Comprehensive evaluation of music teaching effectiveness supported by multimodal machine learning

The three leading indicators of the efficacy of a music education program are student engagement, performance and quality of instruction. Conventional tests rely on mostly subjective observations, subjective grading or unimodal data, which seem to give sufficient, not total or objective results. As the evaluation has demonstrated, this type of data may provide objective evaluation of the interactions between students and their teachers and occasionally incoherent feedback, so that it is hard to evaluate whether or not the supervisors should update or improve their pedagogical practice, no matter the context. Considering these identified shortcomings, the paper introduces a framework, Music Teaching Effectiveness using Multimodal Machine Learning (MuTE-MM) in order to assess the effectiveness of music education in the context of multimodal machine learning. The framework allows to capture various areas of the teaching-learning process based on auditory, visual, and textual streams of data. MuTE-MM results in a more believable and holistic teaching effectiveness evaluation by utilizing sophisticated machine learning methods to combine different modalities. The suggested framework aids both teachers and educators of any size, allowing scaled assessment processes, individualized student feedback, and practical insights into how to enhance teaching. MuTE-MM proposed has a Teaching Accuracy Score (TAS) of 92.4, an Expressive Dynamics Score (EDS) of 0.87, a Student Engagement Index (SEI) of 0.91, and a Feedback Consistency (FC) variance of 0.06, and it outperforms competitive models. Other measures verify strength with MAE 2.1, Multimodal Fusion Effectiveness (MFE) 0.89, Temporal Dependency Capture (TDC) correlation 0.93, and Actionable Feedback Quality (AFQ) 0.90 to provide reliable, interpretable feedback to support effective music education.