Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

Abstract

As student supervisor workloads increase, ensuring efficient support is challenging. A novel machine learning method using clustering analysis is proposed to gain insights into time allocation and identify optimization opportunities. Specifically, a covariance distance measure analysis approach is presented for characterizing the closeness of data items in multi-dimensional cluster analysis through efficient distance measuring. The membership degree of each sample point is first determined concerning class characteristics through the fuzzy c-means approach, which involves first assigning weights to data items. Then, the membership degree is used to determine the degree of dissimilarity between the samples. Finally, the covariance distance measurement between sample points is used as the second optimization criterion to distinguish between data types, and the optimal solution is calculated iteratively by alternating fixed variables to optimize the clustering metric and the distance measurement learning parameters and produce more accurate clustering results. The experimental findings on datasets demonstrate that the proposed approach outperforms benchmarks in terms of clustering accuracy. Therefore, the high clustering accuracy empowers the student supervisor to make informed decisions about time allocation, allowing them to optimize their efforts and resources.