Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach
Universities collect qualitative and quantitative feedback from students upon course completion in order to improve course quality and students’ learning experience. Combining program-wide and module-specific questions, universities collect feedback from students on three main aspects of a course na...
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sg-smu-ink.sis_research-38882021-07-05T06:50:29Z Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach NITIN, Gokran Ila GOTTIPATI, Swapna SHANKARARAMAN, Venky Universities collect qualitative and quantitative feedback from students upon course completion in order to improve course quality and students’ learning experience. Combining program-wide and module-specific questions, universities collect feedback from students on three main aspects of a course namely, teaching style, content, and learning experience. The feedback is collected through both qualitative comments and quantitative scores. Current methods for analyzing the student course evaluations are manual and majorly focus on quantitative feedback and fall short of an in-depth exploration of qualitative feedback. In this paper, we develop student feedback mining system (SFMS) which applies text analytics and opinion mining approach to provide instructors a quantified and exhaustive analysis of the qualitative feedback from students. 2015-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2888 info:doi/10.1109/FIE.2015.7344296 https://ink.library.smu.edu.sg/context/sis_research/article/3888/viewcontent/Analyzing_Edn_Comments_Sentiments_FIE_2015.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 Student feedback education data mining topics sentiments text analytics clustering Computer Sciences Higher Education |
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Student feedback education data mining topics sentiments text analytics clustering Computer Sciences Higher Education NITIN, Gokran Ila GOTTIPATI, Swapna SHANKARARAMAN, Venky Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
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Universities collect qualitative and quantitative feedback from students upon course completion in order to improve course quality and students’ learning experience. Combining program-wide and module-specific questions, universities collect feedback from students on three main aspects of a course namely, teaching style, content, and learning experience. The feedback is collected through both qualitative comments and quantitative scores. Current methods for analyzing the student course evaluations are manual and majorly focus on quantitative feedback and fall short of an in-depth exploration of qualitative feedback. In this paper, we develop student feedback mining system (SFMS) which applies text analytics and opinion mining approach to provide instructors a quantified and exhaustive analysis of the qualitative feedback from students. |
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text |
author |
NITIN, Gokran Ila GOTTIPATI, Swapna SHANKARARAMAN, Venky |
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NITIN, Gokran Ila GOTTIPATI, Swapna SHANKARARAMAN, Venky |
author_sort |
NITIN, Gokran Ila |
title |
Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
title_short |
Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
title_full |
Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
title_fullStr |
Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
title_full_unstemmed |
Analyzing Educational Comments for Topics and Sentiments: A Text Analytics Approach |
title_sort |
analyzing educational comments for topics and sentiments: a text analytics approach |
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Institutional Knowledge at Singapore Management University |
publishDate |
2015 |
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https://ink.library.smu.edu.sg/sis_research/2888 https://ink.library.smu.edu.sg/context/sis_research/article/3888/viewcontent/Analyzing_Edn_Comments_Sentiments_FIE_2015.pdf |
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