Augmented reality (AR) has been widely used in higher education because of its unique characteristics that combine virtuality and reality. This study systematically reviewed the literature on the application of AR in higher education between 2000 and 2023. A total of 237 articles were selected, and the key research findings were analyzed and synthesized based on the coding manual from five aspects: basic information, disciplines, technology features, instructional design, and research results. The results revealed interesting findings regarding AR literature in terms of publication trends and applied disciplines, technical features and affordances, instructional design, and learning outcomes and measurement methods. Furthermore, a meta-analysis was conducted on 60 experimental studies selected from the literature to examine the overall effectiveness of AR-based instructions. The results indicate that AR applications in higher education tend to have a large overall effect size (g = 0.896, 95% confidence interval = [0.685–1.107], p = 0.000), with two significant moderators: instructional function and learning outcomes. Additional sensitivity and publication bias analyses confirmed the robustness of the meta-analytic findings. Based on these results, four implications for educational practice and research investigating AR-supported higher education were proposed and discussed.

Facial expression recognition has wide application prospects in many occasions. Due to the complexity and variability of facial expressions, facial expression recognition has become a very challenging research topic. This paper proposes a Vision Transformer expression recognition method based on hybrid local attention (HLA-ViT). The network adopts a dual-stream structure. One stream extracts the hybrid local features and the other stream extracts the global contextual features. These two streams constitute a global–local fusion attention. The hybrid local attention module is proposed to enhance the network’s robustness to face occlusion and head pose variations. The convolutional neural network is combined with the hybrid local attention module to obtain feature maps with local prominent information. Robust features are then captured by the ViT from the global perspective of the visual sequence context. Finally, the decision-level fusion mechanism fuses the expression features with local prominent information, adding complementary information to enhance the network’s recognition performance and robustness against interference factors such as occlusion and head posture changes in natural scenes. Extensive experiments demonstrate that our HLA-ViT network achieves an excellent performance with 90.45% on RAF-DB, 90.13% on FERPlus, and 65.07% on AffectNet.

Predicting student academic success is a significant task in the field of educational data analysis, offering insights for personalized learning interventions. However, the existing research faces challenges such as imbalanced datasets, inefficient feature transformation methods, and limited exploration data integration. This research introduces an innovative method for predicting student performance by transforming one-dimensional student online learning behavior data into two-dimensional images using four distinct text-to-image encoding methods: Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF). We evaluated the transformed images using CNN and FCN individually as well as an ensemble network, EnCF. Additionally, traditional machine learning methods, such as Random Forest, Naive Bayes, AdaBoost, Decision Tree, SVM, Logistic Regression, Extra Trees, K-Nearest Neighbors, Gradient Boosting, and Stochastic Gradient Descent, were employed on the raw, untransformed data with the SMOTE method for comparison. The experimental results demonstrated that the Recurrence Plot (RP) method outperformed other transformation techniques when using CNN and achieved the highest classification accuracy of 0.9528 under the EnCF ensemble framework. Furthermore, the deep learning approaches consistently achieved better results than traditional machine learning, underscoring the advantages of image-based data transformation combined with advanced ensemble learning approaches.

Facial expressions help individuals convey their emotions. In recent years, thanks to the development of computer vision technology, facial expression recognition (FER) has become a research hotspot and made remarkable progress. However, human faces in real-world environments are affected by various unfavorable factors, such as facial occlusion and head pose changes, which are seldom encountered in controlled laboratory settings. These factors often lead to a reduction in expression recognition accuracy. Inspired by the recent success of transformers in many computer vision tasks, we propose a model called the fine-tuned channel–spatial attention transformer (FT-CSAT) to improve the accuracy of recognition of FER in the wild. FT-CSAT consists of two crucial components: channel–spatial attention module and fine-tuning module. In the channel–spatial attention module, the feature map is input into the channel attention module and the spatial attention module sequentially. The final output feature map will effectively incorporate both channel information and spatial information. Consequently, the network becomes adept at focusing on relevant and meaningful features associated with facial expressions. To further improve the model’s performance while controlling the number of excessive parameters, we employ a fine-tuning method. Extensive experimental results demonstrate that our FT-CSAT outperforms the state-of-the-art methods on two benchmark datasets: RAF-DB and FERPlus. The achieved recognition accuracy is 88.61% and 89.26%, respectively. Furthermore, to evaluate the robustness of FT-CSAT in the case of facial occlusion and head pose changes, we take tests on Occlusion-RAF-DB and Pose-RAF-DB data sets, and the results also show that the superior recognition performance of the proposed method under such conditions.

The mental health of college students, particularly first-year students, deserves significant attention. As they transition from high school to college, they experience substantial changes not only in their learning behaviors but also in their daily behaviors, which may pose mental health challenges. However, existing studies exploring the factors influencing college students’ mental health still have limitations in the selection of predictor variables and behavior assessment. Building on this, our study adopted a cross-sectional design involving 110 first-year college students in China (M = 17.94 years; 47 males, 63 females) and analyzed the impact of daily behaviors on mental health. We collected daily behavioral data through a designed mini program, and assessed mental health using the GHQ-28. LASSO regression was applied to identify key behavioral predictors, and semi-structured interviews were conducted to further explore the impact of these factors. The results show that sleep quality, planned and implemented physical exercise, average self-evaluation scores, average daily number of planned tasks, and the completion rate of planned tasks have a positive impact on mental health. These findings contribute to enhancing the theoretical understanding of how daily behaviors shape college students’ mental health and offer practical guidance for universities to carry out mental health education.

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.