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The emergence of new technologies such as AI, machine learning, and so on drives the discussion over human consciousness among researchers from various disciplines, particularly those from philosophy of mind and computer science. This is true since certain academic theories argue that new technologies like those are much more efficient at simulating human tasks. This allows the discussion concerning human consciousness to acquire attention among researchers interested in learning more about human consciousness. As a result, this study provides a bibliometric analysis of human consciousness research from 2000 to 2022 to provide researchers and academics worldwide with an overview of the outcomes and trends in human consciousness research. A textual query on two databases; Scopus (1413 articles), and PubMed (258 papers) using the term "human consciousness" was conducted on 16 August 2022 by collecting 1671 academic works from 2000 to 2022 relating to human consciousness research for in-depth study. Bibliometric analyses were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to show the current of human consciousness research. This bibliometric study examined the yearly scientific publishing growth, the most productive authors, most often term that has been utilised, most renowned journal name, and which nation has the maximum cooperation with other country. According to the findings of the study, there is a significant inconsistency in worldwide patterns in yearly scientific output, with the number of publications growing and declining. Among all nations, United States of America (USA) published the most papers around human consciousness study. According to the research, it has shown that the most relevant contributors is Jun'ichi Takeno (Scopus), and Brian L Edlow (PubMed). Moreover, the most famous journal in human consciousness research includes Journal of Consciousness Studies (Scopus), and Frontiers in Psychology (PubMed). The significance of this study may aid new researchers in this area by offering information on important publications and authors to refer while undertaking research on this issue. Furthermore, this research helps other researchers understand current trends in this field of study. As an outcome, the objective for conducting this study is to give bibliometric analysis and to fulfill research gaps in bibliometric studies of human consciousness by giving information in the form of an overview, and suggestions. [ABSTRACT FROM AUTHOR]

In the 21st century, as part of the fourth industrial revolution, artificial intelligence (AI), is one of the most important and well-known technology. AI also has made it possible to execute human tasks without the need for humans. However, there is one important concern with consciousness. This is because consciousness is one of the most defining qualities between AI and humans. So, this study presents a literature review and bibliometric analysis of artificial and mind consciousness research around the world from 2000 to 2022 in order to provide researchers and scholars around the world with an overview of the results and trends in artificial and mind consciousness research. A textual query on two databases; Scopus (289 papers), and Web of Science (303 papers) using the term "artificial consciousness" OR "mind consciousness" was performed on 10 June 2022 retrieving 509 scholarly papers from 2000 to 2022 related to artificial and mind consciousness studies for in-depth analysis. Bibliometric analysis were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to visualize and analyze trends of artificial and mind consciousness research. This bibliometric analysis was analyzed the annual scientific publication growth, the most productive authors, most frequent word has been using, most famous journal name, and which countries has highest collaboration with other country. According to the findings of the analysis, there is significant inconsistency in global trends in annual scientific production, with the number of publications increasing and decreasing. Among all countries, United States (USA) contributed the most publications in the field of artificial and mind consciousness research. According to the findings has show the most relevant authors is Kelley TM (Scopus), and Patel AD (WoS). Moreover, the most relevant journals articles in artificial and mind consciousness studies is Procedia Computer Science (Scopus), and Journal Of Consciousness Studies (WoS). The implication of this study is can help new researchers in this field by providing information on relevant publications and authors to consult when conducting research on this topic. Furthermore, this research helps other researchers understand current trends in this area of study. As a result, the justification for doing this study is to provide the first bibliometric analysis and to fill research gaps in bibliometric studies of artificial consciousness and the mind by providing information in the form of a literature review, overview, and guidelines. [ABSTRACT FROM AUTHOR]

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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 study discussed in this article attempts to trace the research trends in information and communication technology (ICT) in top 10 countries during 1999-2018, as reflected by scholarly publications index in the Scopus database. During the mentioned time period, total 18,382 research papers were published. Of these, maximum 1622 (8.82%) records were published in 2017. The annual growth (- 11.07 to 51.30) and the compound annual growth (- 0.01 to 0.23) have shown a fluctuating trend. However, a considerable number of records (7345) were published in the field of computer science while 52.35% of the records (9623) were journal article-type publications. 'Information and communication technology' was the most significant keyword used in 4997 records while the average (145.3) publications were contributed by top 10 organizations in which Japan National Institute of Information and Communications Technology was the most productive organization with 256 records. The maximum contributing country was the United States, standing for 22% of the total publications produced. Toyoshima, M. is the most prolific author with 53 publications on a particular topic, that is, ICT. The overall relative growth has shown a decreasing trend (0.92 to 0.09) while during the period of study, the doubling time has shown an increasing trend (0.75 to 7.54). Out of the total 18,382 records, total 209,919 citations were recorded in which the maximum citations (20,548) were found in 2010. Out of the total 55,913 research papers, maximum (5058) records were contributed by two authors while the least number of publications (2186) came from four authors. The mean value of the degree of collaborations was 0.92 and the overall degree of collaboration has exhibited an increasing trend during the period of study. The average author's collaboration coefficient and modified collaboration coefficient was recorded to be 0.69 while collaborative index was recorded at 4.21. However, Lotka's law of authors' productivity shows, the authors observed are somehow unlike from the numbers of authors expected during the period of study. [ABSTRACT FROM AUTHOR]

The high-tech sector is concentrated in a small number of cities. The ten largest clusters in computer science, semiconductors, and biology account for 69 percent, 77 percent, and 59 percent of all US inventors, respectively. Using longitudinal data on 109,846 inventors, I find that geographical agglomeration results in significant productivity gains. When an inventor moves to a city with a large cluster of inventors in the same field, she experiences a sizable increase in the number and quality of patents produced. The presence of significant productivity externalities implies that the agglomeration of inventors generates large gains in the aggregate amount of innovation produced in the United States. (JEL D62, J24, L60, O31, 034, R32) [ABSTRACT FROM AUTHOR]

Student retention is one of the greatest challenges facing computer science programs. Difficulties in an introductory programming class often snowball, resulting in poor student performance or dropping the major completely. In this paper, we present an analysis of 197 students over 6 semesters from 11 sections of an introductory programming class at a private four-year liberal arts university in the southeastern United States. The goal of this research was to find the earliest point in the assessment sequence which could predict final grade outcomes. Accordingly, we measured the degree of correlation between student performance on quizzes, labs, programs, and tests compared to final course grade. Overall, the results show a strong positive correlation for all four assessment modalities. These results hold significance for educators and researchers insofar as the body of computing education research is extended by evaluating the relative effectiveness of early semester subsets of each of the four categories of student data to model class outcomes. [ABSTRACT FROM AUTHOR]

Background: Numerous efforts worldwide have been made to increase diversity in engineering and computer science (ECS), fields that pay well and promote upward mobility. However, in the United States (U.S.), females and students from underrepresented racial/ethnic minority groups (URM) still pursue ECS training far less than do their peers. The current study explored sex and racial/ethnic differences in ECS college enrollment as a function of math and verbal SAT score patterns (balanced or imbalanced) using an intersectional approach within a U.S. context. Data represented a census of students who took the SAT, graduated from all Virginia public high schools between 2006 and 2015, and enrolled in a 4-year college (N = 344,803). Results: Our findings show, within each sex, URM students were at least as likely as their non-URM peers to enroll in ECS programs when they scored within similar SAT score ranges. Students were more likely to enroll in ECS programs if their SAT profile favored math, compared to students with similar math and verbal SAT scores (balanced profile). This overall pattern is notably less pronounced for URM female students; their propensity to major in ECS appeared to be largely independent of verbal scores. Conclusions: Our findings inform strategies to diversify ECS enrollment. If programs continue to emphasize SAT scores during admission decisions or if more systemic issues of resource allocation in secondary schools are not addressed, other efforts to broaden participation in ECS programs may fall short of goals. Our findings also highlight the importance of considering the intersection of sex and race/ethnicity for recruitment or other educational promotions. [ABSTRACT FROM AUTHOR]

Using responses from a large respondent-initiated online survey, we find that the career interests of many current and aspiring computer scientists in the United States diverge from a popular and official depiction of computer scientists' interests used for career and workforce development worldwide. Distinct profiles of career interests emerged from the data. These profiles suggest that many women in the field value social and artistic expression in a way not currently recognized by established depictions of computer scientists' interests. Better capturing the diversity of interests in computer science might help to boost women's, and men's, engagement in this STEM field. [ABSTRACT FROM AUTHOR]

The prolific (exceptionally high producers of scholarly publications) are strategic to the study of academic science. The highly prolific have been drivers of research activity and impact and are a window into the stratification that exists. For these reasons, we address key characteristics associated with being highly prolific. Doing this, we take a social-organizational approach and use distinctive survey data on both social characteristics of scientists and features of their departments, reported by US faculty in computer science, engineering, and sciences within eight US research universities. The findings point to a telling constellation of hierarchical advantages: rank, collaborative span, and favorable work climate. Notably, once we take rank into account, gender is not associated with being prolific. These findings have implications for understandings of being prolific, systems of stratification, and practices and policies in higher education. [ABSTRACT FROM AUTHOR]

With a growing number of ScratchJr usage, over 19 million users worldwide, we examined the use in the United States of the free ScratchJr programming language, explicitly designed for young children ages 5–7, to learn how to code. Our objective was to explore children's usage of the ScratchJr tablet app at home and school settings. We analyzed usage data from Google Analytics in 1.5 years, comparing Scratchsr usage in the two different settings. Our dataset comprised a total of 4,352,802 coding sessions, generated by a daily average of 2525 home users and 9969 school users. The results suggested that, although children in both settings on average spent an equal duration with ScratchJr, children in home settings spent more time exploring advanced coding blocks and the paint editor compared to children at school. Further, children at school tended to use similar types of coding blocks across several days. In contrast, children at home were more likely to use a diversity of block categories and difficulty levels. The implications of this research are, first, that usage patterns may help us understand how children across settings learn to program differently. Second, based on these findings, it may be essential for parents at home and educators at school to consider using different approaches and strategies. [ABSTRACT FROM AUTHOR]