Publications
This bibliometric analysis explores the evolving landscape of peer-reviewed research on adult online learning motivation from 2000 to 2022. By analyzing 541 scholarly articles using the R bibliometric package and biblioshiny app, this study identifies key trends, influential publications, and emerging themes in the field. Findings indicate a substantial increase in research output, with the United States leading contributions and Computers & Education as the most active journal. The study highlights the impact of COVID-19 on learning motivation, the role of self-regulated and transformative learning, and the integration of gamification and virtual learning communities. Through performance analysis, scientific mapping, and network analysis, this research provides valuable insights into the drivers of motivation in adult online education and offers a foundation for future scholarly exploration.
This study employs an exploratory cluster analysis to examine adolescent technology use and activities during the COVID-19 pandemic and their relationship to mental health outcomes. Using data from the Oxford Achieving Resilience during COVID-19 (ARC) study, the research identifies three distinct clusters of adolescents based on their media consumption and activity levels. Findings reveal that adolescents with high media use and low activity levels exhibited increased depressive symptoms and lower conscientiousness, highlighting critical links between digital behaviors and mental health. The study underscores the importance of balanced technology use, social support, and structured activities in mitigating the pandemic's psychological impact on adolescents. By utilizing machine learning-driven clustering techniques, this research provides nuanced insights into adolescent well-being during a period of global disruption.
This study employs a machine learning-based text mining approach, Latent Code Identification (LACOID), to analyze the career trajectories of bioscience Ph.D. students over an eight-year period. Using data from the Early Career Research (ECR) project, which tracked 336 students across 53 U.S. research universities, the study explores the impact of self-reflection and socialization on early career development. By analyzing annual interview transcripts, the research identifies key themes such as mentorship, professional identity, and institutional influences. Findings reveal how individual characteristics—including gender, race, and prior research experience—shape perceptions of professional growth. The study provides valuable insights into the factors influencing early career researchers and demonstrates the effectiveness of LACOID in qualitative data analysis.