토픽모델링 분석을 이용한 학습역량 연구 동향 분석 및 대학 학습지원 방향 모색

Abstract

Objectives Universities have expanded various support to strengthen students' learning capabilities. However, the meaning and interpretation of learning capabilities were diverse and ambiguous, causing uncertainty in deriving directions for setting learning support goals and establishing promotion strategies. Recognizing these problems, this study aims to prepare evidence for establishing a university learning support system by analyzing trends in studies related to learning competency and extracting topics related to learning competency through text mining and network analysis.
 Methods Basic statistical analysis, core word selection, topic extraction, and research trend were analyzed using keyword network analysis and LDA-based topic modeling techniques by collecting information on the titles and abstracts of 621 academic papers on learning competencies.
 Results The keywords related to learning competency were groups, and the results of selecting key words were learning immersion, self-efficacy, self-regulated learning ability, learning community, problem-solving ability, cooperative learning, knowledge, and creativity. As a result of topic modeling, five topics were extracted, and they were named as learning motivation, learning analysis, learning execution, learning relationship, and learning expansion. Next, as a result of analysis of research trends related to learning competency, it was found that research on learning competency has continued to increase since 2010, and research on learning relationships and learning expansion topics has increased since 2015.
 Conclusions This study is meaningful in that it identified the keywords mentioned in current learning-related studies to foster future talent, extracted and presented related topics, and presented basic data on finding effective support directions to improve student learning competency in universities.