| Summary: | 碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 107 === The study was conducted in collaboration with the office of Institutional Research (IR) at National Ilan University (NIU) in Taiwan to analyze textual opinions in teaching evaluation questionnaires, then apply the analysis results to the teaching of teachers and the selection of outstanding teachers. Teachers’ teaching can be awarded by the teaching evaluation questionnaires, but it takes a lot of manpower and time to extract the students’ opinions. Therefore, the research develops a set of system to automate the analysis of textual opinions in teaching evaluation questionnaires, not only providing reference for the improvement of teachers'' teaching, but also providing a reference for outstanding teacher selection.
The study used data mining methods to qualitatively evaluate teaching questionnaires. First, the textual of the teaching questionnaire is pre-processed and labeled with positive, neutral and negative sentiments, then vectorized and analyzed. The study compared the textual sentiment performance of five kinds of classification models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Attention RNN, Attention LSTM and Convolutional Neural Network (CNN), and finally selected the most suitable classifier.
We found that classifiers with a small number of phrases from feature extraction is better. Among those models CNN model has best performance with non-negative sentiment recognition rate of 96.0% and negative sentiment recognition rate of 94.2%, as well as non-positive sentiment recognition rate of 94.1%, and positive sentiment recognition rate of 95.9%. Thus, the study chose CNN as a classifier for teacher teaching improvement and outstanding teacher selection goals.
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