Yanan Zhou’s research while affiliated with Beijing Foreign Studies University and other places

The introduction of competition has the potential to enhance the efficacy of students' learning performance. Nevertheless, there have been contradictory findings about the impact of intergroup competition on students' learning performance and engagement. Therefore, further comprehensive investigations for this problem are necessary. In order to bridge this gap, the present study seeks to ascertain the efficacy of intergroup competition in relation to students' academic performance and motivation. Consequently, we present the concept of intergroup competition and implement it within the context of an online programming course and an online Chinese-English translation course. The participants of this study consist of sophomore students majoring in Computer Science and English. Initially, a total of 108 sophomore students majoring in Computer Science participated. Then, a total of 100 sophomore students majoring in English participated. A quasi-experimental study was subsequently undertaken to compare students from two courses, which are online programming and Chinese-English translation, assigning them to an experimental group and a comparison group, respectively. Then, we conducted independent samples t-tests to measure the difference between the academic performance of the two group of students from two courses. The results indicate that both groups of students who were exposed to the intergroup competition mechanism demonstrated considerably higher levels of academic performance and engagement compared to the other group of students. The findings indicate that the competition mechanism, has the potential to be a beneficial instrument for enhancing both students' learning performance and motivation.

Introduction The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. Methods In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Experiments Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. Discussion The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios.

As a descriptive-inferential study, this research aimed at revealing the relationship between music training and academic development with the Chinese middle school students' academic performance of mathematics and physics skills. The participants of this study consisted of the students from two different middle schools located at two cities in Shandong province, China. From each school 250 students were selected, and the statistics was used to analyze both the academic performance of the students and the data obtained from the scale designed by the authors. The research results show that the non-music students outperformed music students on both mathematics and physics development. In addition, music training did not contribute to the academic achievement independently but rather integrated with several factors like parents’ education and out-of-school engagement. The findings suggest the positive influence on non-musical cognitive learning, and it has potential implications for the Chinese middle school education.

Introduction Emotion plays a vital role in understanding activities and associations. Due to being non-invasive, many experts have employed EEG signals as a reliable technique for emotion recognition. Identifying emotions from multi-channel EEG signals is evolving into a crucial task for diagnosing emotional disorders in neuroscience. One challenge with automated emotion recognition in EEG signals is to extract and select the discriminating features to classify different emotions accurately. Methods In this study, we proposed a novel Transformer model for identifying emotions from multi-channel EEG signals. Note that we directly fed the raw EEG signal into the proposed Transformer, which aims at eliminating the issues caused by the local receptive fields in the convolutional neural networks. The presented deep learning model consists of two separate channels to address the spatial and temporal information in the EEG signals, respectively. Results In the experiments, we first collected the EEG recordings from 20 subjects during listening to music. Experimental results of the proposed approach for binary emotion classification (positive and negative) and ternary emotion classification (positive, negative, and neutral) indicated the accuracy of 97.3 and 97.1%, respectively. We conducted comparison experiments on the same dataset using the proposed method and state-of-the-art techniques. Moreover, we achieved a promising outcome in comparison with these approaches. Discussion Due to the performance of the proposed approach, it can be a potentially valuable instrument for human-computer interface system.

An aurora is a unique geophysical phenomenon with polar characteristics that can be directly observed with the naked eye. It is the most concentrated manifestation of solar–terrestrial physical processes (especially magnetospheric–ionospheric interactions) in polar regions and is also the best window for studying solar storms. Due to the rich morphological information in aurora images, people are paying more and more attention to studying aurora phenomena from the perspective of images. Recently, some machine learning and deep learning methods have been applied to this field and have achieved preliminary results. However, due to the limitations of these learning models, they still need to meet the requirements for the classification and prediction of auroral images regarding recognition accuracy. In order to solve this problem, this study introduces a convolutional neural network transformer solution based on vision transformers. Comparative experiments show that the proposed method can effectively improve the accuracy of aurora image classification, and its performance has exceeded that of state-of-the-art deep learning methods. The experimental results show that the algorithm presented in this study is an effective instrument for classifying auroral images and can provide practical assistance for related research.

Virtual reality and the Internet of Things have shown their capability in a variety of tasks. However, their availability in online learning remains an unresolved issue. To bridge this gap, we propose a virtual reality and Internet of Things-based pipeline for online music learning. The one graph network is used to generate an automated evaluation of learning performance which traditionally was given by the teachers. To be specific, a graph neural network-based algorithm is employed to identify the real-time status of each student within an online class. In the proposed algorithm, the characteristics of each student collected from the multisensors deployed on their bodies are taken as the input feature for each node in the presented graph neural network. With the adoption of convolutional layers and dense layers as well as the similarity between each pair of students, the proposed approach can predict the future circumstance of the entire class. To evaluate the performance of our work, comparison experiments between several state-of-the-art algorithms and the proposed algorithm were conducted. The result from the experiments demonstrated that the graph neural network-based algorithm achieved competitive performance (sensitivity 91.24%, specificity 93.58%, and accuracy 89.79%) over the state-of-the-art.