Construction and Empirical Study of a Modularized Teaching System for Art Courses Based on a Unified Training Pathway

To address inefficiencies caused by fragmented content delivery, inconsistent instructional pacing, and subjective evaluation in foundational art education, this study proposes a computer-assisted modular teaching system based on a unified training pathway. The system incorporates a digital platform structured on a browser–server (B/S) architecture, featuring modules for content scheduling, demonstration standardization, progress tracking, and evaluation automation. The five instructional stages—Imitation Reinforcement, Geometric Structure, Basic Still Life, Complex Composition, and Figure Drawing—are encoded as structured task units. A lightweight image analysis algorithm based on OpenCV extracts visual features (e.g., contour continuity, spatial alignment) from student work, generating quantitative indicators that support semi-automated evaluation and reinforce instructional decision-making. A 16-week teaching experiment involving 86 foundational-level students was conducted using this platform. Four metrics—teaching progress consistency, module achievement rate, modeling accuracy, and teaching effectiveness dispersion—were used to assess outcomes. Compared to traditional instruction, the system achieved a 24.8% reduction in progression deviation, a 14.6% increase in achievement rate, and a 43.3% reduction in inter-class variance (p < 0.05). This research demonstrates the viability of integrating computational control mechanisms and visual analysis tools into art instruction, enhancing the process stability and reproducibility of modular teaching in exam-oriented contexts.