Feedback Analysis in Outcome Base Education Using Machine Learning
Htar Htar Lwin, Sabai Oo, Kyaw Zaw Ye, Kyaw Kyaw Lin, Wai Phyo Aung, Phyo Paing Ko
Abstract
In outcome base education, we need to utilize students' feedback as one of the key inputs to determine the effectiveness of teaching and learning activities of a university. Analyzing only rating scores does not reveal the true sentiment of students. Textual comments are more reflected true sentiment of the students. This paper is to implement a feedback analysis system based on rating score and textual comments. We used two types of dataset which include rating scores and textual comments, respectively. K-means clustering algorithm is used to cluster rating scores. Classification models are built with various classification algorithms using labeled dataset that got from clustering step. For textual comment analysis, we used Naïve Bayes classifier to train a model and classify test dataset into negative and positive sentiments using 10-fold cross validation. We also tested the model to classify the unlabeled textual comments. According to experimental results, we found that Support Vector Machine gives the best precision result for rating score classification. For textual comment analysis, Naïve Bayes classifier gives optimum result.