Text Mining and Analysis of Student Surveys in Selection of Outstanding Teaching Faculty Member

• Main Authors: Chiu-Wang Tseng, 曾秋旺
• Other Authors: 周瑞仁
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/56d8dn

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 106 === The study was conducted in collaboration with the Office of Institutional Research (IR) at National Ilan University (NIU) in Taiwan to analyze textual opinions found in teaching evaluation questionnaires and apply the analysis results to assisting in the selection of outstanding teaching faculty members. The selection of outstanding teachers requires that selection committee members spend a large amount of time reviewing written data. Therefore, the study develops a set of systems for the analysis of textual opinions in teaching evaluation questionnaires, providing reference materials for the selection committee. The teaching evaluation questionnaire is a form of educational data. The study analyzes this data using educational data mining. In text mining, text sentiment analysis is a common textual data quantification method that can analyze the sentiment tendency of a text author. The study uses text sentiment analysis to quantify the students’ textual opinions and to provide the selection committee with the sentiment tendency of students’ comments on teaching faculty members. We analyze text sentiment separately for different classifiers by using the Chinese text sentiment analysis kit SnowNLP. We compare the efficacy of classifiers that do not take time series factors into consideration (naïve Bayes, fully connected neural network) to those that do (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) RNN, and attention RNN). We found that classifiers that consider time series factors are more effective at analyzing text sentiment. Further, adding LSTM cells and an attention mechanism to a tradition RNN classifier effectively improved its efficacy on long-sequence tasks. As a result, we chose the attention LSTM RNN classifier—with a positive sentiment recognition rate of 97% and a negative sentiments recognition rate of 87%—as our preferred text sentiment classifier. Finally, we used an analysis process to set up an analytics server that will be modularized to facilitate its integration into the systems of different schools.