A comparative study of research performance in computer science

The term gender gap refers to the significant underrepresentation of females in many subjects. In Computer Science, the gender gap exists at all career levels. In this article, we study whether there is a performance gap in addition to the gender gap. To answer this question, we analyzed statistical data on student performance in Computer Science from 129 universities in the United Kingdom covering the years 2002 to 2013. We find that male students were awarded significantly more first-class degrees than female students. We evaluate four other subjects—Subjects Allied to Medicine, Business & Administrative Studies, Mathematical Sciences, and Engineering & Technology—and find that they do not exhibit this performance gap. From this finding, we review explanations for the gender and performance gaps, as well as potential solutions to eliminate the gaps. Most solutions do not require major institutional change and could thus be implemented easily.

The pipeline of three introductory computer science courses at our university have low pass rates. As part of our efforts to improve the pass rates in these courses, we examine the students' mathematics preparation and their progress from one course to the next. We find that the minority of students who enter the university with good mathematics preparation are likely to succeed in our introductory computer science courses. For the majority of students, we suggest a series of interventions throughout the pipeline. Prior to taking Computer Science 1, students could gain more experience with problem solving during the Introduction to Computer Science and Introduction to Computer Engineering courses. We suggest adding precalculus as a prerequisite for Computer Science 2. For students who do not earn an A in Computer Science 2, we plan to examine whether taking a class in a second programming language before taking Computer Science 3 will improve performance in Computer Science 3. Our goal is to improve student learning and retention through these efforts, combined with continual innovation in the classroom.

Advisor–advisee relationship is important in academic networks due to its universality and necessity. Despite the increasing desire to analyze the career of newcomers, however, the outcomes of different collaboration patterns between advisors and advisees remain unknown. The purpose of this paper is to find out the correlation between advisors’ academic characteristics and advisees’ academic performance in Computer Science. Employing both quantitative and qualitative analysis, we find that with the increase of advisors’ academic age, advisees’ performance experiences an initial growth, follows a sustaining stage, and finally ends up with a declining trend. We also discover the phenomenon that accomplished advisors can bring up skilled advisees. We explore the conclusion from two aspects: (1) Advisees mentored by advisors with high academic level have better academic performance than the rest; (2) Advisors with high academic level can raise their advisees’ h-index ranking. This work provides new insights on promoting our understanding of the relationship between advisors’ academic characteristics and advisees’ performance, as well as on advisor choosing.

Environmental science and research in China: A snapshot of the current state

This article presents an observation resulted from the six-year Sino-USA computer science leadership exchanges: the trend towards the emergence of a new computer science that is more universal and fundamental than that in the past. In the 21st century, the field of computer science is experiencing fundamental transformations, from its scope, objects of study, basic metrics, main abstractions, fundamental principles, to its relationship to other sciences and to the human society, while inheriting the basic way of thinking and time-tested body of knowledge accumulated through the past 50 years. We discuss three new concepts related to this trend. They are computational lens and computational thinking articulated by US scientists, and ternary computing for the masses proposed by Chinese scientists. We review the salient features of these concepts, discuss their impact, and summarize future research directions.

<i>Journal of Ecology</i> news

The European Young Chemist Award 2012

The question of which type of computer science (CS) publication—conference or journal—is likely to result in more citations for a published paper is addressed. A series of data sets are examined and joined in order to analyze the citations of over 195,000 conference papers and 108,000 journal papers. Two means of evaluating the citations of journals and conferences are explored: h5 and average citations per paper; it was found that h5 has certain biases that make it a difficult measure to use (despite it being the main measure used by Google Scholar). Results from the analysis show that CS, as a discipline, values conferences as a publication venue more highly than any other academic field of study. The analysis also shows that a small number of elite CS conferences have the highest average paper citation rate of any publication type, although overall, citation rates in conferences are no higher than in journals. It is also shown that the length of a paper is correlated with citation rate.

The Yellow River Basin (YRB) is an important economic zone and ecological barrier in China. The analysis of its research characteristics and hotspots has been helpful to grasping the future research direction. This work carried out text mining and analysis on scientific papers related to eco-environment research in the YRB from English and Chinese publications. It showed that: there was a fluctuating upward trend over the past 30 years, which was closely related to major events in the YRB during the same period. Chinese research institutions have a closer cooperation with the USA, Australia and other developed countries. More articles were from high-quality journals in ecology, the environment, and others. Interestingly, research institutions with more Chinese articles were mainly located around Beijing or the YRB. Additionally, from a research object perspective, both the English and Chinese articles have mainly focused on large areas such as the lower Yellow River, the middle reaches of the Yellow River, and the upper reaches of the Yellow River, then turning to small areas such as the Yellow River estuary and the source area of the Yellow River. Eco-environment research in the YRB has involved multiple disciplines, and “water–soil–vegetation–ecological protection” has been widely concerned. From the evolution law of hot topics, it has shown a transformation from quantity to quality, from utilization to management, from macro to micro, from construction to high-quality development. It suggests that future research should focus on water, soil, the ecological environment and local high-quality development in small regions and small watersheds.

Publisher’s note

The August President's Page is devoted to a description of the IEEE Future Directions Committee by Roberto Saracco. Roberto Saracco is the President of EIT ICTLABS Italy and Italy Node Director (European Institute of Innovation and Technology). His background is in math and computer science. Up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics, and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE, where he has served in various positions in ComSoc, and he currently leads the Future Directions Committee. He has published over 100 papers in journals and magazines and 11 books.

Semantic Web research relies on a number of key methodologies such as knowledge representation languages or reasoning algorithms. As a research community, however, we could not progress based on these methodologies exclusively, but require tools and systems that realize our research results as key technologies for the Semantic Web. There would be no Linked Data without repositories to store them, no Semantic Web applications without application programming interfaces to load and edit ontologies, and no semanticsbased user interfaces without reasoners. Consequently, some of these technologies are research enablers in their own rights. Unfortunately, the developers of such software— often researchers themselves—rarely get appropriate credit for their work. Attempts to publish papers describing their software systems frequently provoke reviews and decision letters questioning their ’conceptual contribution’. This criticism may be correct, be it because the conceptual underpinnings have been published previously, or because there may in fact be little conceptual underpinnings apart from excellent software engineering. As a result, developers of key research enabling software often have considerable difficulties publishing their work in high-quality journals. In turn, paradoxically, researchers using these systems within their own work lack a primary citation source for the used software, and may have to settle for a URL, a short demo paper, or for a workshop contribution as citation. These outlets, however, can not provide the rigid quality criteria and visibility compared to journal publications. There is a need for publishing Tools and Systems papers in primary journals. The criteria applied to evaluate such papers have to differ from those applied to classical research articles. Unfortunately, these alternative evaluation criteria are often not spelled out in detail and therefore not consistently taken into account by reviewers and editors. As a consequence, the Semantic Web journal has adopted the Tools and Systems article as one of its regular paper types and provides crisp criteria for their evaluation: Reports on tools and systems—short papers describing mature Semantic Web related tools and systems. These reports should be brief and pointed, indicating clearly the capabilities of the described tool or system. It is strongly encouraged, that the described tools or systems are free, open, and accessible on the Web. If this is not possible, then they have to be made available to the reviewers. For commercial tools and systems, exceptions can be arranged through the editors. These submissions will be reviewed along the following dimensions: (1) Quality, importance, and impact of the described tool or system (convincing evidence must be provided). (2) Clarity, illustration, and readability of the describing paper, which shall convey to the reader both the capabilities and the limitations of the tool. The impact of the described tool or system is indeed decisive. Authors are asked to provide evidence for a substantial number of researchers using their software for research and applications. In exceptional cases, authors may also argue about the potential future impact of their software given that they can provide clear evidence for this. Clearly, software prototypes which were only implemented as proof-of-concepts, or software which is closely tied to a particular (externally funded) project, are out of scope. Originality or novelty—which are decisive dimensions for high-quality research articles—are not mentioned among the evalu-

Guest Editorial

Large Language Models (LLMs) have significantly advanced prompt-based information retrieval, yet their potential to reproduce or amplify social biases remains insufficiently understood. In this study, we investigate this issue through the concrete task of reconstructing real-world co-authorship networks of computer science (CS) researchers using two widely used LLMs—GPT-3.5 Turbo and Mixtral 8x7B. This task offers a structured and quantifiable way to evaluate whether LLM-generated scholarly relationships reflect demographic disparities, as co-authorship is a key proxy for collaboration and recognition in academia. We compare the LLM-generated networks to baseline networks derived from DBLP and Google Scholar, employing both statistical and network science approaches to assess biases related to gender and ethnicity. Our findings show that both LLMs tend to produce more accurate co-authorship links for individuals with Asian or White names, particularly among researchers with lower visibility or limited academic impact. While we find no significant gender disparities in accuracy, the models systematically favor generating co-authorship links that overrepresent Asian and White individuals. Additionally, the structural properties of the LLM-generated networks differ from those of the baseline networks. These results highlight the importance of examining how LLMs represent social and scientific relationships, particularly in contexts where they are increasingly used for knowledge discovery and scholarly search.

E-learning encompasses not only technology but also pedagogical and instructional strategies to configure a complete learning environment based on the Internet. E-learning is nowadays widely used in higher education as a mean for supporting learning on academic programs. Concurrently, the Open Educational Resources (OER) are becoming a valuable alternative to improve access to high-quality educational content released under open licenses by outstanding universities worldwide. The conjunction of both concepts can configure a strategy to improve the quality of the curricula in the higher education institutions, particularly in development countries, in order to equalize the learning outcomes of international academic programs and to reduce the cost associated with educational content development. This work aims to achieve a preliminary understanding of the potential of the OER availability to be used in E-learning environments. As a case study, we have conducted an exploration of the feasibility of using OER to supplement E-learning environments for Higher Education in Computer Science at Ecuador. The search of the OER suitable to be used for this purpose has been performed on all categories of OER websites, including Open Courseware projects of prestigious universities. Moreover, this paper highlights the main barriers as well as the opportunities derived from adopting OER in E-learning environments.