The application of information and communications technology (ICT) in higher educational institutions has led to the transformation of the environment from digital to smart. The assessment of smart learning environments will highlight the advantages and disadvantages of its construction results and help establish a sustainable space for students’ personalized study. However, the construction level and application effect are difficult to judge thoroughly, so a comprehensive evaluation method is needed. By rethinking the structure of a smart learning environment (the physical space, resource space and social space), this paper uses analytic hierarchy process–fuzzy comprehensive evaluation (AHP-FCE) and genetic algorithm–back propagation (GA-BP) neural network algorithm methods to assess the environment, with the aim of determining their scope of application and providing suggestions for updating the environment construction process. The questionnaire results of 300 students at Central China Normal University (CCNU) were analyzed with an evaluation index system that was collected by expert scoring. The results showed that the AHP-FCE model can simultaneously obtain multiple results but can be influenced by subjective factors, whereas the GA-BP-based model can make the evaluation process easier and improve fault tolerance. The results also indicated that the classrooms need to be modified in terms of the perception infrastructure and resource modules to provide students with a more suitable and comfortable learning space. We hope the study can provide a reference and inspiration for the construction and assessment of smart learning environments.

By evaluating learners' perceived preferences for the learning environment, we can understand the important characteristics and better improve the learning environment, ultimately to provide great potential for the optimization of teaching practice. Seeing that the current research pays less attention to teachers' and students' preferences for the space environment simultaneously, based on the survey of 1937 undergraduates and 107 teachers from a university in central China, this study aims to explore their preferences for smart learning environment. Based on the ecological theory and research results of the existing learning environment, this paper constructed an ecological model and a conceptual model of learning space preferences. An empirical study was conducted to explore the impact of sociodemographic variables on personal spatial preference. The results showed that teachers and students had a positive attitude towards the smart learning environment, and gender, age, grade, subject category and other variables had limited impact on spatial preference.

Known as the third revolution of information technology, the Internet of Things (IoT) embodies the transformation of human technology from “virtual” to “reality”. The application of IoT in education has risen the concerns of both researchers and practitioners. However, there are few research on using bibliometric to visually analyze the hotspots and trends of the IoT in education. In this study, a total of 2257 articles, including (1) 1243 domestic articles from 2005 to 2021; (2) 1014 foreign articles from 2005 to 2021, were collected for comparative analysis between China and foreign countries using the visualization software CiteSpace. The results show that authoritative journals both at home and abroad have paid varying degrees of attentions to the basic theories, emerging technologies and applications of IoT in education. The number of research literatures on this topic is generally increasing year by year. It is particularly worth mentioning that in the field of the IoT in education, on the one hand, the United States, China and Spain are the top three major countries actively participate in, and their international academic cooperation is relatively close; on the other hand, the main research findings are concentrated in the northern and eastern hemispheres, while there is less distributed in the southern and western hemispheres. Regrading research directions, on the whole, much attention has been paid to the construction of smart learning environment; to be specific, its research focus has shifted to the deep integration of intelligent technology and education (e.g., learning feedback supported by IoT technology is delivered to student with the purpose of improving teaching effect). However, in general, the application of IoT in education is still in its infancy, and the scope of practical application is relatively limited. Therefore, future research will concentrate on construction of smart education environment, curriculum teaching and data-driven education evaluation, in order to inaugurate a new situation capable of carrying out experimental teaching, presenting abstract concepts and connecting realistic situations.

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

The performance of a facial expression recognition network degrades obviously under situations of uneven illumination or partial occluded face as it is quite difficult to pinpoint the attention hotspots on the dynamically changing regions (e.g., eyes, nose, and mouth) as precisely as possible. To address the above issue, by a hybrid of the attention mechanism and pyramid feature, this paper proposes a cascade attention-based facial expression recognition network on the basis of a combination of (i) local spatial feature, (ii) multi-scale-stereoscopic spatial context feature (extracted from the 3-scale pyramid feature), and (iii) temporal feature. Experiments on the CK+, Oulu-CASIA, and RAF-DB datasets obtained recognition accuracy rates of 99.23%, 89.29%, and 86.80%, respectively. It demonstrates that the proposed method outperforms the state-of-the-art methods in both the experimental and natural environment.