Ho Chi Minh’s thought on people’s security represents a profound synthesis of Marxist–Leninist theory and Vietnamese revolutionary practice. From a philosophical and political–military perspective, it reflects a dialectical understanding of the relationship between the people, the state, and national defense. Ho Chi Minh emphasized that genuine security originates from the people, is maintained by the people, and serves the people. This concept transcends the traditional notion of state security by integrating moral, political, and social dimensions into a unified system of people-based defense. The study clarifies the ideological foundations of Ho Chi Minh’s thought, including its roots in dialectical materialism, collective strength, and the unity of security and development. In contemporary times, his vision offers enduring relevance for safeguarding national independence, strengthening political stability, and addressing non-traditional security challenges. The paper concludes that Ho Chi Minh’s philosophy of people’s security continues to serve as a theoretical and practical framework for Vietnam’s comprehensive approach to defense and social order.

Applied Regression Analysis is a core course in Master of Applied Statistics programs. It consolidates students' statistical modeling foundations and develops their ability to analyze real-world data. Yet conventional course evaluation relies on final exams, lab reports, and project scores--emphasizing results over process and technique over problem-solving. Instructors struggle to identify where students struggle: data governance, model construction, diagnostics, communication. This design article proposes a dynamic evaluation system that uses a regression modeling competency map and process-oriented assessment to capture evidence from quizzes, code submissions, model outputs, case reports, presentations, and online behavior. The system converts this evidence into actionable feedback: problem localization, diagnostic attribution, and modeling prescription. Supported by automated code analysis, model diagnostics extraction, text analysis, and AI-assisted feedback, it is designed to evaluate students' modeling competencies throughout the full regression workflow. The system is intended to improve evaluation timeliness, specificity, and interpretability; support instructors in evidence-based teaching adjustments; and help students refine their modeling strategies.