Analysis Differences in Academic Persistence Intention and Academic Burnout Based on Psychological Characteristics Clusters of Adult Learners in University Lifelong Education System Departments
본 연구에서는 대학 평생교육체제 사업에 참여하는 성인학습자의 학업중단 현상을 방지하기 위한 토 대를 마련하고자 성인학습자를 심리적 특성별로 집단을 분류하고, 그에 따른 학업지속의향 및 소진의 차 이가 있는지를 확인하였다. 이를 위해 지방소재 A 대학 성인학습자를 대상으로 설문을 진행하였고, 그 결과 108명의 데이터를 수집․분석하였다. 성인학습자의 심리적 특성(대학생활적응, 사회적지지, 학업열 의, 학업목표)으로 집단을 분류하는 군집분석을 실시하였고, 그후 이를 독립변인, 학업지속의향과 학업소 진을 종속변으로 두어 일원분산분석을 진행하였다. 그 결과 고위험집단, 위험잠재집단, 성장가능집단, 고 성장가능집단으로 4개의 군집으로 분류되었다. 그리고 이 군집별 학업지속의향, 학업소진의 차이분석 결과 통계적으로 유의하게 나타났다. 이러한 결과를 바탕으로 성인학습자 개인적 특성에 따른 군집에 따른 맞춤형 지원이 필요하다는 결론을 내렸고, 개인적 특성에 따른 군집유형을 좀 더 다각화하고 위계적으로 분류하며 맞춤형 밀착지원이 필요하다고 제언하였다.
This study aimed to establish a foundation for preventing academic discontinuation among adult learners participating in lifelong education programs. To achieve this, we classified adult learners into groups based on psychlogical characteristics and examined whether there were differences in their intentions for academic persistence and burnout. For this purpose, a survey was conducted targeting adult learners at University A located in the local area. The data of 108 participants were collected and analyzed. Using adult learners' psychlogical characteristics (adaptation to university life, social support, academic enthusiasm, academic goals) as independent variables, and intentions for academic persistence and academic exhaustion as dependent variables, cluster analysis, and one-way ANOVA were performed. As a result, four clusters were identified: the high-risk group, latent risk group, growth potential group, and high-growth potential group. The analysis of differences in intentions for academic persistence and academic exhaustion by cluster showed statistically significant results. Based on these findings, it was concluded that tailored support according to the clusters based on adult learners' individual characteristics is necessary. Furthermore, it was suggested that cluster types based on individual characteristics should be classified diversely and hierarchically, emphasizing the need for personalized and close support.