposed method works better than other two classifiers in term of Attribute reduction and evaluation process.

Authors and Affiliations

Dr. K. Kalaiselvi, J. Sowmiya

Keywords

Related Articles

Multiagent voltage and reactive power control system

This paper is devoted to the research of multiagent voltage and reactive power control system development. The prototype of the system has been developed by R&D Center at FGC UES (Russia). The control system architecture...

Multi-Class Queuing Networks Models for Energy Optimization

The increase of energy consumption and the related costs in large data centers has stimulated new researches on techniques to optimize the power consumption of the servers. In this paper we focus on systems that should p...

Comparative Analysis of Machine Learning Algorithms on IVR data

We aim to classify IVR data (Interactive Voice Response) and provide a detailed summary of the methods and techniques we employed to create a classifier model of reasonably high accuracy. This model is built to process l...

Training of engineering personnel in the Sevastopol State University on the basis of the project “Synergy”

In the article, the problems of modern engineering training are discussed. The practice of Sevastopol State University in this field is represented. The participation of the university in the international project “Syner...

Cys-modified zinc oxide 1D-nanostructures formation for gas sensors application

In this work, the ZnO-based materials were synthesized for gas sensors applications. The nanostructures formation was carried out by mild hydrothermal synthesis with the variations in surfactant concentration. The qualit...

Download PDF file
  • EP ID EP45361
  • DOI http://dx.doi.org/10.4108/eai.12-6-2018.154811
  • Views 249
  • Downloads 0

How To Cite

Dr. K. Kalaiselvi, J. Sowmiya (2018). Predicting Instructors Performance in Higher Education Systems. EAI Endorsed Transactions on Energy Web, 5(18), -. https://europub.co.uk/articles/-A-45361