An Online Teaching Video Evaluation Scheme Based on EEG Signals and Machine Learning

Nowadays, the number of online teaching videos is rising rapidly; how to evaluate the actual effect of these videos objectively and justly is a hot issue. To solve this problem, this paper proposes a video learning effect evaluation scheme based on EEG signals and machine learning, and the k-nearest neighbor regression algorithm is adopted to complete the mental workload test because the determination coefficient of the training set can achieve 1.0 and no other model can achieve this value. Furthermore, the random forest algorithm is employed to complete the concentration test, and the determination coefficient of the training set is 0.978 and that of the test set is 0.929, both better than the existing relevant online learning video evaluation models. Finally, the effect of teaching videos is evaluated based on the learning efficiency of subjects. Through the student satisfaction test, it is found that this scheme can indeed improve students’ satisfaction with watching teaching video, and the increase rate can achieve 85%. This scheme could not only promote teachers to continuously improve their teaching level, but also provide a more reasonable reference for students to choose teaching videos.