Research on Interdisciplinary Teaching Evaluation Model Based on Machine Learning

Abstract

The true validity of the results of the evaluation of the quality of teaching depends on scientifically feasible, reasonable and reliable evaluation methods. In order to improve the accuracy and reliability of interdisciplinary teaching evaluation, we conduct teaching evaluation based on the understanding of "interdisciplinary". A combined nuclear function is constructed on the principle of machine learning algorithm, and a parametric optimization method is established to improve the algorithm. A new model is constructed for interdisciplinary teaching quality evaluation on the new machine learning algorithm RVM (Relevance Vector Machine), and it can be analyzed from the application of interdisciplinary teaching evaluation system. The experimental results show that the machine learning-based interdisciplinary teaching evaluation RVM model has high accuracy and good reliability.