%0 Conference Paper %1 conf/fie/NitinGS15 %A Nitin, Gokarn Ila %A Gottipati, Swapna %A Shankararaman, Venky %B 2015 IEEE Frontiers in Education Conference (FIE) %D 2015 %I IEEE Computer Society %K demo educational sentiment-analysis sfms system topic-modeling %P 1-9 %R 10.1109/FIE.2015.7344296 %T Analyzing educational comments for topics and sentiments: A text analytics approach. %U https://ieeexplore.ieee.org/abstract/document/7344296 %X 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 and avail insights on their teaching practices and this in turn will lead to improved student learning. %@ 978-1-4799-8454-1

@inproceedings{conf/fie/NitinGS15, abstract = {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 and avail insights on their teaching practices and this in turn will lead to improved student learning.}, added-at = {2021-05-10T16:10:33.000+0200}, author = {Nitin, Gokarn Ila and Gottipati, Swapna and Shankararaman, Venky}, biburl = {https://www.bibsonomy.org/bibtex/286b5a13a7422d764217be7ded9f4574f/ghagerer}, booktitle = {2015 IEEE Frontiers in Education Conference (FIE)}, crossref = {conf/fie/2015}, doi = {10.1109/FIE.2015.7344296}, ee = {http://doi.ieeecomputersociety.org/10.1109/FIE.2015.7344296}, interhash = {ecd9ae5d1f588d45e802db91dd561e92}, intrahash = {86b5a13a7422d764217be7ded9f4574f}, isbn = {978-1-4799-8454-1}, keywords = {demo educational sentiment-analysis sfms system topic-modeling}, pages = {1-9}, publisher = {IEEE Computer Society}, timestamp = {2021-05-10T16:10:57.000+0200}, title = {Analyzing educational comments for topics and sentiments: A text analytics approach.}, url = {https://ieeexplore.ieee.org/abstract/document/7344296}, year = 2015 }