An Advanced Computational Model for Data Classification Incorporating Blockchain and Clustering Techniques

This paper proposes an advanced computational model which innovatively combines blockchain and clustering techniques, with a case study in the field of data classification for teacher evaluation. The model was trained and tested with data from private higher education institutions, and the results indicate that after 30 training rounds, it outperforms traditional statistical models and standalone clustering models in terms of classification accuracy. Additionally, the model ensures a more balanced distribution of the classification levels, providing robust and reliable results. While there exist certain limitations in the training process and practical application, this study signifies the potential and efficiency of integrating blockchain and clustering methods in data classification tasks. It offers a novel perspective into the computational possibilities of merging these techniques, hinting at potential applications in various fields requiring data classification, including but not limited to educational technology.