Research Article
Predicting Instructor Performance in Higher Education Using Intelligent Agent Systems
@ARTICLE{10.4108/eai.13-7-2018.164584, author={J. Sowmiya and K. Kalaiselvi}, title={Predicting Instructor Performance in Higher Education Using Intelligent Agent Systems}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={30}, publisher={EAI}, journal_a={EW}, year={2020}, month={5}, keywords={Communication Technology, Educational site, Intelligent agents, Teaching route and Teaching Quality}, doi={10.4108/eai.13-7-2018.164584} }- J. Sowmiya
K. Kalaiselvi
Year: 2020
Predicting Instructor Performance in Higher Education Using Intelligent Agent Systems
EW
EAI
DOI: 10.4108/eai.13-7-2018.164584
Abstract
The arrival of information and communication technology is increasing due to growth of World Wide Web. Predicting the instructor’s performance using the teaching style and their student’s profile is a challenging issue in the education field. Several studies have been conducted to improve the student’s quality by following dynamic contents. Ant Colony Optimization (ACO) is being widely studied by the researchers to optimize the quality of the educational content. This paper researches on predicting the performance of instructors using their teaching attributes. Initially, the profile of the student and the teaching attributes are designed to form the teaching route. Ants as intelligent agents such as filtering agent and a teaching path agent were designed. Experimental results have shown the efficiency of the proposed model. Finally, we discover that the certain set of knowledge like resource efficiency, updated knowledge, positive approach and well –planned teaching models plays a vital role to predict the instructor’s performance [RA-7] .
Copyright © 2020 J. Sowmiya et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
