Research on the Construction and Application of Piano Teaching Evaluation System Based on Performance Ability Development
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Abstract
Piano performance education increasingly requires objective and dynamic evaluation mechanisms capable of tracking students’ long-term skill development. To address the limitations of conventional assessment approaches characterized by excessive subjectivity and insufficient process monitoring, this study proposes a performance-ability-oriented piano teaching evaluation system. An evaluation framework comprising three dimensions—basic skills, performance skills, and creative skills— is established using the analytic hierarchy process and fuzzy neural networks. Entropy-weighting is further employed to determine indicator importance, while visual recognition and decision-tree techniques are integrated for intelligent assessment. A 16-week teaching experiment involving 42 university students demonstrates that the proposed framework achieves an evaluation accuracy of 84.86% and maintains a high correlation (0.892) with expert assessments. Students receiving developmental evaluation exhibited significantly greater improvement in overall performance ability than those in conventional teaching environments. The proposed system provides a data-driven pathway for intelligent music education and offers methodological references for multimodal signal analysis, pattern recognition, and intelligent assessment systems.
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