Abstract
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.
Highlights
Modern online education [1] is a new form of education with the development of modern information technology
In order to solve some negative effects that may be produced by subjective methods, this paper proposes a video learning effect evaluation scheme based on EEG signal. (a) Firstly, the EEG signals of the subjects in the process of mental workload test are extracted. en, the linear and Wireless Communications and Mobile Computing nonlinear characteristics of the EEG signals are extracted by discrete wavelet transform and fuzzy entropy, respectively
A total of six machine learning algorithms are applied to the mental load test and the concentration test, respectively, and the optimal machine learning algorithm model is selected by comparing the determination coefficient
Summary
Modern online education [1] is a new form of education with the development of modern information technology. By March 2020, the number of online learning users in China reached 423 million, an increase of 222 million compared with the end of 2019, accounting for 46.8% of all Internet users [2]. 4.58 million students from 24,000 schools have remained in their homes and practiced online education [3]. Among the many online education platforms, how to objectively and impartially evaluate the teaching videos in these platforms and choose the videos most suitable for learning has become one of the most important problems for many users. Ese comments mostly contain subjective comments of specific users, and it is very easy to see the phenomenon of favorable or negative comments by the online water army Users mainly judge the quality of teaching videos based on comments and scores. ese comments mostly contain subjective comments of specific users, and it is very easy to see the phenomenon of favorable or negative comments by the online water army
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