教學問卷文字意見探勘應用於優良教師之遴選

Abstract

本研究與國立宜蘭大學校務研究辦公室合作，分析教學問卷文字意見，將分析結果應用於輔助優良教師遴選。優良老師遴選需耗費遴選委員大量時間進行書面資料審查，因此本研究開發一套系統，進行教學問卷文字意見的分析，提供遴選委員決策之參考。 教學問卷屬於教育性的資料，本研究依教育資料探勘 (Educational Data Mining) 的流程進行分析；在文字探勘中，文字情緒分析是一種常見的文字資料量化方式，可以分析文字作者的情緒傾向，本研究以此量化教學問卷文字意見，將學生對教師的評論情緒傾向提供給遴選委員參考。本研究以不考慮時間序列因素的中文文字情緒分析套件SnowNLP與類神經網路、考慮時間序列因素的循環神經網路、長短期記憶循環神經網路及專注機制循環神經網路分別分析文字情緒，並比較其效能。 分析結果顯示，考慮時間序列因素分析文字情緒的效果較好；長短期記憶單元與專注機制皆能有效改善傳統循環神經網路在長序列任務的效果。本研究最後選擇專注長短期記憶循環神經網路為文字情緒分類器，其於正面情緒的分辨率達97%，負面情緒達87%。並利用其分析流程，架設一個分析伺服器，將其模組化，以便於整合至學校的系統中。

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.