Clustering models for topic analysis in graduate discussion forums

Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This...

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Main Authors: GOKARN NITIN, Mallika, GOTTIPATI, Swapna, SHANKARARAMAN, Venky
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/4516
https://ink.library.smu.edu.sg/context/sis_research/article/5519/viewcontent/ICCE_Content_Analysis_V1.6.pdf
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spelling sg-smu-ink.sis_research-55192021-07-05T06:27:52Z Clustering models for topic analysis in graduate discussion forums GOKARN NITIN, Mallika GOTTIPATI, Swapna SHANKARARAMAN, Venky Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within and beyond the classroom learning settings. We developed the analysis model and conducted our experiments on a graduate course in Information Systems. The results show that the proposed techniques are useful in discovering knowledge from the forums and generating user-friendly visualizations. Such results can be used by the faculty to analyse the students’ discussions and study the strengths and weaknesses of the students’ cognitive knowledge on course topics. 2019-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4516 https://ink.library.smu.edu.sg/context/sis_research/article/5519/viewcontent/ICCE_Content_Analysis_V1.6.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Topic analysis Online Discussion Forums Clustering Models Topic evolutions Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Topic analysis
Online Discussion Forums
Clustering Models
Topic evolutions
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Topic analysis
Online Discussion Forums
Clustering Models
Topic evolutions
Databases and Information Systems
Numerical Analysis and Scientific Computing
GOKARN NITIN, Mallika
GOTTIPATI, Swapna
SHANKARARAMAN, Venky
Clustering models for topic analysis in graduate discussion forums
description Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within and beyond the classroom learning settings. We developed the analysis model and conducted our experiments on a graduate course in Information Systems. The results show that the proposed techniques are useful in discovering knowledge from the forums and generating user-friendly visualizations. Such results can be used by the faculty to analyse the students’ discussions and study the strengths and weaknesses of the students’ cognitive knowledge on course topics.
format text
author GOKARN NITIN, Mallika
GOTTIPATI, Swapna
SHANKARARAMAN, Venky
author_facet GOKARN NITIN, Mallika
GOTTIPATI, Swapna
SHANKARARAMAN, Venky
author_sort GOKARN NITIN, Mallika
title Clustering models for topic analysis in graduate discussion forums
title_short Clustering models for topic analysis in graduate discussion forums
title_full Clustering models for topic analysis in graduate discussion forums
title_fullStr Clustering models for topic analysis in graduate discussion forums
title_full_unstemmed Clustering models for topic analysis in graduate discussion forums
title_sort clustering models for topic analysis in graduate discussion forums
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4516
https://ink.library.smu.edu.sg/context/sis_research/article/5519/viewcontent/ICCE_Content_Analysis_V1.6.pdf
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