대학 강의평가 서술형 문항의 감정분석 및 의미연결망 분석

Sentiment Analysis and Semantic Network Analysis of University Lecture Evaluation Narrative Questionnaire through A University Practice

목적 이 연구의 목적은 대학 서술형 강의평가의 감정분석과 의미연결망 분석을 통해 수업 개선 방향과 시사점을 도출하는 것이다. 방법 분석자료는 A대학교 2020학년도 1학기부터 2023학년도 1학기까지의 서술형 강의평가 데이터 243,900건이며 감정단어를 긍정, 부정, 중립 순으로 분류하여 연도별, 과목별, 단과대학별 서술형 강의평가 내용의 빈도와 비율을 분석하였다. 텍스톰을 통해 N-gram, 클러스팅 분석을 하였고 Ucinet 6을 사용하여 키워드 간의 네트워크 중심성 분석과 시각화를 하였다. 결과 첫째, 연도별, 이수과목별, 단과대학별 서술형 강의평가 문항의 감정분석 추이는 모두 긍정적인 의견이 우세하게 나타났다. 둘째, 강의평가 내용의 키워드 빈도와 N-gram의 분석 결과, 긍정형 평가에서는 ‘수업’과 ‘감사’가, 부정형 서술형 강의평가에서는 ‘수업’, ‘과제’, ‘아쉽다’ 등의 빈도가 높았으며, ‘수업’은 ‘내용’, ‘진행’, ‘시간’, ‘아쉽다’라는 키워드를 중심으로 확장되었다. 셋째, 강의평가 내용의 키워드 간 의미연결망 분석 결과는 긍정형에서는 ‘감사’, ‘수업’, ‘수고’, ‘설명’, ‘학생’, ‘친절’, ‘도움’, ‘이해’, ‘과제’, ‘시간’ 등이 가장 많이 연결되는 중요한 키워드였다. 부정형 강의평가에서는 ‘수업’, ‘강의’, ‘교수’, ‘과제’, ‘아쉽다’, ‘어렵다’, 힘들다‘’시, ‘험’이 포함된 키워드가 가장 많은 연결로 분석되었다. 결론 본 연구는 서술형 강의평가 감정분석과 의미연결망 분석을 통해 수업 개선 방향을 제시할 수 있는 가능성을 보여주었다.

Objectives The purpose of this study was to derive directions and implications for quality of education through sentiment analysis and semantic network analysis of university descriptive course evaluations. Methods The analysis data consisted of 243,900 descriptive lecture evaluations from the first semester of 2020 to the first semester of 2023 at University A. Emotion words were classified in the order of positive, negative, and neutral, and the frequency and ratio of descriptive lecture evaluation contents by year, subject, and college were calculated. N-gram and clustering were analyzed using Textome, and network centrality between keywords was analyzed and visualized using Ucinet 6. Results First, in the sentiment analysis trend for descriptive lecture evaluation questions by year, course com-pleted, and university, positive opinions were found to be dominant. Second, as a result of the keyword frequency and N-gram analysis of the lecture evaluation contents, the frequency of ‘class’ and ‘thank you’ was high in pos-itive evaluations, and the frequency of ‘class’, ‘assignment’, and ‘unsatisfactory’ was high in negative descriptive course evaluations. In addition, ‘class’ was expanded around the keywords ‘content’, ‘progress’, ‘time’, and ‘regrettable’. Third, the results of semantic network analysis between keywords in the lecture evaluation contents showed that in the positive type, ‘gratitude’, ‘class’, ‘hard work’, ‘explanation’, ‘student’, ‘kindness’, ‘help’, ‘understanding’, ‘assignment’, ‘time’ were the most important and connected keywords. In the negative course evaluation, keywords including ‘class’, ‘lecture’, ‘professor’, ‘assignment’, ‘disappointing’, ‘difficult’, ‘anxiety of test’ were analyzed as the most connected. Conclusions This study showed the possibility of suggesting directions for education improvement through de-scriptive lecture evaluation using sentiment analysis and semantic network analysis.