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关键词： Equity   CS for All   WOMEN   IDENTITY  

摘要： Objectives. At the K-12 level, "CS for All" initiatives across the country strive to increase equitable access to and participation in computer science (CS). However, there are many open questions about the implementation and effectiveness of these initiatives, including the extent to which exposing young people to CS early on can shape their longer-term CS interest and engagement. In this article, we examine CS participation among 6th-8th-grade Black girls and assess whether CS participation during middle school shapes CS interest and engagement during their first year of high school. We focus on Black girls in the hopes of developing a more nuanced and rigorous understanding of computing experiences at the intersection of racism and sexism in this field. Participants. The focal group of students in this study are 6th-8th-grade Black girls from New York City. We employ a comparative lens in this article, contextualizing the CS experiences and outcomes of Black girls to Latinas, Asian, and White girls, as well as Black boys. Study Method. We primarily rely on quantitative data for this article, applying a critical lens to our analyses and interpretation. More specifically, we conduct descriptive analyses of course-taking patterns as well as survey data that focus on student attitudes and beliefs about CS. We then carry out inferential analyses of students' administrative records examining how, if at all, middle school CS participation is related to high school outcomes for Black girls. We employ a comparative lens and rely on qualitative data to make sense of our results. Findings. We find that, troublingly, Black girls in the district are disproportionately less likely to receive CS instruction in middle school. Black girls are also less likely than Black boys, Latinas, and White girls to feel that they belong in CS. However, Black girls in CS courses report similar levels of engagement, family, and peer support, as well as value for CS relative to other students i

关键词： Broadening participation in computing   intersectional computing   mentorship   AFRICAN-AMERICAN WOMEN   STEM   PERSISTENCE   ADOLESCENTS   ENGAGEMENT   INTERESTS   GRADUATE   STUDENTS   VILLAGE   GENDER  

摘要： Several studies have reported the positive benefits of informal Computer Science learning programs for Black girls, which include staff, mentors, and peers reflective of the girls in the program; however, we do not know enough about what motivates Black women to sign up to teach in such programs, or how representation in mentoring affects future CS learning for Black girls. Here we report the intersectional experiences of 11 Black women who served as instructors and mentors for the virtual INTech Summer Camp. The women in this study confirm that it is imperative to consider the intersectional experiences of Black women in CS as a whole, without explicating their identities. We found that Black women in CS feel that it is their responsibility to give back to Black girls to pay it forward from their own mentorship experiences. The Black women in our study describe seeking to fill a gap in mentorship that they experienced themselves, to share their knowledge and experiences to support the development of others who can relate to that intersection of identifying as Black and a girl or woman. Providing positive representation to encourage young Black girls to pursue tech careers is imperative to diversifying the tech workforce.

关键词： Computer science   Machine learning   Society  

关键词： Computer science   Drug discovery   Machine learning   Structural biology  

摘要： Criticism of our decision to publish AlphaFold3 raises important questions. We welcome readers’ views.

关键词： Latinas   Computer science   Undergraduate   Middle school   Counternarrative   RACE  

摘要： This study puts forth the counternarrative of the first author Gislaine, a first-generation undergraduate student, Latina, and computer science major. Gislaine participated in a research internship and STEM mentorship program led by the second author, Meredith. Through this program, Gislaine designed and taught CS lessons to predominantly low-income, African-American middle school students over an 8-day urban summer school program. By analyzing Gislaine's written account of her journey to CS, as well as planning documents and written reflections on her daily experiences mentoring youth, we explore how Gislaine's intersecting social identities shape her experiences both as a CS learner and as a mentor to youth. Gislaine's counternarrative illuminates the systems of oppression present in schools and the CS field, while also highlighting Gislaine's transformative approaches to mentoring students in CS. Gislaine's narratives emphasize the importance of humanizing CS and leveraging students' strengths to promote equitable access to CS education. Through her experiences, she underscores the significance of recognizing and addressing systemic barriers, while also advocating for inclusive and empowering educational practices.

关键词： computer science   diversity and inclusion   gender equity   higher education   science education   women in science   UNDERGRADUATE RESEARCH   SELF-EFFICACY   ROLE-MODELS   STUDENTS   INVOLVEMENT   SENSE   IDENTITY   NEED  

摘要： The gender imbalance in computer science (CS) is one of the most challenging issues in American education. CS is the only science, technology, engineering, and mathematics (STEM) field in which women's representation has steadily declined in recent decades. In this study, we explored one potential approach that could be effective in increasing college women's participation in CS: participation in Women-in-Computing (WiC) groups. Through participant observation and individual interviews in a WiC group at a major research university, we investigated how students engage in WiC, the impacts of the WiC on identity and belonging, and the challenge of sustainability. The results were coded using a hybrid of grounded and deductive coding and indicate that WiC groups offer various programs and events that enable women in CS to fully participate, learn, and grow. WiC represents an identity, a community, a safe space, and a journey. The results also suggest that the WiC has had positive impacts on students' identity and belonging, as evidenced by increased self-efficacy, reduced imposter syndrome, and enhanced sense of belonging and community. Furthermore, we outline three strategies employed by the WiC to ensure the group's sustainability. Our study sheds light on how WiC can encourage women to enter and persist in CS, and on some of the characteristics of a successful WiC. We demonstrate that WiC may be potentially effective in diversifying CS through identity-based participation. Moreover, student leaders design both the structure of the group and the leadership continuity process to ensure sustainability.

关键词： Application programs  

摘要： This paper first presents the NSFC applications and grants of the General Program, Young Scientists Fund and Fund for Less Developed Regions, as well as the Major Program, Excellent Young Scientists Fund, and Distinguished Young Scholars Fund in computer science (F02) in 2023. Then, we analyze the funding situations of universities in computer science in 2023. Finally, the evaluation principles and measures of fund project for computer science are introduced. Meanwhile, the development prospects of computer science in the future are also summarized. © 2024 Science Press. All rights reserved.

关键词： Computational Thinking   Computer Science   Engineering Education   primary education   secondary education   SCRATCH   SCHOOL  

摘要： This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in-person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four-phase methodology, consisting of pre- and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in-person or online. The results indicate that in-person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape.

关键词： Prediction algorithms   Task analysis   Graph neural networks   Electronic mail   Message passing   Approximation algorithms   Computer science   Graph neural networks (GNNs)   hashing   message passing (MP)   network embedding  

摘要： Given a network, it is well recognized that attributed network embedding represents each node of the network in a low-dimensional space, and, thus, brings considerable benefits for numerous graph mining tasks. In practice, a diverse set of graph tasks can be processed efficiently via the compact representation that preserves content and structure information. The majority of attributed network embedding approaches, especially, the graph neural network (GNN) algorithms, are substantially costly in either time or space due to the expensive learning process, while the randomized hashing technique, locality-sensitive hashing (LSH), which does not need learning, can speedup the embedding process at the expense of losing some accuracy. In this article, we propose the MPSketch model, which bridges the performance gap between the GNN framework and the LSH framework by adopting the LSH technique to pass messages and capture high-order proximity in a larger aggregated information pool from the neighborhood. The extensive experimental results confirm that in node classification and link prediction, the proposed MPSketch algorithm enjoys performance comparable to the state-of-the-art learning-based algorithms and outperforms the existing LSH algorithms, while running faster than the GNN algorithms by 3-4 orders of magnitude. More precisely, MPSketch runs 2121, 1167, and 1155 times faster than GraphSAGE, GraphZoom, and FATNet on average, respectively.

关键词： Ensemble learning   Reliability   Clustering methods   Task analysis   Data privacy   Computer science   Urban areas   Clustering ensemble   incomplete data   MULTIVIEW   QUALITY  

摘要： Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.