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Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques

Over the years, in making successful careers, higher education has gained prominence over the graduate students. Faculty teaching practice and performance are thus given the utmost importance in developing students’ quality for performance in academics. The performance of the faculty plays an...

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Main Author: Khalid, Atikah
Format: Final Year Project
Language:English
Published: IRC 2020
Subjects:
Q Science (General)
Online Access:http://utpedia.utp.edu.my/21803/1/23300_Atikah%20Khalid.pdf
http://utpedia.utp.edu.my/21803/
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spelling my-utp-utpedia.218032021-09-24T09:56:41Z http://utpedia.utp.edu.my/21803/ Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques Khalid, Atikah Q Science (General) Over the years, in making successful careers, higher education has gained prominence over the graduate students. Faculty teaching practice and performance are thus given the utmost importance in developing students’ quality for performance in academics. The performance of the faculty plays an important role in academic institutions. Evaluating the faculty members' performance helps to gather critical information and discover new ways of improving them. In this paper, the proposed system can be used as a comprehensive system for evaluating, reporting and analyzing data with a promising audience by utilizing the visual analytics platform in using the educational mining techniques. Based on different parameters, the faculty teaching practice and performance are evaluated and projected by building models. The sample data is collected, preprocessed, and model learning is done using Decision Tree, Support Vector Machine (SVM) and Artificial Neural Network (ANN) in this evaluation. Besides, an analysis of the variable importance for each classifier model is done to see which questions appear in determining the success of faculty members' performance. The idea of this paper is to indicate the effectiveness of Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques on Student's Self-Reflection Tool (SSRT) survey. IRC 2020-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21803/1/23300_Atikah%20Khalid.pdf Khalid, Atikah (2020) Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Khalid, Atikah
Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
description Over the years, in making successful careers, higher education has gained prominence over the graduate students. Faculty teaching practice and performance are thus given the utmost importance in developing students’ quality for performance in academics. The performance of the faculty plays an important role in academic institutions. Evaluating the faculty members' performance helps to gather critical information and discover new ways of improving them. In this paper, the proposed system can be used as a comprehensive system for evaluating, reporting and analyzing data with a promising audience by utilizing the visual analytics platform in using the educational mining techniques. Based on different parameters, the faculty teaching practice and performance are evaluated and projected by building models. The sample data is collected, preprocessed, and model learning is done using Decision Tree, Support Vector Machine (SVM) and Artificial Neural Network (ANN) in this evaluation. Besides, an analysis of the variable importance for each classifier model is done to see which questions appear in determining the success of faculty members' performance. The idea of this paper is to indicate the effectiveness of Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques on Student's Self-Reflection Tool (SSRT) survey.
format Final Year Project
author Khalid, Atikah
author_facet Khalid, Atikah
author_sort Khalid, Atikah
title Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
title_short Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
title_full Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
title_fullStr Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
title_full_unstemmed Visual Analytics for Faculty Teaching Practice and Performance using Educational Mining Techniques
title_sort visual analytics for faculty teaching practice and performance using educational mining techniques
publisher IRC
publishDate 2020
url http://utpedia.utp.edu.my/21803/1/23300_Atikah%20Khalid.pdf
http://utpedia.utp.edu.my/21803/
_version_ 1739832914715082752
score 13.211869

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