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Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

The rapid deployment of emergency remote teaching in the pandemic presents sweeping societal‐level information systems phenomena worthy of scholarly inquiry. This paper reports findings from teacher interviews conducted with K‐12 public school teachers, exploring how digital access and use gaps in communities reflect wider digital and social inequalities as schools fulfilled emergency remote teaching mandates, becoming swept up into e‐learning technology expansion trends propelled by mandates, and unfettered corporate edtech. Results show persistence of home and school level digital affordance gaps as hindrances to pandemic pedagogy. We build upon theory of the digital divide, and crisis and critical informatics literature considering how critical approaches to the study of socio‐technical systems research can inform these understandings, providing insights into how localized digital inequities contribute to broader digital inequality and social inequality, in the educative processes expected of public education in democratic societies. Our work gives voice to one highly pressured and conflicted stakeholder in these dynamics—K‐12 public school teachers—and demonstrates some of the ways in which digital inequity gaps may play a further magnifying role of societal division through expanding edtech deployment in K‐12 grades, if current edtech trends hold. [ABSTRACT FROM AUTHOR]

Background and Context: Most studies about programming misconceptions are conducted at the undergraduate and graduate levels. Since the age level for starting learning programming is getting lower, there is a need for determining programming misconceptions for younger learners. Objective: Our goal is to determine programming misconceptions and their time‐resistance for programming novices at the K‐12 level using Python as a text‐based programming language. Method: We conducted research in the school settings during informatics classes among 98 fifth‐grade students in three elementary schools. We analysed the data collected from two Python tests students took immediately after programming lectures and 5 months later. Results and Conclusions: We detected seven misconceptions: (a) Assigning the expression instead of the calculated value, (b) When reassigning the variable value, assigning the sum value to the variable, (c) Using the symbolic name of the variable instead of its value, (d) Using the first (or previously) assigned variable value, (e) Datatype misconception, (f) Sequencing misconception, and (g) Swapping variable values. By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized compared to previous findings. Lay Description: What is already known about this topic: Programming misconceptions are known issues since the beginning of learning programming in the early 1980sMost programming misconceptions are time resistant.Most studies about misconceptions are conducted at the undergraduate and graduate levels. What this paper adds: The goal of the study is to determine programming misconceptions for programming novices at the K‐12 level.Three more misconceptions about basic programming concepts are revealed compared to the previous findings.By shifting focus on variables and spending more time introducing variables and sequencing during teaching, the appearance of known misconceptions is minimized. Implications for practice and/or policy: Being aware of misconceptions is an important aspect of a teacher's pedagogical knowledge to foster students learning.Aiming to prevent the known misconceptions, teachers can minimize it by interventions during teaching.The educators should emphasize the underlying conceptual programming concepts rather than syntactic ones related to the programming language. [ABSTRACT FROM AUTHOR]

Nursing Sciences are at a moment of paradigmatic transition. The aim of this paper is to reflect on the new epistemological paradigms of nursing science from a critical approach. In this paper, we identified and analysed some new research lines and trends which anticipate the reorganization of nursing sciences and the paradigms emerging from nursing care: biotechnology-centred knowledge; the interface between nursing knowledge and new information technologies; body care centred knowledge; the human body as a cyborg body; and the rediscovery of an aesthetic knowledge in nursing care. [ABSTRACT FROM AUTHOR]