Earticle

본 연구의 목적은 E병원에 입원한 정신과 환자들이 만든 노래 70곡의 가사 분석을 통해 그들의 필 요를 파악하고, 임상 현장에서 노래 만들기의 효과적 적용을 위한 아이디어를 제공하기 위함이다. 가사분석은 Roberts와 McFerran(2013)이 제시한 혼합된 내용 분석방법(mixed methods content)으 로 귀납적 방법과 연역적 방법을 사용하였다. 귀납적 분석 방법에 따라 도출된 총 30개의 코드 (codes)는 공통된 의미를 가지고 있는 ‘감정’, ‘북돋아줌’, ‘감각’, ‘바램’, ‘변화’, ‘삶의 태도’, ‘여 행’, ‘인식’, ‘병식’, ‘관계’, ‘기억’, ‘장소’, ‘사람’, ‘음악’, ‘음식/약물/알코올’, ‘자연’, ‘지지적 환경 을 제공하는 것’의 17개 코드(labelled codes)로 요약되었다. 요약된 17개의 코드는 이후 ‘감정/느 낌’, ‘삶’, ‘인식’, ‘관계’, ‘기억’, ‘장소’, ‘사람’, ‘사건/사물(thing)’의 8개 카테고리(category)로 범 주화되었으며, 최종적으로 ‘내적 자원’, ‘외적 자원’이라는 2개의 주제(theme)로 분류되었다. 8개 각 각의 카테고리(category)에 해당되는 내용의 가사를 연역적 방법에 따라 분석한 연구결과는 다음과 같다. 가사의 내용에서 가장 많이 다루고 있는 내용은 ‘감정/느낌’, ‘삶’, ‘인식’, ‘사건/사물’, ‘사 람’, ‘장소’, ‘관계’, ‘기억’ 순으로 나타났다. 연구 결과 정신과 환자들은 노래 만들기에서 가사 만 들기 방법을 통해 ‘자원화(resourcing)’라는 심리, 정서적 지원과 관련된 치료적 경험을 하고 있다는 것을 알 수 있었다.

The purpose of this study is to identify their needs through the lyrics analysis of 70 songs written by psychiatric patients in E hospital, and to provide ideas for effective application of song writing in the clinical setting. The analysis was a mixed content analysis method proposed by Roberts and McFerran(2013) using inductive and deductive methods. The total of 30 codes were identified through the inductive analysis, which were then summarized into 17 labelled codes – ‘feeling’, ‘encouragement’, ‘sense’, ‘wish & want’, ‘change’, ‘attitude of life’, ‘journey’, ‘awareness’, 'insight into disease’, ‘relationship’, ‘memory’, ‘place’, ‘person’, ‘music’, ‘food/medicine/alcohol’, ‘nature’, ‘supportive environment’. The 17 labelled codes were identified to fall into 8 categories that include ‘emotion/feeling’, ‘life’, ‘awareness’, ‘relationship’, ‘memory’, ‘place’, ‘person’, ‘event/thing’, Finally, these categories were divided into 2 themes - ‘internal resource’ and, ‘external resource’. Lyric analysis of each of the eight categories using the deductive method revealed that the most frequently covered theme in the lyrics was ‘emotion/feeling’, followed by ‘life’, ‘awareness’, ‘relationship’, ‘memory’, ‘place’, ‘person’, and ‘event/thing’. This study suggests that psychiatric inpatients benefit from lyric writing as they have therapeutic experience called ‘resourcing’ which provides them with psycho-social support.

· 연구 주제: 본 연구는 AI 기반 고등교육 환경에서 대학 교수자의 디지털 역량이 시기별 변화 과정과 구조적 재구성 양상 속에서 어떻게 진화해 왔는지를 분석하고, 역량의 구성 요인과 기능적 작동 구조를 탐색하는 것을 목적으로 한다. · 연구 배경: 기존 연구는 초·중등 교사 중심이거나 ICT 도구 활용 능력에 초점을 두어, AI 기반 고등교육 맥락에서 교수자의 통합적 역량 구조를 충분히 설명하지 못하는 한계를 지닌다. · 선행연구와의 차이점: 이에 본 연구는 체계적 문헌고찰과 내용분석을 결합한 혼합적 방법론을 적용하여, 역량 요소 간 상대적 강조 비중과 시기별 변화 추이, 그리고 교수활동의 구조적 흐름을 통합적으로 분석하였다. · 연구방법: 수집된 문헌은 PRISMA 절차에 따라 최종 97편으로 선정되었으며, 이해·설계·실행·분석·성찰·확장의 6개 역량 범주를 코딩 체계로 적용한 내용분석을 병행하였다. · 연구결과: 연구 결과, 디지털 역량은 ICT 활용 중심에서 AI 활용을 거쳐 데이터 기반 교수전문성 중심으로 단계적으로 확장되었으며, 여섯 기능 영역이 순환적으로 연결된 작동 구조로 나타났다. · 공헌점 및 기대효과: 본 연구는 대학 교수자의 디지털 역량이 기능적 흐름과 순환적 구조 속에서 재구성됨을 실증적으로 규명함으로써, AI 기반 고등교육에서의 교수역량 개념을 확장하고 이론적·방법론적 기여를 제공한다.

· Research topics: This study aims to analyze how university instructors’ digital competence has evolved and been restructured within AI-based higher education environments, and to explore its core components and functional operating structure. · Research background: Recent advances in generative AI and multimodal learning analytics have significantly transformed teaching and learning in higher education. However, prior studies have largely focused on K?12 teachers or on ICT tool usage, failing to capture the integrated competence structure required in AI-driven higher education. Although recent research highlights a shift in instructors’ roles from technology users to data-informed decision-makers, systematic analyses of this transformation remain limited. · Differences from prior research: Unlike previous studies that present digital competence as fragmented components or framework-dependent lists, this study integrates a systematic literature review and content analysis to identify the relative emphasis of competence elements and their temporal evolution. It further conceptualizes digital competence as a structurally interconnected and cyclical system embedded in teaching practices. · Research method: A mixed-method approach was employed. A total of 97 studies published between 2015 and 2026 were selected using the PRISMA procedure. Content analysis was conducted based on six competence domains?understanding, design, implementation, analysis, reflection, and expansion?derived from DigCompEdu, AI-TPACK, and UNESCO’s AI competency framework. Frequencies and patterns were analyzed to identify structural characteristics and temporal changes. · Research results: The findings indicate that digital competence has evolved through three stages: ICT-centered competence (2015?2018, 19.6%), AI-centered competence (2019?2021, 26.8%), and data-driven teaching expertise (2022?2026, 53.6%). Digital competence consists of six functional domains, with the “analysis” domain (20.6%) showing the highest proportion. The results also reveal that these domains operate as a cyclical system?understanding, design, implementation, analysis, reflection, and expansion?reflecting the dynamic nature of AI-based teaching practices. · Contribution points and expected effects: This study contributes theoretically by reconceptualizing digital competence as a dynamic and cyclical system centered on data-informed teaching. Methodologically, it demonstrates the value of combining systematic literature review and content analysis for identifying structural patterns in competence research. Practically, the findings provide a foundation for developing competence assessment tools, designing faculty development programs, and establishing AI-based teaching support systems in higher education.