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The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articles while seldom including mainstream conference papers which also contribute to an important part of scientific output in computer science and emerging disciplines like educational technology and are equally and even more important than articles in knowledge transmission. Hence, conference papers should also be included in bibliometric studies to produce a complete and precise picture of scientific production concerning educational technology. This study, therefore, uses bibliometrics and topic modeling to analyze papers from mainstream conferences, including Artificial Intelligence in Education, Learning Analytics and Knowledge, Educational Data Mining, Intelligent Tutoring System, and Learning at Scale, focusing on contributors, collaborations, and particularly research topics and topic evolutions to inform relevant stakeholders about educational technology's development and its future. Results indicate promising areas like affective computing and behavior mining for adaptive instruction, recommender systems in personalized learning recommendations, eye-tracking for cognitive process diagnosis, videos for feedback provision, and natural language processing in discourse analysis and language education.

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios. (Contains 7 tables, 4 figures, and 1 footnote.)

The general purpose of this study is to conduct a content study on 'computer' and 'educational technologies' research and trends between 2016 and 2020. The topics were evaluated according to years, universities of the authors, citations, keywords, document type, source, sponsors and publication languages. The articles examined in the research include keywords related to 'computer' and 'instructional technologies' between 2016 and 2020; 1,798 articles obtained by scanning the Scopus database according to the title, keywords and summary of the articles were examined. When the results of the study were examined, it was concluded that English, which is the universal language, is very common; the researches are mostly published as 'conference papers' and the most used keyword in the study is 'Computer Science'. In addition, it has been concluded that computer science is the basis of educational technologies in recent years. Similar content analysis studies may be recommended for other software used in computer training.

Every word signifies multiple senses. Many studies using comprehension-based measures suggest that polysemes' senses (e.g., "paper" as in "printer paper" or "term paper") share lexical representations, whereas homophones' meanings (e.g., "pen" as in "ballpoint pen" or "pig pen") correspond to distinct lexical representations. Less is known about the lexical representations of polysemes compared to homophones in language production. In this study, speakers named pictures after reading sentence fragments that primed polysemes and homophones either as direct competitors to pictures (i.e., semantic-competitors), or as indirect-competitors to pictures (e.g., polysemous senses of semantic competitors, or homophonous meanings of semantic competitors). Polysemes (e.g., "paper") elicited equal numbers of intrusions to picture names (e.g., "cardboard") compared to in control conditions whether primed as direct competitors ("printer paper") or as indirect-competitors ("term paper"). This contrasted with the finding that homophones (e.g., "pen") elicited more intrusions to picture names (e.g., crayon) compared to in control conditions when primed as direct competitors ("ballpoint pen") than when primed as indirect-competitors ("pig pen"). These results suggest that polysemes, unlike homophones, are stored and retrieved as unified lexical representations.

Using CiteSpace to draw a keyword co-occurrence knowledge map for 1,048 research papers on the quality of higher education from 2000 to 2014 in the Chinese Social Sciences Citation Index database, we found that over the past 15 years, research on the quality of Chinese higher education was clearly oriented toward policies, and a good interactive relationship formed between research and policy. Looking at research topics, apart from macrodiscussions of the spirit of several educational reforms, the relevant topics are mainly focused on higher education quality issues in the massification stage, issues of higher education academic management, issues of student cultivation, and issues of ensuring and evaluating higher education quality. Of these, issues of ensuring and evaluating higher education quality have continually been the mainstream of research on Chinese higher education quality, and there has been an increasing trend of emphasizing ensuring internal quality within schools, emphasizing student participation, and evaluating the effectiveness of student learning. Scholars had a renewed focus on the topics of higher education academic management and student cultivation after 2010, and truly improving the quality of higher education will become a future hot topic of study, however there is an urgent need for further study.

We utilized cross tabulation statistics, word frequency counts, and content analysis of research output to conduct a bibliometric study, and used CiteSpace software to depict a knowledge map for research on entrepreneurship education in China from 2004 to 2013. The study shows that, in this duration, the study of Chinese entrepreneurship education experienced a progression through three stages, an "exploratory stage of learning from foreign models," a "developmental stage geared toward employment problems," and a "transformative stage promoting cultivation of student abilities," and featured three primary characteristics, "multidisciplinary fusion," "imbalanced regional distribution," and "policy orientation." In the future, popular fields in the study of entrepreneurship education are to be concentrated in the three areas, namely "entrepreneurship education and talent cultivation," "entrepreneurship education and entrepreneurial environments," and "entrepreneurship education and innovation education."

The present study aims to identify the most productive countries, journals, authors, institutions and the most used keywords in the field of special education during 2011-2020, based on the WoS database. The widespread effects of the papers and how they are related were analyzed with the bibliometric analysis method. The findings of the study showed that the USA is inarguably the most productive country, followed by England and Australia. On the other hand, there was a very strong positive correlation (r = 0.929) between the number of papers published by countries and their h-index, a similar finding was also found to be present between the countries' h-index and GDP per capita (r = 0.790). Moreover, it was found that the journals with the highest quartile (Q1 and Q2) in the field of special education published significantly more papers than the journals with the lowest quartile (Q3 and Q4). Matson, JL (USA), Sigafoos, J (New Zealand) and Lancioni, GE (Italy) were determined as the most prolific authors, respectively. Autism, intellectual disability, and Down syndrome were the phrases most frequently used as keywords. Our findings provide key information regarding the developments that the research direction of special education field has recently taken. This study also serves a potential roadmap for future studies.

As humanity reaches the 5.0 industrial revolution, education plays a critical role in boosting the quality of human resources. This paper reports bibliometric research on emerging TiP during 1993-2022 in the educational field to analyse its development on any level of education during the last three decades. This study employed a Scopus database. The findings are that the trend of TiP publication in educational fields has tended to increase every year during the past three decades and conference paper became the most published document type, the USA is the country which produces the most publications; "Students" being the most occurrences keyword and total link strength. The publication of the TiP is ranked to the Quartile 1, which implies that a publication with the cited performance is a publication with credibility because the publisher has a good reputation. Researchers can find the topics most relevant to other metadata sources such as Web of Science, Publish, and Perish.

In the recent years, the academic paper influence analysis has been widely studied due to its potential applications in the multiple areas of science information metric and retrieval. By identifying the academic influence of papers, authors, etc., we can directly support researchers to easily reach academic papers. These recommended candidate papers are not only highly relevant with their desired research topics but also highly-attended by the research community within these topics. For very recent years, the rapid developments of academic networks, like Google Scholar, Research Gate, CiteSeerX, etc., have significantly boosted the number of new published papers annually. It also helps to strengthen the borderless cooperation between researchers who are interested on the same research topics. However, these current academic networks still lack the capabilities of provisioning researchers deeper into most-influenced papers. They also largely ignore quite/irrelevant papers, which are not fully related with their current interest topics. Moreover, the distributions of topics within these academic papers are considered as varying and it is difficult to extract the main concentrated topics in these papers. Thus, it leads to challenges for researchers to find their appropriated/high-qualified reference resources while doing researches. To overcome this limitation, in this paper, we proposed a novel approach of paper influence analysis through their content-based and citation relationship-based analyses within the biographical network. In order to effectively extract the topic-based relevance from papers, we apply the integrated graph-based citation relationship analysis with topic modeling approach to automatically learn the distributions of keyword-based labeled topics in forms of unsupervised learning approach, named as TopCite. Then, we base on the constructed graph-based paper–topic structure to identify their relevancy levels. Upon the identified relevancy levels between papers, we can support for improving the accuracy performance of other bibliographic network mining tasks, such as paper similarity measurement, recommendation, etc. Extensive experiments in real-world AMiner bibliographic dataset demonstrate the effectiveness of our proposed ideas in this paper. [ABSTRACT FROM AUTHOR]

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains. [ABSTRACT FROM AUTHOR]

Discusses high recall search strategies for undergraduates and how to overcome information overload that results. Highlights include word-based versus visual-based schemes; five summarization and visualization schemes for presenting information retrieval citation output; and results of a study that recommend visualization schemes geared toward mapping and associative thinking. (Contains 63 references.) (Author/LRW)

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Reports on a use study of citations from college freshman composition papers at Western Illinois University (Illinois) that indicated the need for library instruction and library collection building. Tables and graphs indicate the number of citations per bibliography; the number and types of format sources; publication dates; and distribution of topics. (10 references)

Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies.

This article examines issues faced by scholarly societies that are developing and sustaining online paper repositories. It is based on the AERA Conference on Online Paper Repositories, which focused on fundamental issues of policy and procedure important to the operations of online working paper repositories. The report and recommendations address a range of issues and offer recommendations relevant to disseminating scholarly knowledge online. It is meant to serve as useful guidance for scholarly societies that might undertake such an endeavor and for researchers and other readers interested in wider access and use of scholarly work. It also aims to be informative to authors who might consider placing their working papers in an online paper repository.

Describes how students can exploit the power and flexibility of computers to find, organize, and store information gathered while conducting research. Describes how students can use computers to compose, revise, and edit their research papers. (RS)

The restricted time allocated to courses and the immediate need to read literature in English necessitates the teaching of selective reading in many English as a foreign language contexts. This paper reports on one element of an expeditious (quick, effective, efficient and selective) reading course for learners in an English for Academic Purposes (EAP) context. It details how postgraduate students of chemistry in a Tunisian university are taught to read titles of empirical research papers, as this becomes as important as reading abstracts for fast-tracking relevant data. Analysis of the literature in the field of technical writing identifies four types of titles. Based on this classification, the course component aims at raising the learners' awareness of the linguistic exponents that typify titles, and making them appreciate the importance of titles and their classification. The paper details how this single strategy fits into an expeditious approach to the teaching of reading.

Rosetta, an indexing and retrieval system indexes papers in a collection based on the way they have been described when referenced by other papers in the collection. Using Rosetta, simple natural language queries retrieve high-precision results in which descriptions based on citations clearly summarize retrieved papers, allowing users to quickly determine which papers most clearly meet their information needs. (Author/AEF)

Maturity models have been introduced over the last five decades as guides and references for information system (IS) management in organizations from different sectors. In the education sector, maturity models have also been used to evaluate higher education institutions (HEIs) across several dimensions, such as information and communication technology (ICT), management, process management, course curricula, course/HEI accreditation, e-/m-learning, online courses and pedagogical strategies. In fact, HEIs are faced with enormous complexity due to the specificity and quantity of their processes. Based on the guidelines concerning a methodology for a systematic literature review, the maturity models of different subareas of education are identified and categorized in this paper. For each maturity model associated with HEIs' ISs, the appropriate methodology for development and validation is identified, as well as the scope, stages and their dimensional characteristics. This study highlights the need to develop an IS maturity model that supports HEIs in their core business activities and management.

Examines whether the paper-medium structure of the major classics bibliography, "L'Annee Philologique", validly reflects information retrieval paradigms of individual classicists. Faculty and graduate student attitudes toward and use of features of the paper-medium form are discussed, and attitudes toward the upcoming CD-ROM version are examined. (Author/LRW)