Text Mining Analysis of Teaching Evaluation Questionnaires for the Selection of Outstanding Teaching Faculty Members

Abstract

This paper was conducted in collaboration with the Office of Institutional Research at National Ilan University in Taiwan to analyze textual opinions found in teaching evaluation questionnaires and applied the analysis results to assisting 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, this paper 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. This paper analyzes these 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. This paper uses a 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 (naive Bayes and 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. Furthermore, adding LSTM cells and an attention mechanism to a traditional RNN classifier effectively improved its efficacy on long-sequence tasks. As a result, we chose the attention LSTM 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 set up an analytics server that will be modularized to facilitate its integration into the systems of different schools.