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关键词： Prototypes   Computer science   Costs   Training data   Software   Usability   Chatbots   Commercialization   Digital systems  

关键词： Computer science education   Epistemology   Feminist standpoint theory   Inclusivity   Lived experiences   Positionality   Subjectivity  

摘要： In this article, we present a conceptual framework for teaching computer science (CS) to address the unique challenges faced by marginalized groups. The framework is grounded in feminist standpoint theory and describes three key practitioner-focused areas aimed at broadening participation and increasing participation in CS education (CSEd). These principles are 1) Creating dialogic and dialectical relationships with the CS community, 2) Embracing intersectionality in CSEd, and 3) Framing CS education as a lived experience. It also offers a distinct perspective in that it is concerned with how both sides of the dialogue (i.e., the oppressed and the oppressor) grow and change in relation to one another. The strategies, along with the framework detailed in this paper, provide scholars and educators with new avenues for unpacking underrepresentation in CSEd and potential methods to address it.

关键词： Short-term trajectories   Expectancy -value -cost   Chemistry   Computer science   University students  

摘要： University students studying science, technology, engineering, and mathematics (STEM) tend to lose motivation, on average, over time. However, detailed understanding of when and how students are most likely to lose motivation, as well as whether motivational trajectories differ across contexts, is lacking. Accordingly, we investigated trajectories of expectancy, task values, and perceived costs in four introductory STEM courses (N = 2,104). Latent change score models revealed nuanced patterns of change, indicating average motivational declines in expectancy and task values and average increases in cost perceptions observed in prior research tend to occur primarily at the beginning of the semester. Motivational trajectories also differed in the shape and timing of mean changes across the four STEM courses, suggesting motivational loss trends may not be universal. Variation in trajectories also related to course performance and major persistence, with different motivational beliefs playing different roles at various times in the semester. Findings highlighted the importance of early course performance in predicting motivational change, as well as the importance of motivational changes for short- and long-term outcomes. Findings underscore the contextualized nature of motivational constructs, as both change patterns and relations to correlates differed across settings, indicating a need for researchers to carefully consider how samples are aggregated and further uncover contextual features that shape motivation. Overall, our study findings add essential understanding of motivational strengths and needs in real-world classroom contexts, with implications for the design and timing of intervention for instructors by examining what developmental patterns are common across settings and which are more particular to specific settings.

关键词： Reinforcement learning   Throughput   Vehicle dynamics   Schedules   Dynamic scheduling   Data augmentation   Computer science   Traffic signal control   reinforcement learning   fairness-aware scheduling   sample efficiency   multi-intersections  

摘要： Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose a Fairness-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches.

关键词： Student voice   equity   ethics and social responsibility with technology  

摘要： Culturally responsive computing (CRC), that centers sociopolitical issues and transformational uses of technology, has been described as valuable for increasing engagement with computing, especially for historically underrepresented minoritized students. But what do high school students think? Through a sociocultural lens prioritizing student voices recorded in 56 interviews over a period of 2 years (1-3 years after students' first experience with computer science (CS) education through Exploring Computer Science or Advanced Placement CS Principles in high school), this study centers the perspectives of 39 primarily low-income, Latine and Black youth from urban California and rural Mississippi public schools to understand what they perceive as the role of technology in our world and what they subsequently desire of their computing education. While none have studied CRC before, the majority responded with CRC ideas about the kind of pedagogy they believe would make for a more meaningful computing learning experience: They see computing as a form of power that impacts both good and bad in the world and want computing educators to prepare them to take on these issues of equity, ethics, social responsibility, and underrepresentation in the field. The students' perspectives offer important pedagogical insight into how to support deeper engagement with computing in current CS for All initiatives, while also preparing youth for the rapidly evolving and increasingly complex computing landscape that impacts all of our lives.

关键词： Image edge detection   Self-supervised learning   Complex networks   Deep learning   Matrix decomposition   Computer science   Tensors   Community detection   complex network   contrastive learning   motif  

摘要： Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

关键词： Computer science   CS identity   Single-item scale   Reliability   Validity  

摘要： Student identity measures in Science, Technology, Engineering, and Mathematics (STEM) fields are a strong predictor of future persistence in those disciplines. However, there is a paucity of studies with validated measures of students' computer science (CS) identity. The aim of the present study was to assess the reliability and validity of a proposed single-item CS identity measurement for college students (CS-I-1) that balances practical considerations and psychometric concerns. Using data from 6,044 college students across the United States, we examined the criterion, concurrent, construct validity, as well as the reliability of a CS identity measures. We find that a single item, "I see myself as a computer science person," (with a 6-point rating scale ranging from 0 = not at all to 5 = very much) is both valid and reliable as a single-item CS identity measure among U.S. college students. Being a quick and easy-to-use diagnostic tool, this measure has both practical utility and theoretical significance. We foresee its simple use in identify students with potential for pursing a CS career and for gauging the impact of CS courses and other experiences when used as a pre- and post-test measure, making it a valuable tool for assessing changes in CS identity among students.

关键词： Quality of service   Measurement   Artificial neural networks   Reliability   Computer science   Noise   Internet of Vehicles   Bandwidth Categorization   Quality of Service   Cooperative Intelligent Transportation Systems (C-ITS)   Machine learning   Delay sensitive applications   Routing Protocols  

摘要： Quality of Service (QoS) plays a pivotal role in numerous delay-sensitive applications that range from general to specific such as the Internet of Medical Things (IoMT), Industrial Internet of Things (IIoT), Unnamed Aerial Vehicles (UAVs), Industrial Automation, and Cooperative Internet of Vehicles (C-IoV), etc. Every application has numerous contributions to human daily life activities, but here in this work, we focused on the C-IoV in the context of QoS metrics. Even though the literature suggested several techniques to address the QoS issues in this emerging technology, but we have not come across a single article that addresses this issue in a cooperative environment, considering the impact of communication congestion and contention by taking into account emergency vehicles and traditional vehicles. Given that, in this paper, we introduce a hybrid framework known as the Traffic Congestion and Priority-Aware Algorithm (TCPAA). This innovative paradigm leverages the capabilities of computer vision, Deep Neural Networks (DNN) and Dijkstra algorithm to strategically incorporate the transmission channels and network entities with an objective to improve the QoS metrics in emergency vehicles. Initially, we developed a dataset with computer vision algoritms "real-time (OpenCV "Background Subtraction") to evaluate and chose the best machine learning algorithms among random forest, support vector machine (SVM), k-means clustering, and DNN. Based on the result statistics, we select DNN, and classified vehicles into two classes: Emergency and traditional vehicles to train the model. Subsequently, we set standard for two type of communications such as regular and prioritized traffic. We incorporate a micro base station (Acirc;mu BS) in the network for prioritized traffic to facilitate congestion-free communication of emergency vehicles, while the Dijkstra algorithm is used to managed the communication of traditional vehicles. Considering the nature of operation of future au

关键词： Middle-aged   Women   Older adult care   Gender role   Value of lost leisure   Willingness to pay  

摘要： This study evaluates the extent of leisure time loss experienced by middle-aged women in South Korea who care for their aging parents and quantifies the monetary value of this lost leisure time. By employing an open-ended direct question, we derived respondents' willingness to pay and willingness to accept compensation for leisure time lost due to caregiving duties. Our findings reveal that middle-aged women engaged in regular caregiving are willing to pay an average of 9313 KRW (US$7.74) per hour for leisure time and would require 13,539 KRW (US $11.40) per hour as compensation for their lost leisure. The annual total monetary value of leisure time loss for consistent caregivers was estimated to range between 7403.4 billion KRW (US$6232 million) and 10,127.5 billion KRW (US$8525 million). The study highlights the significant challenges faced by caregivers, particularly middle-aged women, emphasizing the need for social awareness and discussions on gender role distribution to promote gender equality. It underscores the importance of developing effective support policies, including financial compensation and mental health resources, to alleviate caregivers' dual burden of work and home responsibilities. Understanding the economic impact of leisure time loss can help address long-term socioeconomic issues associated with an aging society, crucial for improving the efficiency and sustainability of care services for the older generation. The economic value estimated in this study will provide essential information for evaluating the cost-effectiveness of public services, supporting the economic feasibility of policies and programs, especially because respite services involve significant government expenditure.

关键词： Early warning systems   Predictive analytics   At-risk students   Machine learning   Classification  

摘要： Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics.