인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study tracked a cohort of 496 undergraduates identified as “potential dropouts” from March 2021 to June 2025 to examine when dropout risk peaks and which factors influence the timing of departure. Using administrative records, we analyzed survival by gender, counseling type (in-person vs. online), their interaction, and risk-management level (Guidance, Caution, Intensive, Critical). Survival functions were estimated with the Kaplan–Meier method, group differences assessed via Log-rank tests, and students remaining enrolled at study end were treated as right-censored observations. Results showed that males had significantly lower survival than females (χ²=14.171, p<.001), with survival at 1,440 days of ~82% for males and 93% for females. In-person counseling was associated with higher survival than online counseling (χ²=18.241, p<.001). The gender–counseling interaction was notable: male students receiving only online counseling had the lowest survival (76.4% at 1,440 days), while female students receiving in-person counseling had the highest (98.4%). Survival rates also declined steadily across higher risk-management levels (χ²=46.466, df=3, p<.001). Hazard analysis indicated a concentrated risk period around the transition into the third academic year. These findings highlight the need to focus preventive efforts on male students—particularly those limited to online counseling—by providing in-person sessions and integrating academic/life coaching before third-year entry, while initiating earlier, tiered interventions for students in Intensive or Critical categories. The study contributes longitudinal, event-time evidence on dropout risk patterns, although limitations include the single-institution scope and reliance on administrative indicators. Future research should incorporate time-varying factors (e.g., military service, job search) and multi-institutional longitudinal datasets.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094325376