Computational Mapping of the Literature on Self-Directed Learning: Evolution, Trends and Research Hotspots

Abstract

Self-directed learning (SDL) skills enable an individual to be self-sufficient and capable of continuing the learning process irrespective of social and contextual changes. SDL helps navigate our learning by fixing learning goals, identifying learning resources, and choosing appropriate strategies to achieve the goal. Considering the uncertainty in social and contextual conditions in the modern world, it is essential for every human being. The SDL skills prepare individuals to face real-life situations that require pragmatic solutions. This study presents a comprehensive Scientometric mapping of academic research done in SDL. The study extracted bibliographic records of 2056 articles on SDL from the Scopus and Web of Science databases. Initially, study focused on the analysis of growth and forecast growth of publications and citation, followed by the trending topics, top contributing authors, organizations/institutions, countries, sources of publications and most influential articles have been identified. Moreover, study analysed the authorship collaboration and international collaboration pattern of the research publications on SDL. It is found that SDL research is increasing exponentially as an emerging topic, and a major amount of scholarly work is done in nursing and medical practices. The analysis also reveals that 69.36% of SDL-related research work has originated from China and USA. The understanding gained from the analysis will benefit young researchers, policymakers, educationists, and teachers to re-design the education practices for improved and quality education. The findings provide a robust understanding of the research hotspots on SDL and a roadmap for further research in this field.