Earticle

Traditionally male-dominated, forest engineering has seen a notable rise in women’s involvement in both education and the profession. University education is a pivotal phase where students develop professional identities and form perceptions about their future careers. For women students, experiences gained during this phase are significant not only for individual career planning but also for promoting gender equality, enhancing sectoral diversity, and increasing the visibility of women engineers within the profession. However, scientific studies addressing women students’ perceptions of the profession, their reasons for choosing it, and their expectations remain limited. This study aims to examine the perspectives of women undergraduate students in the Department of Forest Engineering at Bursa Technical University, Türkiye, on the profession, their views on acquiring professional competencies, and their perceptions of gender inequality both during their studies and throughout their future careers. Additionally, the study explores male students' perceptions on these issues. A survey form was designed based on literature and expert input, covering four areas: (i) student profiles and choice motivations, (ii) views on the profession and career, (iii) opinions on developing professional skills, and (iv) perceptions of gender inequality in the profession. The survey was conducted with 176 students of different year groups. Descriptive analyses and difference tests were applied to the collected data. Key findings indicate that 15.7% of women students chose the department due to family influence, 71.1% perceive greater challenges for women in the sector, and 54.2% believe women must exert more effort to secure employment.

본 연구는 임산공학 연구자들의 연구데이터 관리 현황 파악을 목적으로 하였다. 연구 목적 달성을 위해 DAF(Data Asset Framework)라는 도구를 이용하여 설문조사를 수행하였다. DAF는 기관이 연구데이터를 어떻게 관리하고 있는지 식별, 위치, 설명 및 평가할 수 있는 수단을 제공하는 조사 도구이다. 이러한 DAF를 활용하여 국립산림과학원 내 임산공학 분야 연구자들을 대상으로 연구데이터 관리 현황을 분석한 결과, 연구데이터 생성 방법 및 유형, 공유, 저장, 보존 그리고 재사용 등과 같은 5가지의 카테고리의 현황 및 문제점이 도출되었으며, 또한 문제점 관련하여 해결책을 제시하였다. 이번 연구는 DAF와 같은 체계적인 도구를 활용한 기초 조사로서 특정 분야의 RDM 시스템 설계 시 연구데이터의 현황 및 문제점을 분석하는데 참고자료가 될 수 있을 것이다.

This study was started with the aim of grasping the current status of research data management of forestry engineering researchers. In order to achieve the research purpose, the survey was conducted using a tool called DAF (Data Asset Framework). DAF is an investigative tool that provides a means to identify, position, describe and evaluate how the agency manages research data. Using this DAF, the research data management status was analyzed for researchers in the field of forestry engineering at the National Institute of Forest Science. As a result of analysis, the current status and problems of the five categories such as the method and type of research data creation, sharing, storage, preservation, and reuse were identified, and solutions were presented in relation to the problems. This study is a basic investigation using a systematic tool such as DAF, and can be used as a reference for analyzing the current status and problems of research data when designing RDM system in a specific field.

본 연구는 공학계열 A대학 대학생들의 생애주기별 학업성취를 예측하고, 이에 영향을 미치는 핵심 변인을 탐색하는 데 목적이 있다. 이를 위하여 공학 중심 대학인 A대학 졸업생 701명의 데이터를 수집하였으며, 머신러닝 알고리즘인 랜덤포레스트와 설명가능한 인공지능 기법인 SHAP기법을 활용하여 분석을 실시하였다. 분석 결과, 첫째, 캠퍼스 행동 데이터가 누적되지 않은 초기 시점(1학년 1학기)에는 ‘1-1학기 출석률’이 학업성취를 예측하는 가장 큰 변인으로 확인되었으며, 입학유형 및 학생부등급과 같은 입학 전 배경 변인과 영어･수학･물리 등의 기초학력이 주요 예측 변인으로 도출되었다. 둘째, 학년이 진급함에 따라 입학 전 배경 변인의 영향력은 급격히 감소하고, 매 학기 누적되는 출석률 및 전공 교과 성적이 성취를 결정짓는 핵심 기제로 재편되었다. 본 연구 결과를 바탕으로 공학계열 학습자의 특성을 고려한 데이터 기반 교수학습 지원 체계 구축 및 실시간 출석 모니터링 관리 방안을 제언하였다.

Purpose: This research aimed to predict the academic achievement of engineering college students across their institutional life cycle and to explore the key predictive variables influencing their performance. Design/methodology/data/approach: For this study, the authors collected behavioral and academic data from 701 graduates of University A, an engineering-oriented specialized institution. Data analysis was conducted by employing the Random Forest machine learning algorithm combined with SHAP, an explainable artificial intelligence technique. Findings/Results: The major findings showed that during the initial phase (1st year, 1st semester) when campus behavioral data had not yet accumulated, the ‘1-1 semester attendance rate' served as the most powerful predictor of academic achievement, alongside pre-entry background variables (e.g., admission type, high school grades) and ‘basic academic skills' in English, Mathematics, and Physics. Second, as students progressed through their academic years, the influence of pre-college background variables diminished rapidly, whereas cumulative attendance rates and major course performance restructured into the primary mechanisms determining academic achievement. Value: Based on these findings, we propose establishing a data-driven teaching and learning support system tailored to the characteristics of engineering learners and implementing real-time attendance monitoring strategies.