과학기술인의 형식 및 무형식학습 유형에 따른 학습접근 차이 분석

This study aimed to elucidate the effects of science and technology professionals learning approaches on formal and informal learning. In accordance with the research objectives, data were collected from 340 participants engaged in R&D pre-cycle education at the National Science and Technology Human Resources Development Institute. The collected data were analyzed using R software, employing descriptive statistics, difference analysis, and Latent Profile Analysis(LPA) methods. The results revealed five latent groups based on learning and informal learning patterns, named as Informal Learning Non-Preferers, Uniform Learners with Short-term Memory, Learning Burnout Individuals, Independent Learning Planners, and Transfer Planning Formalists. No significant differences were found in the gender, age, and job Cluster characteristics across learning pattern Clusters; however, job Cluster exhibited statistically significant differences. Lastly, differences in the level of learning approach were observed depending on the learning and informal learning Clusters. Specifically, Transfer Planning Formalists had higher average deep learning approach levels compared to all other groups, while Independent Learning Planners and Uniform Learners with Short-term Memory groups exhibited higher average deep learning approach levels than Informal Learning Non-Preferers and Learning Burnout Individuals. The study discussed the importance of understanding and comparing learning patterns among science and technology professionals, emphasizing the need for a mixed-method approach combining variable-centered and person-centered analyses. Based on the research findings and conclusions, implications for talent development in science and technology, as well as insights for researchers studying adult learning, were provided.