Directory » SEUNG WON YOON
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Associate Professor
Dr. Seung Won Yoon is the president of the Academy of Human Resource Development for the term 2024–2026. His research focuses on enhancing both employee and organizational performance by integrating leadership, learning/knowledge sharing, and technologies. He passionately applies frameworks from social capital, network science, and data analytics to achieve these goals. He also served two terms as an associate editor for the Human Resource Development Quarterly journal from 2015 to 2022.

Ph.D., Human Resource Education, University of Illinois at Urbana-Champaign (2003)
M.A., Teaching Eng as a Second Language, University of Illinois at Urbana-Champaign (1998)
B.A., English Language & Literature, Sung Kyun Kwan University (1996)
000. Courses Taught at TAMUC, NIU, WIU
HR/People Analytics
Organizational Leadership
Human Performance Technology
Institutional Effectiveness
Quantitative Research and Statistics I/II
Qualitative Research
Research Methodologies
Designing and Evaluating e-Learning
Instructional Design
618. Evaluation Models in HRD
Applying the Utilization-Focused-Evaluation framework while incorporating major design options (e.g., Logit, CIPP, Complex Adaptive System) for continuous improvement purposes.
627. Research and Development in HRD
EHRD 603. Foundations of HRD
EHRD 605. Principles and Practices of (Organizational) Leadership
Theories of leadership and their relevance to employee development and performance as well as organizational effectiveness, changes, and improvement.
EHRD 689. Social Network Analysis I
Hands-on and experiential learning of whole-network research – designing and analyzing a network using a roster-based survey or name directory (to empirically examine the influence of structure and relational interdependence).
EHRD 689. Social Network Analysis II
Building up on the first course, this second course focuses on how to design and analyze ego-centric networks. Multivariate regression analysis will be revisited, and multi-level analysis (hierarchical linear modeling and growth curve models) will be added to interpreting prediction models.