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Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [ABSTRACT FROM AUTHOR]

The 33rd International Workshop On Combinatorial Algorithms (IWOCA 2022) was held at the University of Trier in Germany. This workshop covers a wide range of topics related to combinatorial algorithms. The special issue of the journal Algorithmica contains extended versions of selected papers from IWOCA 2022, which were nominated by the Program Committee and underwent a rigorous reviewing process. The special issue includes nine papers on various topics such as perfect matchings, algorithmic questions, and winner determination algorithms. One paper was chosen as the Best Paper of IWOCA 2022 and another as the Best Student Paper. The special issue is recommended for readers interested in exploring more papers from IWOCA 2022. [Extracted from the article]

In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction. This paper presents post-COVID best practices for employing technology in higher education based on an original survey and follow-up interviews of seventeen computing instructors at our institution. After a literature review, we describe four general categories of practices that enhance the post-COVID classroom: online student activities, digital instructor notes, remote classroom participation and collaboration, and a paperless classroom. For each of these categories, we provide vignettes to illustrate scope and intent. We also offer recommendations for addressing digital dishonesty, required infrastructure, institutional support, and being prepared to seamlessly return to a blended or fully remote environment in the event of another crisis. Finally, we identify additional emerging technological challenges and opportunities that require further effort. Overall, this paper emphasizes the need for a shift towards improved practices in the classroom rather than just a return to pre-pandemic norms. We believe implementing these recommendations will result in a more flexible, accessible, and robust post-COVID educational experience.

Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

In this paper we discuss the types of knowledge used by six middle school students as they engaged with a debugging task designed to integrate ideas from computer science, mathematics and science. Our findings show that the computational thinking practice of debugging is a rich source of opportunities to integrate these different disciplines. The analysis illustrates how the types of knowledge the students did and did not use at each step of the debugging process were related to their ability to succeed at each step. Our work contributes to theory and practice by uncovering implications for studying debugging through two refined frameworks and for designing debugging tasks to support transdisciplinary learning.

The International Journal of Semantic Computing has published a special issue on Semantic Computing, which includes five selected papers from the 17th IEEE International Conference on Semantic Computing. Semantic Computing focuses on the derivation, description, integration, and use of semantics for various resources. The papers in the special issue cover topics such as intent detection, constructing probabilistic models from knowledge graphs, unsupervised estimation of subjective content descriptions, the influence of noisy labels on question answering systems, and automatic domain-adaptive sentiment analysis. Each paper presents innovative approaches and findings in their respective areas of research. [Extracted from the article]