As the informatization process continues to advance, digital natives have become a major component of society. The widespread application of digital technologies in education has transformed their learning styles and knowledge acquisition, leading to a growing phenomenon of technology dependency. Based on grounded theory, this study conducts in-depth interviews with 31 digital natives and implemented three-level coding using NVivo11 to identify the underlying mechanisms that lead to technology dependency in the learning process and construct an attribution model of technology dependency. The model identifies factors contributing to technology dependency at the individual, school, and societal levels, including technological convenience, teacher encouragement, and peer pressure. It also identifies potential cognitive, behavioral, and emotional impacts of technology dependency, including reduced independent learning, weakened critical thinking, and distracted attention. To address these impacts, intervention strategies are proposed at the school and societal levels, including increased awareness and supervision, as well as psychological counseling and intervention, to help digital natives reduce their technology dependency and develop appropriate behavior habits using technology.

Online peer assessment provides new theoretical and technical means for the development of critical thinking, and peer evaluation and peer interaction are the main activities implemented by online peer assessment. The article proposes a model of online peer assessment for critical thinking development around peer evaluation and peer interaction, uses social network and epistemic network analysis methods, and explores the development process of critical thinking in online peer assessment from the temporal dimension and role dimension, respectively, using the interactive comments as the analysis content. The findings show that (1) critical thinking shows a progressive and continuous development process in online peer assessment; (2) peer interaction is the main driver for critical thinking development; (3) critical thinking has progressive differences in different stages and groups of online peer assessment; and (4) critical thinking has a significant structural pattern. The article reveals the development process of critical thinking from the perspective of online peer assessment, and provides a theoretical basis and practical reference for improving the quality of online peer assessment and the development level of critical thinking.

A multi-sensor fusion model based on Bidirectional Long Short-Term Memory (BiLSTM) and gated attention mechanism is proposed to address the challenges of heterogeneous data fusion and insufficient real-time performance in multi-sensor human activity recognition. This method captures bidirectional long-term dependencies through BiLSTM and employs gated attention to achieve adaptive weighting of multi-source features, effectively enhancing feature representation capability and fusion efficiency. Experimental results demonstrate that the proposed approach achieves a highest accuracy of 95.3%,in addition, under a comparable lightweight setting, the proposed model exhibits lower parameter count and faster inference latency than the Transformer baseline, thereby achieving a more favorable balance between accuracy and efficiency.Ablation studies further confirm the critical role of BiLSTM and the attention mechanism in improving performance. This research provides a solution that balances recognition accuracy and inference efficiency for behavior recognition in complex scenarios, showing strong potential for practical applications.

To address the importance of global background information and local detail features in rain streak removal, as well as the limitations of deep learning models in handling high-resolution images, we propose ClearMamba, a spatio-temporal state-space model with global context fusion. This model achieves pixel-level feature refinement through spatio-temporal feature modeling and global context integration, significantly enhancing single-image deraining performance. Experiments on datasets such as DID-Data demonstrate the superiority of this algorithm, with a peak signal-to-noise ratio (PSNR) improvement of approximately 4.9% compared to methods like PReNet. Furthermore, we systematically evaluate the empowering potential of deraining models for downstream visual tasks (e.g., object detection), achieving top performance. The proposed method offers a practical solution for optimizing intelligent visual systems in rainy environments.

Focusing on the prediction of customers' new car model purchase behavior, this paper carries out a comparative analysis on the performance of four classification algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Support Vector Machine (SVM). Based on data related to car consumption, following data preprocessing, hyperparameter tuning is performed for each model. For Logistic Regression, the regularization parameters and penalty terms are optimized. For the KNN algorithm, the number of neighbors is adjusted, and an appropriate distance measurement method is selected. For Gaussian Naive Bayes, the adaptability of the feature independence assumption is verified, and corresponding fine-tuning is carried out. Optimization is performed on key parameters of SVM, including the kernel function and regularization coefficient. After training and testing evaluation, the SVM demonstrates outstanding comprehensive performance after tuning, with an AUC reaching 0.965 and a prediction accuracy of 93%. Both KNN and Gaussian Naive Bayes achieve an AUC of 0.963 and an accuracy of 93%, while Logistic Regression has an AUC of 0.955 and an accuracy of 91%. The research shows that SVM has significant advantages in fitting nonlinear data, is suitable for the car consumption prediction scenario, and provides powerful data support and decision basis for enterprises to develop targeted marketing strategies.

On-site observation in the marine environment is required to obtain real and reliable data for research on marine microorganisms. This article presents an automatic seawater sample collection and distribution system for ships based on the NI CompactRIO. The system is deployed on scientific research vessels and establishes connections with instruments such as the winch system at the stern of the vessel and microbial flow image analyzer, under the control of the management system, achieving fully automatic or semi-automatic extraction, filtration, transportation, and automatic cleaning of the entire pipeline of seawater samples. The system can also monitor various water quality parameters such as temperature, salinity, dissolved oxygen, conductivity, chlorophyll, etc. online and upload to the system for storage. The system is designed based on a reliable and stable embedded hardware system, which is conducive to long-term operation in marine environments, maximizing the preservation of the original appearance of seawater samples, and ensuring the authenticity and effectiveness of analysis and detection data.

Traditional anti-bullying interventions in schools rely primarily on proactive reports from teachers and students, together with periodic inspections and surveillance by administrators. These approaches are untimely reactive and labor-intensive, which makes timely detection and response to bullying incidents difficult. A deep-learning-based campus-bullying monitoring system that provides robust protection for campus security and safeguards the mental health of students. The system integrates YOLOv8 for object detection and 3D ResNet for action recognition as its core algorithms, while leveraging servo-driven dynamic tracking as its enabling hardware technology. By combining modules for object detection, action analysis, and dynamic tracking, we construct a comprehensive and intelligent monitoring framework. By processing and analyzing live surveillance streams in real time, the system can rapidly and accurately identify potential bullying behaviors and promptly issue early-warning notifications so that administrators can intervene without delay. In addition, the system includes the “Glimmer Haven” mini-program, which offers emotional support and psychological solace to victims, alleviating their mental distress. An AI-powered chatbot further assists students and faculty in understanding and addressing campus-bullying issues, thereby fostering a safer and more harmonious school environment.

Aiming at the core pain points of current car owners, such as scattered maintenance records and lack of targeted reminders, and combining with the diversified development trend of China's automobile aftermarket in 2024, an intelligent record and recommendation system for automobile maintenance is designed and implemented. With intelligent recording and accurate reminders as the core, the system constructs a three-layer architecture consisting of a perception layer, a data layer, and an application layer: The perception layer collects image documents, integrates YOLOv8 to locate and correct document areas, and uses OCR (Optical Character Recognition) technology to extract VIN (Vehicle Identification Number) codes. The data layer adopts MySQL master-slave architecture, Redis cache, and Alibaba Cloud OSS (Object Storage Service) to store information such as vehicle files and maintenance records. The application layer generates accurate reminders based on three-dimensional factors and dynamically iterates the system message reminder strategy in combination with user feedback.Test results show that the recognition accuracy of key information in system documents reaches 99.5%, and the record query response time is less than 1 second. The system can effectively reduce manual entry operations, solve the problems of low efficiency of traditional maintenance management and lack of targeted reminders, and provide car owners with a lightweight and highly user-friendly one-stop maintenance management service.

Aiming at the problems existing in the current teaching of database course, such as single teaching mode, disjointed theory and practice, and one-sided evaluation method, this paper puts forward a project-driven teaching mode based on constructivism theory. This model takes "supermarket management system" as the teaching project carrier, and designs and implements the whole process teaching scheme covering project start-up, exploration construction and achievement evaluation, aiming at guiding students to actively construct database knowledge through active exploration and collaborative communication in real situations. Teaching practice shows that this model can effectively improve students' ability of problem solving, teamwork and knowledge transfer, and provides an effective path with both theoretical support and practical value for the teaching reform of database course.

The "Curriculum-Training-Competition-Innovation-Industry"(CTCI²) innovation and entrepreneurship practice teaching model, driven by digital-intelligent empowerment and competitions in higher vocational computer-related disciplines, aims to effectively integrate digital-intelligent technologies, skills competitions, and innovation/entrepreneurship practices. Its goal is to cultivate high-level technical and skilled talents equipped with innovative thinking and precise career orientation, capable of adapting to the new-era industrial environment. This model explores the construction of a five-in-one innovation and entrepreneurship practice teaching framework based on digital-intelligent empowerment and competition-drive, the establishment of a digital-human interactive CTCI² innovation platform, and the development of an integrated educational effectiveness evaluation system. It seeks to propel higher vocational computer education into a new stage characterized by enhanced quality and excellence, value-added empowerment, and ultimately achieve high-quality development in vocational education.

The Linux laboratory course is a fundamental core course for computer science and information related majors, characterized by strong practicality and complex operational chains. Traditional evaluation systems mainly rely on students' submitted lab reports, which are result-oriented, lacking process analysis and contextual awareness, making it difficult to comprehensively and dynamically reflect students’ actual abilities and learning status. To this end, this paper proposes an evaluation system for Linux laboratory courses that integrates knowledge graphs with multimodal perception. It integrates key technologies such as code behavior analysis, speech and facial emotion recognition, system operation trajectory extraction, and knowledge graph reasoning. The system takes multimodal inputs—including student behavior logs, oral defense speech, facial expressions, operation flows, and error types—and applies focal contrastive learning and a Neo4j-based knowledge graph model to achieve comprehensive scoring and personalized feedback. Experimental results demonstrate that the proposed system significantly improves evaluation accuracy, consistency, and the stimulation of students’ learning motivation, providing a new paradigm and technological support for teaching reform in Linux and other system-oriented laboratory courses.

This article aims to explore an effective evaluation path that adapts to the educational goals of vocational colleges that combine moral and technical education. It organically integrates the CIPP evaluation model with the concept of "dual core of morality and technology", and constructs a three-dimensional evaluation system that covers professional qualities, skills, and career development capabilities. Take the course of Responsive Web Development for example, it conducts evaluations through four stages: background, input, process, and outcome, and combines blended teaching to achieve full-cycle data collection and dynamic optimization. Practice shows that this system can enhance students' technical abilities and professional qualities, providing theoretical and practical paths for the implementation of "dual core of morality and technology" in professional courses in vocational colleges.

In response to the disconnection between traditional electronic technology experiment teaching content and complex engineering problems, this paper restructures the experimental courses in a task-chain-based hierarchical manner to enhance students' practical abilities and comprehensive professional competencies. During the restructuring process, the curriculum design adopts the perspective of engineers' professional qualities and technical skills, promoting the deep integration of experimental content with engineering demands. The experimental content has been reorganized into five progressive levels: basic experimental projects, compound experimental projects, comprehensive experimental projects, AI-enhanced innovative experimental projects, and real competition questions from electronic design contests. Simultaneously, the course promotes innovation and reform in teaching methods and evaluation systems, moving away from a single assessment model toward a multidimensional framework that integrates theoretical knowledge, practical skills, innovative thinking, and teamwork. Through systematic reform, the electronic technology experiment course not only enhances students' hands-on abilities, but also consistently strengthens their comprehensive competencies in analyzing and solving engineering problems. Ultimately, these efforts lay a solid foundation for cultivating high-quality, interdisciplinary, and application-oriented talent in private universities, effectively bridging the gap between talent development and industry needs.

Under the background of the "Double High Plan" construction, the disconnect between higher vocational talent cultivation and industrial technology iteration urgently needs to be addressed. Based on the "Five Golds" (elite majors, high-quality courses, outstanding teachers, advanced practice bases, and innovative teaching materials) integrated resource optimization framework, and relying on the reform practice of the Internet of Things major at Suzhou Polytechnic University, this study explores the construction of an educational ecosystem featuring "integration of industry and education, and integration of certificates and curricula". The model aims to achieve deep coupling between professional chains and industrial chains, dynamic synchronization between course content and technological development, and effective integration of faculty capabilities with industry practices. Practical results demonstrate that this approach enhances the alignment of students' occupational competence with enterprise demands, providing a replicable pathway for the digital transformation of higher vocational education. As a foundational project for high-quality development in vocational education, the "Five Golds" initiative requires systematic integration and dynamic adaptation mechanisms to drive vocational education from adaptive reform toward innovative leadership^[1].

With the rapid development of technologies such as cloud computing and artificial intelligence (AI), the education sector is undergoing profound changes. As an essential component of the information technology major, the teaching reform and practice of Linux technology courses are of great significance for cultivating high-quality talents that meet the demands of the new era. This paper focuses on the teaching innovation in the field of information technology and deeply explores the teaching reform paths and practical methods of Linux technology courses in a private cloud environment, empowered by AI technology. By analyzing the existing problems in the current teaching of this course, it expounds on the necessity and feasibility of the cloud-intelligence integrated teaching model. It elaborates on specific measures such as building a teaching environment based on a private cloud platform, optimizing teaching content and resources with AI assistance, and innovating teaching methods and means. Through the analysis of actual teaching cases, it compares the teaching effects before and after the reform, aiming to provide beneficial references and inspirations for improving the teaching quality of Linux technology courses and cultivating high-quality talents that adapt to the demands of the times.