텍스트 마이닝을 활용한 어린이집교사 온라인 상담의 내용분석

Text Mining Analysis of the Online Counseling Contents of Nursery School Teachers

Objective: This study aimed to analyze the counseling contents of daycare center teachers by using text mining and semantic network analysis methods to find the necessary support directions for daycare teachers and to improve the quality of child-care. Methods: Five hundred thirteen cases of counseling recorded on the open bulletin board of online counseling (Naver Bands for Nursery Teacher Counseling) were collected, and frequency analysis, centrality solidarity analysis, and machine learning-based topic analysis were conducted using the NetMiner4.3 program. Results: First, teacher-to-child ratio was highest in the frequency. Second, colleagues were all high in all centrality analysis. Third, machine learning-based topical analysis shows that the topics were categorized as subjects about childcare and education , working environment that supports professional development and working condition , and among them, first-time teacher concerns accounted for 44% of the total counseling content. Conclusion/Implications: This study implied that it is necessary to provide high-quality child-care and education to infants by lowering the teacher-to-child ratio , and a systematic program is needed to help improve effective communication skills in interpersonal relationships such as between parents, fellow teachers, and principals. In addition, self-development and efforts to improve teachers expertise should be prioritized in order to improve infant care quality and quality of teachers.