Sentiment Research on Student Feedback to Improve Experiences in Blended Learning Environments
R.K. Kavitha

R.K. Kavitha, Assistant Professor (SRG), Department of Computer Applications, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India. 

Manuscript received on 09 September 2019 | Revised Manuscript received on 18 September 2019 | Manuscript Published on 11 October 2019 | PP: 159-163 | Volume-8 Issue-11S September 2019 | Retrieval Number: K103409811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1034.09811S19

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Abstract: Educational data mining has aroused a great research interest among the educational institutions nowadays. Blended learning is used as a pedagogy in the field of teaching and learning. Blended learning is a fusion of online digital media with conventional teaching in classrooms where the teacher and student has to be present physically throughout teaching duration. Teacher –student interaction is made possible using the internet during the non-contact hours. For rendering a valuable blended learning environment, it is essential to gather users’ opinion or feedback on this learning methodology. Therefore, opinion-mining techniques have been used in this paper for helping the academicians to improve and promote such learning environments. Students positive or negative feelings towards the subject teaching can be analyzed using these techniques. This paper discusses how sentiment analysis can be performed on the feedback collected in a learning management system with the intention of advancing the teaching learning process. This work presents the experimental results that were obtained after comparison of various feature selection methods say Chi-square, Information Gain, Mutual Information and Symmetrical Uncertainty.

Keywords: Feature Selection, Blended Learning, Text Classification, Sentiment Analysis, Opinion Mining.
Scope of the Article: Software Engineering Tools and Environments