Smart Teaching Design Mode based on Machine Learning and its Effect Evaluation

Abstract

With the continuous progress of science and technology, the mode of smart teaching is more and more applied in the actual teaching process. Under the intelligent teaching system mode, teachers and students can jointly complete the construction of curriculum resources and classroom teaching tasks. This paper evaluates its application effect by introducing two examples of smart teaching. Among them, the evaluation effect of the academic teaching network system shows that the teaching network system based on machine learning is composed of three parts: model selection, data preparation, and modeling prediction. Moreover, it can greatly improve the prediction performance of the intelligent algorithm in the teaching mode by introducing the oversampling technology SMOTE algorithm (Synthetic Minority Oversampling Technique). To further verify the advantages of the smart teaching model, an example of social sciences teaching is introduced. The results show that the relevant predictive performance indicators in the computer engineering discipline are improved by using a combination of intelligent algorithms.