通过主题建模探讨大学生志愿服务偏好

Jong Hwa Lee
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Topic modeling was employed, utilizing the latent Dirichlet allocation (LDA) algorithm. To achieve a more nuanced understanding, data preprocessing steps were undertaken, including morphological analysis, elimination of redundant words, and consolidation of synonyms. The analysis revealed eight primary topics: “childcare initiatives,” “emotional and psychological support for youth,” “enhancing civic engagement in environmental matters,” “boosting digital literacy among the elderly,” “support for the disabled,” “support for student learning and career progression,” “stray animal welfare initiatives,” and “promotion of arts and cultural opportunities.” The study also identified distinct volunteering preferences among students majoring in humanities and social sciences, natural sciences and engineering, and arts. 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引用次数: 0

摘要

本研究将以受惠对象、活动方法等志愿服务主要属性为中心进行探索。分析资料使用了京畿道4年制K大学社会服务科目听课生们提交的1284件“社会服务计划书”,分析方法采用了基于潜在的重复分配算法的topik建模。为了更准确地掌握分析资料的内容,经过了形态素分析、消除不用语、统一注意语等数据前处理过程。分析结果,照顾儿童’活动”、“青少年心理·情绪支援活动”、“市民参与对环境议题的促进活动”、“老人数码门增进理解能力”、“残疾人志愿活动”、“学生学习和就业援助活动”、“遗弃动物保护活动”、“艺术和文化机会支持活动”以上8个话题引出了,志愿者偏爱人文,理工系,艺术体育系列之间的差异得到了确认。以研究结果为基础,提出了有关大学志愿服务教育、大学生志愿服务活动有效运营的启示。This study explores the volunteering preferences of university students by identifying key attributes associated with volunteering, such as beneficiaries and methods of participation。The research analyzed 1284 volunteer plans submitted by students enrolled in The social service course at K University in Gyeonggi-do。Topic modeling was employed, utilizing the latent Dirichlet allocation (LDA) algorithm。To achieve a more nuanced understanding, data preprocessing steps were undertaken, including morphological analysis, elimination of redundant words, and consolidation of synonyms。analysis revealed eight primary topicschildcare initiatives, emotional and psychological support for youth, enhancing civic engagement in environmental matters, boosting digital literacy among the elderlysupport for the disabled, student learning and career progression, stray animal welfare initiatives, and promotion of arts and cultural opportunitiesThe study also identified distinct volunteering preferences among students majoring in humanities and social sciences, natural sciences and engineering, and arts。the research provides valuable insights for enhancing university volunteer education and improving the management of student-led volunteer activities。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring University Students’ Volunteering Preferences through Topic Modeling
본 연구는 본 연구는 대학생의 자원봉사 선호를 수혜 대상, 활동 방법 등 자원봉사 주요 속성을 중심으로 탐색한다. 분석자료는 경기도 소재 4년제 K 대학의 사회봉사 과목 수강생들이 제출한 ‘사회봉사 계획서’ 1,284건을 활용했고, 분석방법은 잠재 디리클레 할당 알고리즘에 기반한 토픽모델링을 적용했다. 분석자료의 내용을 더 정확히 파악하기 위해 형태소 분석, 불용어 제거, 유의어 통일 등의 데이터 전처리 과정을 거쳤다. 분석 결과, ‘아동 돌봄 활동’, ‘청소년 심리․정서 지원 활동’, ‘환경 이슈에 대한 시민 참여 촉진 활동’, ‘노인 디지털 문해력 증진 활동’, ‘장애인 지원 활동’, ‘학생 학습 및 진로 지원 활동’, ‘유기동물 보호 활동’, ‘예술 및 문화 기회 지원 활동’이상 8개 토픽이 도출되었고, 자원봉사 선호에 있어 인문사회계열, 이공계열, 예체능계열 간 차이가 확인되었다. 연구 결과에 기반해 대학의 자원봉사 교육, 대학생 자원봉사 활동의 효과적 운영에 관한 시사점을 제시했다.This study explores the volunteering preferences of university students by identifying key attributes associated with volunteering, such as beneficiaries and methods of participation. The research analyzed 1,284 volunteer plans submitted by students enrolled in the social service course at K University in Gyeonggi-do. Topic modeling was employed, utilizing the latent Dirichlet allocation (LDA) algorithm. To achieve a more nuanced understanding, data preprocessing steps were undertaken, including morphological analysis, elimination of redundant words, and consolidation of synonyms. The analysis revealed eight primary topics: “childcare initiatives,” “emotional and psychological support for youth,” “enhancing civic engagement in environmental matters,” “boosting digital literacy among the elderly,” “support for the disabled,” “support for student learning and career progression,” “stray animal welfare initiatives,” and “promotion of arts and cultural opportunities.” The study also identified distinct volunteering preferences among students majoring in humanities and social sciences, natural sciences and engineering, and arts. Based on these findings, the research provides valuable insights for enhancing university volunteer education and improving the management of student-led volunteer activities.
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