A study on the teaching model of introducing modern network analysis tools into the electric network theory course

Abstract

The Electric Network Theory course suffers from difficulties in manual derivation of complex networks, a lack of visual support for abstract concepts, and a disconnect between algorithmic logic and theoretical principles. This study proposes a teaching model that systematically introduces modern network analysis tools. Based on the modular characteristics of knowledge, this model establishes a mapping relationship between the algebraic topological expressions of the tools and the theoretical content. It designs a coupling mechanism for tool integration and instruction, constructs an algorithmic path for network equations, a hybrid strategy of symbolic and numerical methods for dynamic circuits, and a visual derivation method for nonlinear networks. The model achieves efficacy optimization through multi-dimensional evaluation indicators, dependency constraint identification, and an iterative model of higher-order thinking. This model aims to achieve a deep integration of tool assistance and theoretical derivation.