Background: Enhancing the academic performance (AP) of college students can contribute to the overall scientific literacy among this population, thereby fostering societal progress.Objective: The study investigates the correlation between college students’ AP and the socio-educational environment (SEE, including family, roommates, and teachers), study motivation (SM, including self-efficacy and study behaviors). Based on the research findings, recommendations are offered to students, educators, and school administrators.Settings: Utilizing a stratified sampling approach, data was collected by selecting a sample of 330 first-year computer science students from a specific local university in Hebei Province, China.Methods: Data will be collected through a hierarchical sampling method. Using correlation analysis, difference analysis, and structural equation modeling (SEM) as data analysis methods. The data passed reliability and validity analysis (Cronbach’s Alpha = 0.88, KMO = 0.88, χ²/df = 1.49RMSEA = 0.04).Results: The independent sample T-test results showed that female students had higher academic performance than male students (p < 0.001), and there was no significant difference in academic performance between students from single parent or orphan families and students from normal families (p = 0.14), from non urban areas and from urban areas (p = 0.67). The results of the mediating effect analysis indicate that SM exerts complete mediation in the association between SEE and AP, with a mediating effect value of 0.18.Conclusion: The educational disparity between urban and rural areas in China is gradually narrowing. Support policies for students from impoverished families in higher education institutions are showing initial effectiveness. The conducive learning environment and educational atmosphere for students can indirectly influence their psychological state, thus impacting their academic performance during their university years.

A novel symmetric parallel mechanism 6RPS with multi-redundant actuations is proposed in this paper, which possesses two rotational and one translational degrees-of-freedom. Motion and constraint screw systems of this mechanism are established and then character of the freedom is analyzed. Transmission performance indexes are established, and dimension optimization is operated by considering the distribution of transmission performance. Whole high-performance workspace and performance distribution are analyzed with optimal dimension. Motion interference condition of the mechanism is studied, and accessible posture spaces are analyzed by combining joint space restriction. A comprehensive performance comparison with a counterpart configuration is exhibited. Research results of this paper are of reference significance for both the research of parallel mechanism with two rotational and one translational degrees-of-freedom, and the application of mechanism 6RPS.

This paper focuses on the singularity of actuation coordination of redundantly actuated parallel manipulators and proposes a series of novel indices to evaluate the actuation coordination. The singularity is analyzed based on the coordinated dynamics model and divided into two categories: input singularity and output singularity. Influences of the singularity on the control performance of redundantly actuated parallel manipulators are studied with mechanisms 2RPU + 2UPR + RPR and 3RPR. Local and global performance indices of redundantly actuated parallel manipulators with actuation coordination are put forward, and the dimension optimization method considering the proposed indices is studied based on mechanism 6RPS. The research of this article possesses good reference significance for the performance analysis and control application of redundantly actuated parallel manipulators.

Traditional classroom teaching evaluations often rely on subjective feedback and post hoc academic metrics, which limits their objectivity and timeliness. To address these limitations, this study proposes YOLOv11_BWC, a real-time object detection framework for student behavior detection, classroom attention assessment, and teaching quality evaluation in intelligent education. YOLOv11_BWC integrates a BiFormer module to improve occlusion robustness in complex classroom scenes, a Wise-IoU v3 loss to enhance multi-scale localization, and a lightweight Shuffle Attention mechanism to improve computational efficiency. Using real classroom videos from 257 students, three posture-related behaviors, namely head-up, head-down, and sprawled posture, were annotated, augmented from 840 to 1,326 images, and used for model training and evaluation. On the classroom behavior dataset, YOLOv11_BWC achieved 0.9672 mAP@0.5, 0.7594 mAP@0.5:0.95, 0.931 recall, and 136 FPS, outperforming YOLO26, YOLOv8, YOLO11, RT-DETR, and YOLOv12 in the overall balance between detection accuracy and real-time performance. Additional Jetson Orin Nano testing and POCO/SCB-Dataset3 validation further supported its embedded deployment efficiency and cross-domain robustness. Heatmap visualizations verified more focused feature responses, and temporal analysis revealed an exploratory classroom attention pattern of high initial attention, mid-class decline, and final resurgence. The framework provides objective, non-invasive, and fine-grained behavioral evidence for instructional feedback and dynamic classroom quality assessment.

The rapid proliferation of generative artificial intelligence (GenAI) in higher education raises critical concerns regarding educational equity. However, t