Student Feedback Analysis with Recommendations
DOI:
https://doi.org//10.32628/CSEIT22847Keywords:
Student Feedback, Systematic Literature Review, Sentiment AnalysisAbstract
When modern institutions use their student data, they can better understand their students' educational experiences. Teachers are better able to educate their kids as a result of this. Big Data is also being used to transform the educational system in order to provide a well-rounded education to pupils. Analyzing how well the teaching has been effective for the students is an important criterion in teaching. Students' feedback is an important component that should be encouraged in order to improve the learning experience. Each faculty member is rated on a scale of 1 to 5, with 5 being the highest, based on student feedback. To capture and process emotions from feedback, Naive Bayes classifiers and simple text mining algorithms were applied. Rating scores are clustered using the K-means clustering technique.We use sentiment analysis techniques to analyse student feedback in this work.
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