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Purpose: This study aims to conduct a thorough examination and bibliometric analysis of the scholarly articles on PjBL (Project-Based Learning) and 21st-century skills published from 2020 to 2023. The analysis will specifically concentrate on the global distribution of these articles and identifying any prevailing research patterns or trends. Methodology: Utilizing "PjBL" and "21st-century skills" as search terms, the Scopus database was employed for the analysis encompassing annual publications, nations, institutions, authors, journals, references, and keywords in the field. This analysis was facilitated by VOSviewer and Microsoft Excel 2019 software. Findings: There has been a significant increase in publication volume between 2020 and 2023. Among the 2,472 articles within the Scopus database by the end of October 2023, Indonesia emerged as the primary contributor of publications among all nations. The VOSviewer indicates that the key issues revolve around, project-based learning (PjBL), pedagogy, critical thinking, education, sustainability, higher education, and 21st-century skills. Significance: A comprehensive analysis of the global landscape concerning project-based learning (PjBL) and, between 2020 and 2023, aims to uncover pivotal trends, emerging issues, influential authors, and seminal works in educational research. The anticipated outcomes hold the potential to offer multifaceted insights into PjBL and 21st-century skills, contributing to the exploration of innovative educational methodologies. Additionally, this study's investigation of specific research inquiries aims to provide a holistic overview, further enriching the understanding of these crucial educational paradigms.

Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that enables users to store, query, and analyze large datasets. It also explored the benefits and applications of both tools and their integration with other platforms and services. The project offers an introductory insight into Tableau's functionalities, employing a data file from the US Census Bureau via Google BigQuery.

We examined how Learning Analytics literature represents participants from diverse societies by comparing the studies published with samples from WEIRD (Western, Industrialized, Rich, Democratic) nations versus non-WEIRD nations. By analyzing the Learning Analytics studies published during 2015-2019 (N = 360), we found that most of the studies were on WEIRD samples, with at least 58 percent of the total studies on WEIRD samples. Through keyword analysis, we found that the studies on WEIRD samples' research topics focused on self-regulated learning and feedback received in learning environments. The studies on non-WEIRD samples focused on the collaborative and social nature of learning. Our investigation of the analysis tools used for the studies suggested the limitations of some software in analyzing languages in diverse countries. Our analysis of theoretical frameworks revealed that most studies on both WEIRD and non-WEIRD samples did not identify a theoretical framework. The studies on WEIRD and non-WEIRD samples convey the similarities of Learning Analytics and Educational Data Mining. We conclude by discussing the importance of integrating multifaceted elements of the participant samples, including cultural values, societal values, and geographic areas, and present recommendations on ways to promote inclusion and diversity in Learning Analytics research.

The aim of this study is to reveal the bibliometric profile of articles published in the field of science education and technology. In the study, in which descriptive research model was used, bibliometric analysis method was preferred in order to reveal and examine the research trends of scientific publications on science education and technology. In the study, Web of Science Core Collection was preferred as the database and VOSviewer program was preferred as the bibliometric network analysis program. In the Web of Science Core Collection database, 2485 articles were accessed after searching with the keywords "science educat*" and "technology*". As a result of the research, it was determined that the first article in the relevant subject area was published in 1980 and the most articles were published in 2022. The country with the highest number of publications in the related field is the USA. The researcher who published the most in the related field is Franz Xaver Bogner. The most publications were published in the Journal of Science Education. Most frequently used keyword in the related articles is science education. The study is important in terms of revealing the research trend of science education and technology articles.

The use of #digitalpowerups is a technique that involves associating keywords with prompts in online discussion forums, which enables students to have more choice and voice. These powerups not only help structure responses but also enrich discussions and develop academic skills necessary for online assignments. The approach leverages the social media interaction space of discussion forums by introducing hashtags that remind students of the prompt being addressed and indicate the level of Bloom's being engaged. By using the powerups, students can engage in mid-levels and higher-order levels of Bloom's, along with the lower levels that they typically engage in based on the design and facilitation of the discussion. Students typically participate in the lower levels of Bloom's taxonomy (#remember, #understand) due to the way the discussion is structured and facilitated. However, the use of #digitalpowerups encourages students to move beyond these levels and engage in mid-levels (#apply, #analyze, #evaluate) and higher-order levels (#create, #connect) during discussions. The powerups also scaffold or frame student responses with habits of mind skills. This article examines how the #digitalpowerups strategy supports learning in a virtual community for Environmental Concerns in the Environmental Sociology Course through content analysis of student discussion postings. The primary data (total of 375 postings) were collected from the Environmental Sociology Class throughout the following academic years: Fall 2020 (96 postings), Fall 2021 (67 postings), Spring 2022 (92 postings) and Spring 2023 (120 postings).

This study aims to examine the studies on computer-assisted teaching in science teaching in terms of different variables with bibliometric analysis. Scopus database was used to collect the data. The phrase "science education and computer-assisted teaching" was written in Scopus on 11_07_2023, and 1232 publications were found. Since not all data is related to computer-assisted teaching in science education, the Scopus Social Science section was selected and filtered, and 454 publications were subjected to bibliometric analysis. VOSviewer program was used for bibliometric analysis. As a result of the research, most studies on computer-assisted teaching in science education were done in 2010, most articles were preferred as document type, the most frequently used keyword was computer-assisted instruction, the country with the most publications was the United States, and the most cited publication was Leidner and Jarvenpaa (1993) and the most frequently used term is student.

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.

This study aims to create a comprehensive bibliometric map of published scientific articles on e-learning in mathematics education (ME). We used Web of Science (WoS) database to analyzed 341 articles published by 1018 authors representing 79 countries between 2012 and 2022. In this context, we examined scientific articles in terms of scientific production, network analysis, trend topics, thematic change, and conceptual structure. Bibliometric analysis was performed in the study. According to findings, the number of published articles differ according to the years, but the number of citations is constantly increasing. Albano, and Dello Iacono are the most prolific authors. The most productive institutions are Salerno and Black Sea Technical Universities. China, USA, and Russia lead the countries of the corresponding authors. According to the network analysis, the journals of Computers & Education and Computers in Human Behavior form strong links. The most used keywords are students, technology, and mathematics. The pandemic, COVID, reality, and process are trend topic titles. It determined that the best size reduction obtained in the conceptual analysis constituted approximately 31% of the total variability. We presented some suggestions based on the findings obtained at the end of the research.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.

Despite growing interest in spaced retrieval methods, a research gap has been discovered, with insufficient data to support best practises in year-one science. After analysing five systematic action cycles, four interrelated themes emerged: dual coding, cognitive load, peer communication, and feedback-driven metacognition. While literature emphasises the need to take a child's cognitive load into account, the themes of peer communication and dual coding provoked debate among participants. Despite this conflict, an agreement was reached on how limited reading abilities affect retrieval practise. While this small-scale study cannot draw definitive conclusions, I discovered that by optimising cognitive load, teachers can ensure that a task is desirably difficult using dual coding, discussion, and feedback-driven metacognition, all of which contributed to the success of knowledge acquisition methodologies. According to my research, ensuring a retrieval task is challenging, increases children's development of retention of scientific knowledge. However, minimising literacy constraints is critical to avoid thinking being exhausted by writing or reading, rather than recalling and applying. Consequently, the relevance of the study for education and society is illustrated by the findings, which emphasise the necessity to reduce extraneous load (reading and writing) during retrieval tasks and propose potential solutions to this issue.

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.

A scientometric analysis and systematic review of an immersive virtual learning environment (IVLE) designed to improve the development of hard skills comprised this study. The review focused on the period between 2016 and 2023 and was conducted using the Scopus database. This study employed a scientometrics analysis and systematic review methodology. In this study, a comprehensive analysis was conducted on a corpus of 12 scholarly articles that have been published within the last 8 years. This study looked at the aggregate magnitude and trajectory of scholarly publications and looked at a number of trends in scholarly research, including annual reports, article counts, article distribution across different sources, finding the most useful sources, keyword analysis, looking into the best collaboration groups, looking at how themes have changed over time, judging contributions, and practical implications. The primary findings regarding the data and document types indicated that a total of 12 articles were published across 10 different sources within the timeframe of 2016 to 2023. The research conducted on IVLE for the development of hard skills exhibited fluctuations in its pace, alternating between deceleration and positive acceleration. The growth rate of the articles peaked in 2022. The primary publication with the greatest of source is "Lecture Notes in Networks and Systems." Keyword Plus growth rate for terms like "virtual reality," "e-learning," and "teaching." The nations exhibit a high degree of collaboration in the field of research. The findings indicated that the authors hailing from Mexico and Colombia exhibited the greatest frequency (n = 2). The thematic evolution of the study indicated two significant advancements: firstly, the emergence of e-learning studies that have propelled the field of e-learning, and secondly, the integration of virtual reality into e-learning practices. The classification of applications encompasses various components: title, solution, learning strategy, and learning tasks.

This research aims to analyze the results of studies conducted in Turkey on reasoning skills in mathematics teaching and to reveal what kind of trend there is in this field. Within the scope of this study, databases were searched with the keywords "reasoning"(muhakeme, akil yürütme) and "reasoning skill" (Muhakeme becerisi, akil yürütme becerisi), and the results were examined in accordance with the inclusion criteria regarding mathematics teaching. One hundred sixty-three studies were included. Each of the studies included in the meta-synthesis study was analyzed descriptively according to type, year, method, sample type and size, data collection tools, statistical analysis, learning field, keywords, reasoning type and purpose. In addition, the studies' results were content analyzed and tabulated by coding the differences and similarities between them with a holistic approach. It has been determined that studies on the learning fields in the mathematics curriculum are mostly carried out in the field of learning numbers and operations in the secondary school mathematics curriculum. When evaluated in terms of reasoning types, almost half of the studies were conducted on mathematical reasoning. It has been observed that after mathematical reasoning, the most focus is on proportional reasoning. When the aims of the studies included in the research were examined, it was determined that the most focused ones were "examining the factors affecting reasoning skills", "measurement of reasoning skills" and "the effect of teaching practices on reasoning skills". In the studies examined, it was seen that there were 33 teaching practices whose effects on reasoning skills were examined.

Student learning outside the formal classroom is inextricably linked to informal learning environments. In many countries, most activities that employ informal learning prioritize the integration of science, technology, engineering, and mathematics (STEM) disciplines and have shown a positive impact on increasing students' interest, self-efficacy, and awareness of the STEM field. Thus, this study aims to systematically review the activities reported in the relevant studies focusing on informal STEM learning for K-12. High-index journals published under SCOPUS and Web of Science databases were utilized using a predetermined search strategy and retrieved two research team members' screened articles. Only empirical studies containing the terms "STEM education", "summer camp", and "informal learning" in the title, abstract, and keyword were included. Data were coded and organized into a matrix that was qualitatively assessed and categorized into themes. Based on the 25 studies reviewed, it was found that the integration of STEM disciplines in informal learning is notably varied. The characteristics of the STEM activities can be organized into seven themes: inquiry, focus on problems, design, cooperative learning, student-centred, hands-on, and 21st-century skills. Practice recommendations include a quality curriculum that aligns with informal STEM learning needs.

Research using school discipline and infraction data has contributed to public policy conversations by helping elucidate the effects of and disproportionate experience of school disciplinary outcomes. This research brief presents results from an analysis of the public availability of such data from state departments of education. Findings suggest that while public availability of discipline data has not changed significantly over the past decade, states are more likely to disaggregate such data by subgroups. Unfortunately, such data remain generally focused on a small number of exclusionary practices rather than nonpunitive or nonexclusionary alternatives. Infraction data are slightly less available than discipline data and significantly less likely to be disaggregated by subgroup.

In this crossover experiment, we investigated the impact of a statement bank, enabling the reuse of previously written feedback (SA condition), on 45 math teachers' feedback for 60 completed linear equation tests, compared to traditional pen-and-paper feedback (PP condition). In the SA condition, teachers were encouraged to use atomic feedback, a set of formulation requirements that makes feedback items significantly more reusable. A previous study found that significantly more feedback was written in the SA condition but did not investigate the content of the feedback. To address this gap, we employed a novel approach of combining text mining with qualitative methods. Results indicate similar wording and sentiments in both conditions. However, SA feedback was more elaborate yet general, focusing on major and minor strengths and deficits, while PP feedback was shorter but more concrete, emphasising main issues. Despite low feedback quality in both conditions, the statement bank led to less effective diagnostic activities, implying that teachers' careless use of statement banks, although convenient, might lead to lower-quality feedback.

This paper analyses the methodologies in 148 empirical data-driven learning studies for L2 English in prestige journals to examine best practice. Manual coding and corpus analysis of key words and n-grams from the past 5 years (2018-2022) explore the field as a whole and how methodologies have evolved, suggesting improvements and future avenues for research.

Background: The emergence of ChatGPT in the education literature represents a transformative phase in educational technology research, marked by a surge in publications driven by initial research interest in new topics and media hype. While these publications highlight ChatGPT's potential in education, concerns arise regarding their quality, methodology, and uniqueness. Objective: Our study employs unconventional methods by combining altmetrics and bibliometrics to explore ChatGPT in education comprehensively. Methods: Two scholarly databases, Web of Science and Altmetric, were adopted to retrieve publications with citations and those mentioned on social media, respectively. We used a search query, "ChatGPT," and set the publication date between November 30th, 2022, and August 31st, 2023. Both datasets were within the education-related domains. Through a filtering process, we identified three publication categories: 49 papers with both altmetrics and citations, 60 with altmetrics only, and 66 with citations only. Descriptive statistical analysis was conducted on all three lists of papers, further dividing the entire collection into three distinct periods. All the selected papers underwent detailed coding regarding open access, paper types, subject domains, and learner levels. Furthermore, we analysed the keywords occurring and visualized clusters of the co-occurring keywords. Results and Conclusions: An intriguing finding is the significant correlation between media/social media mentions and academic citations in ChatGPT in education papers, underscoring the transformative potential of ChatGPT and the urgency of its incorporation into practice. Our keyword analysis also reveals distinctions between the themes of the papers that received both mentions and citations and those that received only citations but no mentions. Additionally, we noticed a limitation that authors' choice of keywords might be influenced by individual subjective judgements, potentially skewing results in thematic analysis based solely on author-assigned keywords such as keyword co-occurrence analysis. Henceforth, we advocate for developing a standardized keyword taxonomy in the educational technology field and integrating Large Language Models to enhance keyword analysis in altmetric and bibliometric tools. This study reveals that ChatGPT in education literature is evolving from rapid publication to rigorous research.

Background: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. Methods: A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. Results: Three search filters were created in MEDLINE: a "precision-maximising" filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a "sensitivity-maximising" filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions. Conclusion: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

The research aims to analyse the most current (2015-2023) literature on STEM education to gain a deeper understanding of current trends, priorities and developments in the field of STEM education. Scientific studies determined within the scope of the study were examined using bibliometric and content analysis methods, which are among the systematic compilation methods. First, access to the WoS database was gained, and a subject-based search was conducted using the keywords "STEM education" and "Meta-analysis." In this context, 38 scientific studies containing these relevant keywords were found in the search. The collected data were initially analyzed on the basis of descriptive attributes in the WoS. Subsequently, the listed publications were analyzed using the bibliometric analysis tool VOSviewer for bibliographic attributes, including citation counts, co-citation, co-authorship, cooccurrence, and bibliographic coupling analysis types, based on authors, institutions, and countries. Finally, 18 articles from these scientific publications were evaluated through content analysis in terms of various characteristics of following the PRISMA flowchart. The most prominent keywords associated with the examined concepts included technology-related terms like "computer-based learning," "digital game-based learning," "academic achievement," "active learning," and "problem-centered instruction." Furthermore, the general focus of scientific studies appears to be on determining the impact of a STEM education method on student performance.

We report the first evidence that the gist mechanism of fuzzy-trace theory and the associative mechanism of activation monitoring theory operate in parallel, in the recall version of the Deese/Roediger/McDermott illusion. In three experiments, we implemented a new methodology that allows their respective empirical indexes, gist strength (GS) and backward associative strength (BAS), to each be manipulated while the other is held constant. In Experiment 1, increasing GS increased false recall of missing words, but increasing BAS did not. In Experiments 2 and 3, however, increasing GS and increasing BAS both increased recall of missing words, and those effects were independent and additive. In all three experiments, GS and BAS affected true recall of list words in qualitatively different ways: (a) Increasing GS always improved true recall, regardless of whether BAS was high or low, but (b) increasing BAS impaired true recall when GS was high and improved true recall when GS was low. To pinpoint the retrieval loci of the two variables' effects, we analyzed the data of all experiments with the dual-retrieval model. Those analyses showed that the variables' respective effects were due to different retrieval processes.

Much of the data produced and consumed by students, teachers, and educational managers is in textual format. Text Mining (TM) and Natural Language Processing (NLP) have been applied in the educational context in different ways. Ideally, such applications combine computational, linguistic, pedagogical, and psychological aspects. This article aims to gather and analyze scientific publications that have applied TM and NLP techniques in textual corpora from distance-higher education through a Systematic Literature Review. Eight scientific databases were searched (ACM DL, Scopus, Web of Science, IEEE Xplore, ArXiv, SpringerLink, ScienceDirect, and ERIC), and publications from 2017 to 2021 were selected. 718 unique publications were screened to identify primary research capable of characterizing this scientific area. 52 resulting publications were fully analyzed, and some consolidated results include: 38% of works had the professors as end users, followed by students (27%) and managers (25%); the English language was present in 50% of publications, followed by the Portuguese language (13,5%) and others languages; the text mining tasks most used were text classification (27%), sentiment analysis (17%), information extraction (15%), chatbot (15%) and topic modeling (13%); LDA (Latent Dirichlet Analysis) was the technique most used (19%); the Python language was the programming language most prevalent (42%), and 54% of works do not mention any educational construct or theory. Thus, this article presents an unprecedented overview of the field of Educational Text Mining (ETM) in distance higher education and analyses the main results obtained, aiming for future research in the area.

Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this article, we demonstrate extraction of a problem space through text mining in R. Text mining, an artificial intelligence form of natural language processing, synthesizes and summarizes vast quantities of verbal data. Text mining provides visualization of large narrative datasets to illustrate the structure and connections within the problem space of an ill-structured problem. The Gates Open Research data set of 11,979 verbal autopsy responses (Flaxman et al., 2018) informs the ill-structured problem space of childhood death from infectious disease in developing nations. In this article we apply text mining to automate the process of identifying connections that efficiently illustrate this problem space.

Purpose: The word learning of preschool-age children with developmental language disorder (DLD) is improved when spaced retrieval practice is incorporated into the learning sessions. In this preregistered study, we compared two types of spacing--an expanding retrieval practice schedule and an equally spaced schedule--to determine if one of these approaches yields better word learning outcomes for the children. Method: Fourteen children with DLD aged 4-5 years and 14 same-age children with typical language development (TD) learned eight novel nouns over two sessions. Spacing for half of the novel words was expanded gradually during learning; for the remaining novel words, greater spacing remained at the same level throughout learning. Immediately after the second session and 1 week later, the children's recall of the words was tested. Results: The children with TD recalled more novel words than the children with DLD, although this difference could be accounted for by differences in the children's standardized receptive vocabulary test scores. The two groups were similar in their ability to retain the words over 1 week. Initially, the shorter spacing in the expanding schedule resulted in greater retrieval success than the corresponding (longer spaced) retrieval trials in the equally spaced schedule. These early shorter spaced trials also seemed to benefit retrieval of the trials with greater spacing that immediately followed. However, as the learning period progressed, the accuracy levels for the two conditions converged and were likewise similar during final testing. Conclusion: We need a greater understanding of how and when short spacing can be helpful to children's word learning, with the recognition that early gains might give a misleading picture of the benefits that short spacing can provide to longer term retention.

Educational Data Mining (EDM) is an interdisciplinary field that encapsulates different fields such as computer science, education, and statistics. It is crucial to make data mining in education to shape future trends in education for policymakers, researchers, and educators in terms of developments. To have an all-inclusive understanding of EDM studies, a comprehensive examination of both the intellectual and social structure of the field with a global perspective and its evolution over time is required to provide adequate comprehension of the past, present, and as well future research direction for this research field. In this respect, this research study aims to explore the performance analysis of the EDM, the intellectual structure of the EDM, the social structure of the area, and the temporal modeling of the EDM through bibliometric analysis. In this bibliometric analysis, the existing body of knowledge that covered 2010-2021 was presented on educational data mining to provide future directions in the field of education. The results of the study showed the number of publications increased by 1325% (8 to 114) over the years. It has been determined that the most influential journals in the field are "Computers & Education", "International Journal of Advanced Computer Science and Applications" and "Educational Technology & Society" and the most influential authors are "C. Romero", "S. Ventura" and "R.S. Barker", and the USA, Spain, and China seem to be the most influential countries in the field of EDM. The themes of "CLASSIFICATION" and "SYSTEMS" in the first sub-period (2010-2013) of EDM, and "LEARNING ANALYTICS", "PREDICTION OF ACADEMIC PERFORMANCE" and "SOCIAL MEDIA" in the second sub-term (2014-2017) and the third and last sub-period (2018-2021), "PREDICTION OF ACADEMIC PERFORMANCE", "MACHINE LEARNING", "PROCESS MINING", "PARTICIPATION", "KNN", "J48" and "BAYESIAN NETWORK" themes were identified as engine themes. In addition, as a result of the thematic evolution map, it was discovered that the themes "DEEP-LEARNING (DL)" and "EMOTION" are emerging themes for future studies for shaping the future of education based on sustainable goals.

Information design--incorporating research in graphic design, typography, visualization, and usability--is a user experience practice directly applicable to contemporary museums, libraries, and archives. Information design principles and guidelines improve engagement at every point of service, effectively and efficiently complementing the mission of knowledge organizations. This historical survey explores information design in the context of contemporary user experience design, and provides an overview of information design principles and guidelines developed over 40 years of research and professional application. Applying elements of information design to archival finding aids enhances usability, while also preserving descriptive and contextual structures.

Previous research reveals that the visual design of open-ended questions should match the response task so that respondents can infer the expected response format. Based on a web survey including specific probes in a list-style open-ended question format, we experimentally tested the effects of varying numbers of answer boxes on several indicators of response quality. Our results showed that using multiple small answer boxes instead of one large box had a positive impact on the number and variety of themes mentioned, as well as on the conciseness of responses to specific probes. We found no effect on the relevance of themes and the risk of item non-response. Based on our findings, we recommend using multiple small answer boxes instead of one large box to convey the expected response format and improve response quality in specific probes. This study makes a valuable contribution to the field of web probing, extends the concept of response quality in list-style open-ended questions, and provides a deeper understanding of how visual design features affect cognitive response processes in web surveys.

The literature search underpins data collection for all systematic reviews (SRs). The SR reporting guideline PRISMA, and its extensions, aim to facilitate research transparency and reproducibility, and ultimately improve the quality of research, by instructing authors to provide specific research materials and data upon publication of the manuscript. Search strategies are one item of data that are explicitly included in PRISMA and the critical appraisal tool AMSTAR2. Yet some authors use search availability statements implying that the search strategies are available upon request instead of providing strategies up front. We sought out reviews with search availability statements, characterized them, and requested the search strategies from authors via email. Over half of the included reviews cited PRISMA but less than a third included any search strategies. After requesting the strategies via email as instructed, we received replies from 46% of authors, and eventually received at least one search strategy from 36% of authors. Requesting search strategies via email has a low chance of success. Ask and you might receive--but you probably will not. SRs that do not make search strategies available are low quality at best according to AMSTAR2; Journal editors can and should enforce the requirement for authors to include their search strategies alongside their SR manuscripts.

This scoping review aims to identify and systematically review published mapping reviews to assess their commonality and heterogeneity and determine whether additional efforts should be made to standardise methodology and reporting. The following databases were searched; Ovid MEDLINE, Embase, CINAHL, PsycINFO, Campbell collaboration database, Social Science Abstracts, Library and Information Science Abstracts (LISA). Following a pilot-test on a random sample of 20 citations included within title and abstracts, two team members independently completed all screening. Ten articles were piloted at full-text screening, and then each citation was reviewed independently by two team members. Discrepancies at both stages were resolved through discussion. Following a pilot-test on a random sample of five relevant full-text articles, one team member abstracted all the relevant data. Uncertainties in the data abstraction were resolved by another team member. A total of 335 articles were eligible for this scoping review and subsequently included. There was an increasing growth in the number of published mapping reviews over the years from 5 in 2010 to 73 in 2021. Moreover, there was a significant variability in reporting the included mapping reviews including their research question, priori protocol, methodology, data synthesis and reporting. This work has further highlighted the gaps in evidence synthesis methodologies. Further guidance developed by evidence synthesis organisations, such as JBI and Campbell, has the potential to clarify challenges experienced by researchers, given the magnitude of mapping reviews published every year.

In order to investigate the influence of separation of grade distributions and ratio of common items on the precision of vertical scaling, this simulation study chooses common item design and first grade as base grade. There are four grades with 1,000 students each to take part in a test which has 100 items. Monte Carlo simulation method is used to simulate the response matrices by self-made program in R 3.0. As the items are scored by 0/1, we select two-parameter logistic model of Item Response Theory and use BILOG-MG for concurrent calibration with EAP method. The Bias and RMSE are calculated as precision indicators. The results show that: (1) Estimation precision of item and ability parameters differs in different grades. For discrimination and difficulty parameters, estimation precision is higher as closer to the base grade and is lower with the increase of effect size. For the ability parameters, the estimation precision is high generally except for fourth grade which is much lower. The precision is best at 0.5 of effect size in general. (2) There is an interaction between the ratio of common items to total test and effect size. When the effect size is 0.5 and 1.0, estimation precision of each grade is most accurate at 30% of common-item ratio. When the effect size is 1.5, the estimation precision of difficulty parameters is best for first, second, and third grade at 30% of common-item ratio while grade 4 at 15% of common-item ratio. The ability parameters of all grades are all best estimated at 15% of common item ratio. There must be a trade-off between the estimation precision of ability parameters and item parameter if the common item ratio is at the range of 15% to 30%. (3) The choice of base grade affects the accuracy of vertical scaling. When the lower grade is selected as the base grade, if the number of consecutive cumulative conversions from the upper grade test score to the lower grade exceeds 2, there will be a large deviation. Therefore if the senior grade changes to the junior grade, it is suggested that the gap of grades should not exceed 2 grades. As a whole, the proportion of anchor items for vertical scaling is set at 30%, but it is better to set the proportion of anchor items as "variable" value (15%-30%) when considering the separation of grade distributions.

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the "user-item" latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students' subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students' self-directed learning efficiency.

This article introduces the CELI corpus, a new learner corpus of written Italian consisting of ca. 600,000 tokens, evenly distributed among CEFR (Common European Framework of Reference for Languages) proficiency levels B1, B2, C1 and C2. The collected texts derive from the language certification exams administered by the University for Foreigners of Perugia all around the world. The corpus contains rich metadata pertaining to text-related and learner-related variables. It expands the domain of learner corpora by being, among other things, both freely available online to the research community, and by focusing on a target language other than English. The article also presents and evaluates the POS-tagging procedure, thus contributing to best practices in learner corpus annotation.

Throughout the period of pandemic, many studies have been conducted on emergency remote teaching (ERT) in different fields and from different perspectives, which reveal that there has been a lack of a comprehensive map showing the rapid and continuous responses of these studies to the process. The purpose of this research is to analyze open access research on ERT using bibliometric method, and to reveal current trends in this field. VOSviewer software was used for data analysis; the data collection process was shaped using the PRISMA framework. 238 studies were included in the analysis. The distribution of the open access studies analyzed in the field of emergency remote teaching by year, type of publication, subject, country, and sources was examined; citation analysis (by journal and publication), authorship patterns and collaboration, common word analyses are included. It was found out that these open access publications mainly consisted of journal articles and were dated 2020 and 2021; most of the publications were in the field of educational sciences. Based on common word analysis, the most important topics that are addressed in studies on the ERT process are the process of pandemic, distance education and higher education, while the challenges experienced regarding teachers and pedagogic issues during the process, teacher education, student-related characteristics (such as self-regulated learningmotivation-academic success) and participation are found to be frequently studied topics. The concepts of instructional design, collaborative learning, social presence, and assessment are also among the topics covered. It is anticipated that the implications for policy and practice based on the examination of research trends will have a significant effect on the structuring of future online learning environments, as well as the ERT designed for emergencies.

Geometry has an important place in mathematics education because it forms the basis of mathematical thinking and allows us to see the emergence of logical theory. As in all fields, academic journals, which are among the official communication languages of science, play an important role in the construction, dissemination and use of scientific knowledge in the field of geometry teaching. With reference to this fact, the aim of the study was to examine articles published in the field of geometry teaching and indexed in SSCI (1975-2020) and ESCI (2015-2020) in the Web of Science Core Collection database by bibliometric analysis method. 109 articles related to geometry teaching (72 within the scope of SSCI and 37 within the scope of ESCI) were reached for the given periods. According to the findings of the research, while there has been a recent interest in geometry education within the scope of SSCI and ESCI, the number of articles still published is limited. Therefore, in mathematics teaching, geometry teaching lags behind areas such as arithmetic and algebra. Especially in recent years, it is noted that the keyword "Van Hiele Levels" and the keywords of innovations that technology brings to geometry education such as "Dynamic Geometry", "Virtual Reality", "Geogebra" and "Geometry Thinking" are frequently used in articles in the field of geometry teaching. It is observed that, while quantitative research methods are widely preferred in studies within the scope of SSCI in the field of geometry teaching, qualitative research methods are preferred within the scope of ESCI.

Bibliometric reviews of research have gained increased popularity since the mid-2010s. Yet, many readers may be uncertain as to the purpose and method of bibliometric reviews. This article provides an introduction to bibliometric reviews, clarifies their purpose, and elaborates on methods of data collection, analysis, and interpretation. The latter half of the article is devoted to an illustrative, state-of-the-science, bibliometric review of research on problem-based learning (PBL). In this illustrative review, the author applied descriptive statistics, citation analysis, and co-citation analysis to a database of 5,764 Scopus-indexed documents on problem-based learning published between 2017 and 2022. The analyses found increasing diversity of the PBL knowledge base with respect to geographical sources, subject area origins, and focus of the research compared with findings reported in past reviews. Author co-citation analysis visualized the intellectual structure of the recent PBL literature revealing four dominant schools of thought: Interdisciplinary PBL Theory and Practice, Active Learning, Social and Experiential Learning, and PBL Process. The findings highlight the continuing growth and spread of PBL, as well as a trend of integration with other methods of experiential and active learning.

The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data to predict their academic performance. In this study, this interaction information is called "week 0" data. The results suggest that "week 0" interaction data can be used to identify the academic success of online students and predict first assignment performance.

Mathematical creativity is among the most intriguing research fields in the world. This is plausible because research on mathematical creativity, particularly in the field of education, has a positive impact on many dimensions of life. Even though numerous studies have been conducted on this topic, there are still many aspects that have not been examined. Using bibliometric analysis, the authors of this study evaluated scientific articles on mathematical creativity from 2002 to 2022 that were indexed in Scopus using Biblioshiny and VOSviewer. The authors analyzed 162 publications in terms of document distribution patterns and growth trends, contributions and impacts from countries, institutions, authors, and journals, patterns of development and evolution of the theme of mathematical creativity, and future research opportunities. Despite a decrease in the average number of citations per document, the results suggest a significant increase in the number of publications between 2002 and 2022. The United States and the University of Haifa are the nations and institutions with the highest publication output, ZDM-Mathematics Education has the highest impact, and Bicer is a core author who is extremely productive and influential. "Creativity" has been the most popular keyword over the past two decades, but it is not the only one. This study encourages future research on mathematical creativity in mathematics education to not only focus on the theme of discipline-specific instructional practices, but also on the theme of general instructional practices involving more person, process, product, and press/environment creativity.

This research aims to analyze the past literature on blended learning in higher education and investigate the research trends on this subject. Thus, it aims to present a roadmap for future studies. In this context, bibliometric and descriptive analysis methods were used in the study carried out with the descriptive survey model. 1970 studies were accessed using the Web of Science (WoS) database to reach the data within the scope of the research. As a result of the filtering process on the database, the distribution of the relevant publications by year, document type, publication language, country and WoS indexes, the most influential institutions and research, cooperation between institutions and countries, the most cited authors, and the most studied topics were reviewed. According to the research results, the studies on blended learning in higher education were primarily published in the form of articles in English between the years 2002-2021. It is also deduced that Spain stands out, especially in producing publications, and these studies are generally published in the Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) index type. The most active journal with high citation density is Computers & Education, and the country that collaborates most is England. Regarding the keywords used in the articles, while the concepts of online learning, higher education, and student participation are prominent in the studies conducted in the first years, the concepts such as flipped classrooms, Edmodo, sustainability, gamification, mobile learning, and emotions came to the fore in the following years. In this context, discussions were conducted within the framework of the literature, and suggestions were made related to the findings obtained.

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

While using artificial intelligence in education is a popular field of study for researchers, it has become a joint application for educational institutions. Educational institutions are trying to establish artificial intelligence-based systems to improve the existing education systems. On the other hand, education researchers want to determine which artificial intelligence models are the most effective. To provide an in-depth resource for both researchers and educators on the use of artificial intelligence in education, this study aims to make a bibliometric analysis of articles related to artificial intelligence in education. After the query was made in the Web of Science database, 1153 articles related to the subject were obtained. As a result of the bibliometric analysis of the articles obtained, the most influential journals are Education and Information Technologies and Computers & Education, and the most influential authors are Scouller, Biggs, and Hwang. After 2019, it has been observed that there has been a significant increase in the number of studies, the first examples of which were found in 1985. It is thought that this study, which provides results on the most cited publications, trending topics, thematic map of keywords, and co-occurrence network, will serve as a bedside resource for both educators and researchers. Implications of the findings for theory and practice are discussed.

This study's main objective is to present bibliometric data on articles related to digital educational environments (DEEs) published in the journals indexed in Scopus database between 2003 and February 2023. The data collected from 61 articles published within the study's scope were subjected to bibliometric analysis based on six categories: number of articles and citations, most cited articles, most used keywords, most influential countries, most-important institutions, and the most important journals. This study was designed as a descriptive study and offers bibliometric network maps of the most popular papers, keywords and countries, institutions, and journals. VOS viewer was used to create network diagrams and bibliometric analyses. The bibliometric analysis showed that the most frequently used keywords were digital learning environment(s), digital educational environment. The most prolific authors related to DEE are Antón-Sancho, Vergara, Barana, and Marchisio. The results showed that DEE studies were started in 2003 and the most articles were published in 2021. This study presents a global perspective on DEEs and proposes vision for future research.

This study investigates the trends of doctoral dissertations produced in Türkiye in the field of Educational Technologies. 292 doctoral dissertations were included in this study which was conducted in the design of document analysis in the period between 2011 and 2020. The dissertations were put to content analysis by using the Dissertation Analysis Form developed within the scope of this study. The results demonstrated that the approach of the mixed method research was more commonly used in the dissertations. As a research model, it has been determined that case study from qualitative approaches and quasi-experimental model from quantitative approaches are mostly preferred. It is seen that the students of the faculty of education are greatly used as the sample. It is concluded that descriptive statistical methods are mostly used during the data analysis phase and the SPSS program is the most frequently preferred data analysis program. It has been determined that simple level statistical analysis techniques are used in theses. It is clear that although effect size calculations were made, there were also studies in which the type was not specified. Of the statistical techniques used for validity and reliability, Cronbach's Alpha internal consistency coefficient was found to be frequently used. 1376 keywords emerged in the analysed theses. These keywords were presented visually using a force-directed algorithm. Stating sampling methods clearly in studies will guide the studies to be conducted in the future. The sampling of different types of students is important in that it enriches the potential areas of study paving the way for the emergence of different perspectives.

This study is designed to reveal the research trends of graduate theses published in the field of computer programming in K-12 between 2018 and 2022. Document analysis was used for data collection in this study. The data was divided into 9 categories, and the results demonstrated that the scholars in the Departments of Computer Educational and Instructional Technologies (CEIT) at Atatürk University and Ondokuz Mayis University of Türkiye mainly published master's theses in this field in 2019. Another result was that though CEIT is also the main department for doctoral theses, no difference exists in terms of years or universities. "Teaching, learning, and training programming" and "computational thinking" are the most frequently used keywords in graduate programs, respectively. While quantitative and mixed method (parallel, convergent), convenience sampling, and psychometric scales are preferred in master's theses, mixed method (explanatory, mixed), purposive sampling, interviews/focus group interviews, and psychometric scales are preferred in doctoral theses, respectively. Observation and semi-structured interview forms are used as data collection tools in the theses. Although quantitative methods are mainly utilized in data analysis, parametric and non-parametric statistical tests are also used, and content analysis is resorted to as part of qualitative methods. Whilst validity and reliability studies have been carried out measurement tools are employed in master's theses, scales have been developed in doctoral theses. The present study also offers some suggestions for future research on teaching computer programming.

The purpose of this study is to examine the publications conducted in education related to virtual worlds with the bibliometric analysis method. This study employed the appropriate keywords determined via digging the Web of Science database. A total of 2,978 publications meeting the criteria of the current study were examined. VOSviewer software was used for the analysis. We utilized co-authorship analysis to see the descriptive characteristics of the examined publications. The most conducted type was proceedings and the number of publications fluctuated over time with a peak in 2012. England, USA, and Spain had the most publications among the countries, and Open University, Coventry University, and the Polytechnic University of Bucharest had the most publications among the organizations. The majority of the studies were published in the journal of Computers & Education, British Journal of Educational Technology, and Interactive Learning Environments. For revealing the most common keywords, we used the co-occurrence analysis. "e-learning", "second life" and "higher education" were the most occurred keywords in the publications. The citations analysis was used to see which authors and publications were mostly cited in the examined papers. The most cited authors were Dede, C., Lan, Y. and Ketelhut, D. J. According to the analysis of the cited references, Dalgarno and Lee, (2010), Merchant, Goetz, Cifuentes, Keeney-Kennicutt, and Davis, (2014) and Dunleavy et al. (2008) were the most cited studies. Technological developments have affected the interest in the use of VWs in education. As a new type or extension of virtual worlds, metaverse promises new opportunities in education. The findings of this study would present a road map for those future research to see the general picture of the relevant area.

Background/purpose: ChatGPT, a recent form of AI-based language model, have garnered interest among people from diverse backgrounds with its immersive capabilities. Using ChatGPT to support or generate scientific research has also created an ongoing debate over its advantages versus risks. The present study aimed to conduct an AI-enabled research process using ChatGPT so as to evaluate its potential to generate an accurate, clear, concise, and unbiased information as these are essential elements of rigorous scientific work. Materials/methods: To achieve this aim, we worked on emergency remote teaching (ERT), which garnered significant interest due to its wide-spread use during the COVID-19 pandemic, and created opposing views, particularly in comparison to online teaching. We conducted a simultaneous query on ChatGPT-3.5 and 4 on five basic themes: (1) the definition and emergence of ERT, (2) the appropriateness of ERT for different grade levels, (3) a comparison between ERT and online teaching, (4) the possible outcomes of ERT, (5) the future prospects and uses of ERT, and we performed a comparative evaluation of these responses with regard to accuracy, clarity, conciseness, and potential bias. We also used Cohen's kappa to assess inter-rater agreement in our analysis. Results: The results indicated that both versions were capable of generating accurate information without significant bias although the responses lacked depth and insight with being somewhat repetitive. As the level of judgment required by the query increased, the performance of ChatGPT-4 was much better; it provided clearer and more concise responses with a more synthesized and detailed categories of information on ERT. Conclusion: Based on our results, we state that the cooperation of human and artificial intelligence is still warranted to ensure an accurate and reliable output from AI-based scientific queries. If ChatGPT is a plane with innovative technologies, there still needs to be a pilot in the cockpit to make use of these technologies in the best way so as to fly the plane safely to its destination.

The three components that form the basis of the educational process are the teacher, the learner, and the environment. These three components are affected by the developing and changing technology as a result of globalization considerably. Teaching and learning techniques should be updated and connected with these developments; new tools are therefore needed to make the necessary updates. Determination and application of the new tools include many decisions. Decision-makers can make more effective decisions using Multi-Criteria Decision-Making Techniques (MCDM), a complex decision-making tool that includes both quantitative and qualitative factors at present time. This study aimed to determine which MCDM methods are used in studies conducted in higher education, which is one of the most important development level indicators of countries, and to present a systematic literature review of MCDM method applications. The study was conducted in three stages: first, known electronics were searched until the end of 2021 using keywords; then, all studies were listed in a systematic taxonomy, and in the last stage, Thematic Network Analysis was used to evaluate the development of MCDM studies in the higher education area. It is determined that the Analytical Hierarchy Process (AHP) method is the most widely used method in higher education in MCDM applications. It was observed that the most common use of MCDM applications in higher education is e-learning as well. This study aims to be a guide for all researchers and practitioners who will study in both higher education and the MCDM areas.

The purpose of this study was to leverage the artificial intelligence of ChatGPT to produce a scholarly review of the research literature on best practices for technology integration in higher education. The researchers searched the ERIC database using the keywords "technology integration" and "higher education or college or postsecondary" and "best practices." Peer reviewed scholarly journal articles were selected spanning the past 15 years, that had a focus on emerging technologies as well as student and faculty development. The Review of the Research Literature and Results sections were combined in that ChatGPT produced most of that output. It was found that emerging technologies should be embraced if they helped students learn, improved instruction, increased cooperative learning or collaboration, and were not contrary to the learning institution's mission. The researchers suggested that the writing produced by ChatGPT was adequate and human-like but not at the depth or desired level of a human scholar writing a review of the research literature. Also, they concluded that ChatGPT could be used as a tool, similar to the way a mathematician uses a calculator to solve complex problems, and that the ongoing evolution of ChatGPT was likely to produce better outcomes in the future. [Cowritten with ChatGPT.]

It is thought that the results of a study examining the articles published in peer-reviewed journals on technology in science education in terms of many criteria will provide important information to researchers. For this purpose, bibliometric network analysis was chosen as a method. The purpose of choosing this research method is to clearly summarize the relationship between science education and technology in order to identify technology in science education. In order to answer the research questions, bibliometric data consisting of 8511 articles in the Scopus database were evaluated using the bibliographic data obtained with the VOSviewer program. In addition, Pareto Law, Price Law, Lotka Law were used within the scope of citation analysis in the research. The results obtained from the research are presented.

This research aims to create a bibliometric map of studies on learning analytics. In this research, the bibliometric mapping technique was used from an international perspective to evaluate trends in learning analytics research by describing the factors of author, publication, keyword, journal, country, and citation. Most of the research on the subject has been published in the Journal of Learning Analytics. The results show that Gasevic D. is the most relevant author. Open University is the most relevant university. The USA has the most publications, co-authored publications, single national publications, and cited articles overall. The terms "learning," "higher education," and "analytics" are frequently used keywords. As a result of the thematic mapping analysis of the keywords, the themes in the Q1 area were learning analytics and education concepts. It has been seen that the concepts of learning analytics, analytics, and performance are at the center of the created co-occurrence network diagram.

This article reports on meta-synthesis research that examined contemporary scholarship on global citizenship for the purpose of identifying a possible alignment with Daisaku Ikeda's views on global citizenship. Thirty relatively contemporary scholarly articles on the subject matter were examined using a qualitative meta-synthesis methodology. Ikeda's speech entitled 'Thoughts on education for global citizenship', delivered over 25 years ago at Columbia University's Teachers College, USA, contains his most frequently cited ideas on the salient conditions required for global citizenship. As Ikeda is a thoughtful and prolific author on the subject of global citizenship, there is merit in exploring the alignment of his ideas about this concept with those articulated in contemporary scholarship. Conducting a meta-synthesis through the lens of Ikeda's essential elements of global citizenship has helped to identify potentially useful contributions to the global citizenship discourse. This article highlights salient common themes of global citizenship uncovered through the meta-synthesis research, as well as providing an alternative definition of global citizenship gleaned from the findings.