Evaluation of Potential Correlation of Piano Teaching Using Edge-Enabled Data and Machine Learning

Data science has expanded at an exponential growth with the advancement of big data technology. The data analysis techniques need to delve deeper to find valuable information (Sarac 2017). The notion of edge computing is broadly acknowledged. Edge-enabled solutions provide computing, analysis, storage, and control nearer to the edge of the network, which support the efficient processing and decision-making. Machine learning has also attained significant attention in this context due to its flexibility and its ability to provide a variety of supervised, unsupervised, and semisupervised techniques. This research presents a specific model to evaluate the potential correlation of piano teaching using machine learning. The data analysis is performed at the edges of network for efficient results (Tan et al. 2017). The association rule mining technique of machine learning is utilized with the integration of improved T-test method. The improved T-test is performed for the measurement of association rules and proposed a new measure and influence degree of association rules. It is evident from the results that the use of the degree of influence as a measure of association rules to find the potential relevance of multimedia-assistant piano teaching evaluation data is extremely feasible. It overcomes shortcomings of existing measurement standards and reduces the generation of redundant rules. The existing literature highlights the concepts of evaluation of potential correlation and evaluates the advantages. However, there is a lack of an effective strategy for piano teaching. The proposed model performs efficient calculation and storage. The feasibility and effectiveness of the proposed framework are verified using the analysis of the actual dataset. The verification results show that it is feasible and valuable to find the potential relevance of multimedia-assisted piano teaching evaluation.