Multi-dimensional Data Optimal Classification Algorithm for Quality Evaluation of Distance Teaching in Universities.

In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation. [ABSTRACT FROM AUTHOR]