Journal of Electrical Engineering and Automation

In response to the challenges of passive learning and inadequate knowledge internalization prevalent in traditional education, this research has developed and implemented a deep-learning cycle for the course of Electrical Machinery, structured around a “Diagnose-Guide-Autonomize-Reflect” framework. The process begins by analyzing historical learning data to generate a dynamic “Knowledge Misconception Map,” which guides the refinement of instructional materials and the creation of tiered, autonomous assignments. The integration of a “Dual-Track Registration” system and a “Three-Element Self-Correction” mechanism empowers students to conduct in-depth self-assessment and reflective learning. Practice has shown that this paradigm significantly enhances the attainment of the course objectives for Electrical Machines, effectively fosters autonomous learning habits and critical thinking. It facilitates a fundamental shift from merely acquiring knowledge to mastering how to learn, providing a reference for teaching reform in the context of the new era.

Electrical machinery; Learning-centered; Knowledge misconception map; Dual-track registration; Three-element self-correction