英文摘要 |
The universities in Taiwan have fully implemented the teaching evaluation for institution research and teaching quality. However, there are too many qualitative data in teaching evaluation from students, the administration and teachers must spend a lot of time and effort on reading each comment to understand students' opinions. It is hard to efficiently explore these opinions and get feedback for teaching. In order to solve this problem, this study adopted the text-mining technology to analyze the teaching evaluation data. Based on the teaching evaluation data of the compulsory courses of Information Management Department, Chung Yuan Christian University from the academic year 2014 to 2017, we use term-frequency method, word-cloud presentation, and sentiment-analysis to analyze the data of 5 types courses (including management, technical, practical, theoretical and business courses) and low evaluation-score courses to understand the critical teaching elements in these courses. The main results are as follows: (1) Teachers can understand the teaching elements which students want and care by the text-mining methodology applied to the qualitative analysis in teaching evaluation; (2) Students focus on different teaching elements by different features of course; (3) Teachers with low evaluation score must change their teaching method according students' opinions. According the findings of this study, the administration and teachers can effectively understand students opinions about the courses and improve teachers' teaching quality and students' learning performance. |