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关键词： Students  

摘要： Background and Context. Generative AI (GenAI) is becoming increasingly common in computer science education, yet its effects on student learning, skill development, and perceptions remain relatively underexplored. Despite widespread concerns about overreliance on GenAI, few studies have systematically examined how structured scaffolding can shift students’ engagement with GenAI tools in formal educational settings. Objectives. We investigate whether and how a dedicated "AI-Lab" intervention, which emphasizes scaffolding and mindful usage, shapes students’ understanding, perceptions, and applications of GenAI tools in undergraduate computer science and first-year engineering courses. Our main goal is to determine whether students develop a more deliberate approach to using GenAI that preserves and enhances essential skill building. Method. Over three regular semesters at Purdue University, we implemented the AI-Lab in two mandatory Data Structures and Algorithms courses, one elective Competitive Programming course, and one first-year engineering course, yielding 831 valid student responses. Using a mixed-methods approach, we collected and analyzed quantitative survey data (pre- and post-intervention) as well as qualitative focus group transcripts to capture both broad trends and in-depth perspectives. Findings. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students’ more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students’ awareness of their own skill development. Implications. Our findings underscore that

关键词： Prototypes   Vectors   Pipelines   Genetic algorithms   Computer science   Problem-solving   Heuristic algorithms   Binary trees   Space exploration   Metaheuristic   optimization   automated algorithm design   automated machine learning   evolutionary algorithm   evolutionary algorithm  

摘要： Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.

关键词： computer vision syndrome   electronic devices   nomophobia   Peru   smartphone  

摘要： The objective was to estimate the prevalence and identify the factors associated with Computer Vision Syndrome (CVS) among health science students at a university in Peru, we conducted a cross-sectional analytical study in Tacna, Peru, from October 12 to December 15, 2023. Participants were assessed through physical surveys. We evaluated 502 health science students;59.2% were female, and the median age was 21 years (range 19 to 23 years). 78.1% had CVS, and 52% had moderate nomophobia. In the adjusted model, monthly family income of 2000 to 5,000 soles (PR: 0.81;95% CI: 0.73 to 0.91) and over 5,000 soles (PR: 0.84;95% CI: 0.74 to 0.95) was associated with a lower prevalence of CVS compared to families with income less than 2000 soles. Additionally, the prevalence of CVS was higher among students with moderate nomophobia (PR: 1.91;95% CI: 1.24-3.16) and severe nomophobia (PR: 2.07;95% CI: 1.31-3.48) compared to those with no symptoms or only mild symptoms of nomophobia. The prevalence of CVS is high among health science students, especially in medical students. As the severity of nomophobia increases, so does the prevalence of CVS, and higher family income is associated with lower CVS.

关键词： STEM education   Cognitive abilities   Grit   High school   Excelling students   Math   Physics   Computer science  

摘要： BackgroundThe current study aimed to investigate the associations between cognitive abilities, grit, and academic achievement in high school STEM studies. The study utilized a natural sample of excelling STEM students (N = 814) who underwent a selection process to join elite undergraduate STEM programs. Through this selection process, the students reported their high school grades in Math, Physics, and Computer science, as well as their Psychometric Entrance Test (PET) scores, which are equivalent to the SAT. They also completed a Grit questionnaire and took various cognitive ability *** study highlights the significant role of grit in STEM achievements, particularly for students with moderate cognitive abilities. We found that while cognitive abilities were strong predictors of academic STEM success, grit weakly contributed to predicting achievements in STEM studies beyond cognitive abilities alone. The study's main finding revealed that grit moderates the association between cognitive abilities and academic STEM grades, with higher levels of grit diminishing the impact of cognitive abilities on STEM success. Specifically, students with high grit could leverage their determination and passion to bridge cognitive gaps, effectively reducing performance *** findings underscored the multifaceted nature of cognitive skills and non-cognitive factors like grit in contributing to STEM success. The study highlights the importance of fostering grit, particularly among students with moderate cognitive abilities. Higher grit can especially benefit these students in achieving higher STEM grades. According to Social Cognitive Career Theory (SCCT), higher achievements could promote one's self-efficacy and encourage students to pursue STEM careers after high school. This underscores the importance of integrating both cognitive and non-cognitive factors in educational and career guidance programs.

关键词： Technology   Computational biology and bioinformatics   Computer science   Software  

摘要： Computer scientists and bioinformaticians address four key questions to help rookie coders to make the right choice.

关键词： Tensors   Clustering algorithms   Feature extraction   Image segmentation   Data mining   Transforms   Technological innovation   Matrix decomposition   Computer science   Urban areas   Low-rank tensor   multiview clustering   scale-driven   unsupervised learning  

摘要： Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://***/ChuanbinZhang/SDTR

摘要： The COVID pandemic has significantly influenced educational methodologies, leading to a shift towards more interactive and technology-integrated teaching approaches. Live-coding, which involves real-time coding demonstrations, has gained recognition as a valuable tool in computer science education. This study investigates the impact of the pandemic on the popularity and application of live-coding. By conducting a comprehensive literature review of 22 research papers, this study categorizes the papers based on their publication date relative to the pandemic: pre-COVID (2017 to 2019), during COVID (2020 to 2022), and post-COVID (2023 to 2024). The papers were selected using a specific search query for live-coding in introductory computer science education on Google Scholar. A systematic literature review was performed to determine their sentiment towards live-coding, categorizing the sentiments as positive, neutral, or negative. The sentiment data were then statistically analyzed using Fisher's Exact Test to assess significant differences across the three periods. Results from this manuscript indicate shifts in positive sentiment towards live-coding during and after the pandemic.

摘要： Most computing departments offer one or two undergraduate programs, but the CSIT Department at a mid-south state university offers three, all three accredited by the Accreditation Board for Engineering and Technology (ABET): Computer Information Systems, Computer Information Technology, and Computer Science. In 2018, an integrated 3-in-1 B.S. program structure was adopted, which earned ABET accreditation in 2021--2022. With about 650 majors and 100--150 graduates annually, the streamlined curriculum has optimized resources and contributed to the department's success.

关键词： Python  

摘要： Large Language Models (LLMs), such as GitHub Copilot and ChatGPT have become popular among programming students. Students use LLMs to assist them in programming courses, including generating source code. Previous work has evaluated the ability of LLMs in solving introductory-course programming assignments. The results have shown that LLMs are highly effective in generating code for introductory Computer Science (CS) courses. However, there is a gap in research on evaluating LLMs’ ability to generate code that solves advanced programming assignments. In this work, we evaluate the ability of four LLM tools to solve programming assignments from advanced CS courses in three popular programming languages, Java, Python, and C. We manually select 12 problems, three problems from introductory courses as the baseline and nine programming assignments from second- and third-year CS courses. To evaluate the LLM-generated code, we generate a test suite of 1000 test cases per problem and analyze the program output. Our evaluation shows that although LLMs are highly effective in generating source code for introductory programming courses, solving advanced programming assignments is more challenging. Nonetheless, in many cases, LLMs identify the base problem and provide partial solutions that may be useful to CS students. Furthermore, our results may provide useful guidance for teachers of advanced programming courses on how to design programming assignments. © 2025, CC BY.

关键词： Bioinformatics   Computer science  

摘要： The security of medical information has become a significant challenge with the move from traditional filing systems to electronic records. This study proposes the use of blockchain technology to address these concerns. The system registers patients and medical staff with unique IDs and stores patient diagnoses as immutable records on the blockchain. A central interplanetary file system stores the collected data, which can be accessed by authorized users like nurses, pharmacists, and patients via special access details. Users must log in before accessing medical records through the Electronic Record Management system. This approach can be scaled to multiple hospitals. After testing, the system showed some latency issues with 100 nodes, but performance improved with more nodes (300-500), demonstrating better scalability as the system handles more data and hospitals. Overall, the proposed blockchain-based system offers a secure, scalable solution for managing and accessing medical records.