With computer networks as the foundation, the new generation of information technologies is accelerating the empowerment of various sectors of society. Industries and enterprises urgently require talents equipped with data-oriented thinking skills, which raises higher demands for computer network education. To address the challenges in teaching-such as students' difficulties in constructing a systematic knowledge framework, insufficient practical abilities, and inadequate professional competencies-this study is based on the positioning of an application-oriented undergraduate institution. Guided by the philosophy of Outcome-Based Education (OBE) and constructivist learning theory, and considering the data-oriented thinking characteristics of the course, this study proposes the teaching concept of “making learning proactive.” It clarifies teaching objectives oriented toward professional competencies and leverages ChatGPT, knowledge graphs, and intelligent agents to reconstruct course content and optimize teaching methods. In addition, online platform resources are established, an evaluation system is constructed, and flipped classroom teaching is implemented. Practical results demonstrate that the flipped classroom approach significantly enhances teaching efficiency. Empowered by digitally and intelligent technologies, students engage in deep learning of computer networks through task-driven and data-oriented thinking teaching strategies, thereby improving their data-oriented thinking ability, practical skills, and application capabilities. © 2025 Copyright held by the owner/author(s).
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