College Search and Fit Platforms Market Outlook

According to our latest research, the global College Search and Fit Platforms market size reached USD 1.48 billion in 2024, reflecting the sector’s rapid digital transformation and the increasing reliance on technology for higher education decision-making. The market is poised for robust expansion, with a projected CAGR of 11.2% from 2025 to 2033, which will bring the market value to approximately USD 3.78 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising demand for personalized college selection experiences, the proliferation of digital tools for educational guidance, and the growing complexity of college admissions processes worldwide.

One of the primary growth drivers for the College Search and Fit Platforms market is the increasing complexity of college admissions, which has made it challenging for students and parents to navigate the myriad of options and requirements. As higher education institutions diversify their offerings and admissions criteria, students are seeking platforms that can offer tailored recommendations based on academic background, extracurricular interests, location preferences, and financial constraints. The integration of artificial intelligence and machine learning into these platforms has further enhanced their ability to deliver personalized guidance, making them invaluable resources for applicants. This technological advancement not only streamlines the college search process but also improves the likelihood of a better fit between students and institutions, thereby increasing student satisfaction and retention rates.

Another significant factor propelling market growth is the increasing digital adoption among educational consultants, high school counselors, and parents. The COVID-19 pandemic accelerated the shift to digital platforms, making virtual campus tours, online application assistance, and scholarship searches more accessible than ever before. As a result, stakeholders across the education ecosystem are leveraging these platforms to gain comprehensive insights into colleges and universities, compare options efficiently, and access up-to-date information about scholarships, deadlines, and admission requirements. This trend is expected to persist, as digital natives continue to prefer technology-driven solutions for critical life decisions such as college selection.

Additionally, the globalization of higher education has spurred the need for cross-border college search and fit solutions. With more students aspiring to study abroad, platforms now offer multi-regional databases, language support, and guidance on international admissions processes. This has opened up significant opportunities for platform providers to expand their reach and cater to a diverse, global audience. Furthermore, the increasing emphasis on diversity, equity, and inclusion in higher education is prompting platforms to incorporate features that help underrepresented groups identify institutions with supportive environments and resources, further fueling market growth.

From a regional perspective, North America continues to dominate the College Search and Fit Platforms market, driven by the high penetration of technology in education and the presence of leading platform providers. However, Asia Pacific is emerging as the fastest-growing region, with a CAGR of 13.4% expected over the forecast period, attributed to the rising number of college-bound students, expanding internet access, and increasing awareness of global education opportunities. Europe also represents a significant market, benefiting from strong government support for digital education initiatives and a growing trend of international student mobility. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as digital infrastructure improves and demand for higher education guidance rises.

Product Type Analysis

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As the field of computing education grows and matures, bringing together computing education and higher education research becomes essential. Educational research has highlighted that how students study is crucial to their learning progress, and study behaviors have been found to play an important role in students' academic success. This data summarizes the results of a systematic literature review intended to find out what we know about the study behaviors of computing students and the role of educational design in shaping them. A taxonomy of study behaviors was developed and used to clarify and classify the definitions of study behavior, process, strategies, habits, and tactics, as well as identifying their relations to the educational context. The search resulted in 107 included papers, which were analyzed according to defined criteria and variables. Results revealed a fragmented field of research with ambiguous terminology and a tendency to focus on very specific educational contexts. Although computing education as a field is well equipped to expand the knowledge about both study behaviors and the connection to the educational context, the lack of common terminology and theories limits the impact. Finally, this review stresses that future research and practice should consider adopting a common framework to define and systematize study behavior data and contextualize the research in such a way that researchers and educators across institutional borders can compare and utilize results. The main contribution of this work is to provide a comprehensive synthesis of study behaviors in computing education, the paper also discusses the theory behind these definitions and how the field can develop in the future.

This dataset includes data files and supplementary files related to the review study "The concept of competence in educational research: a knowledge map of main research traditions and topics". It consists of the following documents. Supplementary file 1: Search process. This text document outlines a) our general search strategy, presented in table format, b) the search query we used to retrieve 62 systematic competence reviews from Scopus, c) the search terms employed by those systematic reviews, presented in table format, and d) the search terms we included in our own query, presented in table format. Supplementary files 2a-aj: Retrieved documents. These data files list the 135,298 documents retrieved through the search, along with their corresponding bibliometric information. Supplementary Files 3a-b: VOSviewer map and network files (csv), generated using VOSviewer software, contain results from the direct citation network analysis. Using these files with VOSviewer, our direct citation network graph can be reconstructed in an interactive form. Supplementary File 3c: DCNA cleaned map file (xlsx) provides a list of documents by cluster (research tradition), along with their bibliometric details and related statistics calculated by VOSviewer. With this file, the data can be easily read, filtered and sorted. When we refer to the extensive library of further reading in the article, we are referring to this file. Supplementary file 4: Yearly number of documents in the main traditions of competence research (jpg/png). This supplementary figure illustrates the historical development in the number of documents within each research tradition. It was used to support the writing of the Results section. Supplementary file 5: Data preprocessing algorithm (term co-occurrence analysis). Conducting the term co-occurrence analysis separately for each research tradition presented a key challenge: VOSviewer only accepts files that strictly follow the format of standard database export files. Therefore, we needed to create a separate file for each research tradition, containing all documents assigned to that tradition and formatted to match the structure of a Scopus export file. We addressed this by developing a custom Python code that first extracted cluster assignments from the direct citation network analysis (Supplementary Files 3a-c). It then processed the full dataset (Supplementary File 2), assigning each row to cluster 1, 2, 3, 4, or none, based on matches in DOI, author name, and document title. Supplementary files 6a-l: VOSviewer results files (term co-occurrence analysis). These files are similar to Supplementary Files 3a–c, but pertain to the term co-occurrence analysis instead. For each research tradition, there are three corresponding files: map files, network files, and cleaned map files. The map files (csv) and network files (csv), generated using VOSviewer software, contain results from the term co-occurrence analysis. Using these files with VOSviewer, our term co-occurrence graphs can be recreated in an interactive form. The cleaned map files (xlsx) contain the 500 most frequently occurring terms for each research tradition, along with related statistics calculated by VOSviewer. With these files, the data can be easily read, filtered and sorted.

Known as one of the competences of the 21st century, research competence can help students navigate through the complexities of a continuously shifting world. This study aims to analyze the acquisition and development of this competence in a sample of 154 undergraduate students of two bachelor’s degrees in Education Sciences (Social Education and Pedagogy) of the Universitat Autònoma de Barcelona (Spain). We conducted a three-phase study, in which (1) the learning outcomes related to research competences declared in the syllabi were identified and mapped through a content analysis of each syllabus; (2) students’ perceptions about the development of these learning outcomes were gathered through a questionnaire; and (3) guidelines to foster research competences among these undergraduates were explored by a Delphi panel technique. The results show that communicative skills and state-of-art reviewing skills are the least present across the courses of both degrees. The design of research competency acquisition across courses is uneven and does not seem clearly articulated. The students’ perception is consistent with the shortcomings, or disarticulation, observed in the curriculum analysis. They consider that the most poorly acquired competencies are the state-of-the-art reviewing, content knowledge, and communicative skills. Apparently, more emphasis is given to reflective thinking and communicative skills; but still, it is necessary to strengthen the acquisition of scientific content, the search for trustworthy information. These results were discussed with two panels of experts from which guidelines were defined to improve the acquisition, development, and evaluation of the research competence through university training in this field.

Description

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

Key Features

The dataset includes the following key features:

1. Countries and Areas: Name of the countries and areas.
2. Latitude: Latitude coordinates of the geographical location.
3. Longitude: Longitude coordinates of the geographical location.
4. OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males.
5. OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females.
6. OOSR_Primary_Age_Male: Out-of-school rate for primary age males.
7. OOSR_Primary_Age_Female: Out-of-school rate for primary age females.
8. OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males.
9. OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females.
10. OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males.
11. OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females.
12. Completion_Rate_Primary_Male: Completion rate for primary education among males.
13. Completion_Rate_Primary_Female: Completion rate for primary education among females.
14. Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males.
15. Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females.
16. Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males.
17. Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females.
18. Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students.
19. Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students.
20. Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education.
21. Primary_End_Proficiency_Math: Proficiency in math at the end of primary education.
22. Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education.
23. Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education.
24. Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24.
25. Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24.
26. Birth_Rate: Birth rate in the respective countries/areas.
27. Gross_Primary_Education_Enrollment: Gross enrollment in primary education.
28. Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education.
29. Unemployment_Rate: Unemployment rate in the respective countries/areas.

Potential Use Cases

• Global Education Analysis: Evaluate the status of education in different countries and regions, identifying disparities and trends.
• Gender Disparities: Analyze gender-based differences in education, including out-of-school rates and literacy.
• Education Policy Evaluation: Use completion rates to assess the effectiveness of education policies.
• Proficiency Analysis: Investigate students' proficiency in reading and math at different education levels.
• Socioeconomic Impact: Study the relationship between education and unemployment rates.
• Geospatial Analysis: Explore geographical patterns of education indicators.

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There is convincing evidence that the learning environments digitalization of tools and equipment ultimately results in the speed and depth learning involvement of academia members, by raising attainment of each of the digital learning experiences. The majority of the research that was conducted on the topic of enhancing the digital skills of learners, which would ultimately lead to an increase in their active engagement, was conducted on students in primary and secondary education, leaving members of higher education outside of the scope of the study. Given the uninterrupted search for academic performance and innovation, the current research considers the technological changes that lead to the transformation of the traditional academic learning environments as previously known. The current paper considers the changes in the learners’ engagement in the context of the dually digital transformation of the higher academic multi-institutional digitally-learning enhancements. An important factor to be considered regards the leadership evolution (in terms of teaching) that over time, led to a different speed contextual shift, according to its effectiveness, leading to higher or lower students learning (dis)engagement. The current manuscript aims to examine how the higher education digitalization levels could affect the student’s learning engagement, under the close monitoring of the academia leadership styles practice. Data collection and analysis implied at first a qualitative approach by issuing an online-distributed survey that resulted in a number of 2272 valid responses. After performing structural equation modelling and proving a valid assessment tool, the analysis resulted into statistically proving the validity of two main hypotheses according to which students learning engagement has a positive effect on the practice of academic leadership. Additionally, results emphasized the fact that higher education digitalization has altogether a negative effect of students learning engagement. Consequently, the current study stresses on the importance of different peers’ categories in the context of higher education institutions performance, with an emphasis on the different levels of students’ engagement and the leadership styles evolution and practice, aspects uniformly developing within a continuously digitally transformation of the higher education environment.

Efforts to delineate the field of medical education through journal identification have explored primarily bibliometric or consensus-based methods. These approaches often included journals outside the core focus of medical education as many non-medical education journals tend to publish medical education research. A systematic search and screening process was conducted to identify English-language journals with a clear focus on medical education (the MEJ-35). By incorporating academic databases, search engines, and existing lists across two time points, it provides the a comprehensive and methodologically rigorous delineation of medical education journals. The MEJ-35 offers a resource for researchers, educators, and institutions to navigate the publication landscape in medical education. It facilitates targeted journal selection as well as informing future work in refining and expanding the delineation of medical education as a scholarly discipline.

This dataset contains the metadata records about research products (research literature, data, software, other types of research products) with funding information available in the OpenAIRE Graph produced on July 2024.Records are grouped by funder in a dedicated archive file (.tar). fundRef contains the following funders 100007490 Bausch and Lomb Ireland 100007630 College of Engineering and Informatics, National University of Ireland, Galway 100007731 Endo International 100007819 Allergan 100008099 Food Safety Authority of Ireland 100008124 Department of Jobs, Enterprise and Innovation 100009098 Department of Foreign Affairs and Trade, Ireland 100009099 Irish Aid 100009770 National University of Ireland 100009985 Parkinson's Association of Ireland 100010399 European Society of Cataract and Refractive Surgeons 100010546 Deparment of Children and Youth Affairs, Ireland 100010547 Irish Youth Justice Service 100010993 Irish Nephrology Society 100011096 Jazz Pharmaceuticals 100011396 Irish College of General Practitioners 100012733 National Parks and Wildlife Service 100012919 Epilepsy Ireland 100012920 GLEN 100012921 Royal College of Surgeons in Ireland 100013029 Iris O'Brien Foundation 100013206 Food Institutional Research Measure 100013381 Irish Phytochemical Food Network 100013433 Transport Infrastructure Ireland 100013917 Society for Musicology in Ireland 100014251 Humanities in the European Research Area 100014364 National Children's Research Centre 100014384 Amarin Corporation 100014902 Irish Association for Cancer Research 100015023 Ireland Funds 100015278 Pfizer Healthcare Ireland 100015319 Sport Ireland Institute 100015442 Global Brain Health Institute 100015992 St. Luke's Institute of Cancer Research 100017144 Shell E and P Ireland 100017897 Friedreich’s Ataxia Research Alliance Ireland 100018064 Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media 100018172 Department of the Environment, Climate and Communications 100018175 Dairy Processing Technology Centre 100018270 Health Service Executive 100018529 Alkermes 100018542 Irish Endocrine Society 100018754 An Roinn Sláinte 100019428 Nabriva Therapeutics 100019637 Horizon Therapeutics 100020174 Health Research Charities Ireland 100020202 UCD Foundation 100020233 Ireland Canada University Foundation 100022895 Health Research Institute, University of Limerick 100022943 National Cancer Registry Ireland 501100001581 Arts Council of Ireland 501100001582 Centre for Ageing Research and Development in Ireland 501100001584 Department of Agriculture, Food and the Marine, Ireland 501100001586 Department of Education and Skills, Ireland 501100001587 Economic and Social Research Institute 501100001588 Enterprise Ireland 501100001591 Heritage Council 501100001592 Higher Education Authority 501100001593 Irish Cancer Society 501100001594 Irish Heart Foundation 501100001595 Irish Hospice Foundation 501100001598 Mental Health Commission 501100001599 National Council for Forest Research and Development 501100001600 Research and Education Foundation, Sligo General Hospital 501100001601 Royal Irish Academy 501100001603 Sustainable Energy Authority of Ireland 501100001604 Teagasc 501100001627 Marine Institute 501100001628 Central Remedial Clinic 501100001629 Royal Dublin Society 501100001630 Dublin Institute for Advanced Studies 501100001631 University College Dublin 501100001633 National University of Ireland, Maynooth 501100001634 University of Galway 501100001635 University of Limerick 501100001636 University College Cork 501100001637 Trinity College Dublin 501100001638 Dublin City University 501100002736 Covidien 501100002755 Brennan and Company 501100002919 Cork Institute of Technology 501100002959 Dublin City Council 501100003036 Perrigo Company Charitable Foundation 501100003037 Elan 501100003496 HeyStaks Technologies 501100003553 Gaelic Athletic Association 501100003840 Irish Institute of Clinical Neuroscience 501100004162 Meath Foundation 501100004210 Our Lady's Children's Hospital, Crumlin 501100004321 Shire 501100004981 Athlone Institute of Technology 501100006518 Department of Communications, Energy and Natural Resources, Ireland 501100006553 Collaborative Centre for Applied Nanotechnology 501100006554 IDA Ireland 501100006759 CLARITY Centre for Sensor Web Technologies 501100009246 Technological University Dublin 501100009315 Cystinosis Ireland 501100010808 Geological Survey of Ireland 501100011030 Alimentary Glycoscience Research Cluster 501100011031 Alimentary Health 501100011626 Energy Policy Research Centre, Economic and Social Research Institute 501100012354 Inland Fisheries Ireland 501100014384 X-Bolt Orthopaedics 501100014710 PrecisionBiotics Group 501100014745 APC Microbiome Institute 501100014826 ADAPT - Centre for Digital Content Technology 501100014827 Dormant Accounts Fund 501100016041 St Vincents Anaesthesia Foundation 501100017501 FotoNation 501100018641 Dairy Research Ireland 501100018839 Irish Centre for High-End Computing 501100020270 Advanced Materials and Bioengineering Research 501100020403 Irish Composites Centre 501100020425 Irish Thoracic Society 501100020871 Bernal Institute, University of Limerick 501100021102 Waterford Institute of Technology 501100021110 Irish MPS Society 501100021525 Insight SFI Research Centre for Data Analytics 501100021838 Royal College of Physicians of Ireland 501100022542 Breakthrough Cancer Research 501100022610 Breast Cancer Ireland 501100022728 Munster Technological University 501100022729 Institute of Technology, Tralee 501100023378 Lauritzson Foundation 501100023551 Cystic Fibrosis Ireland 501100023970 Tyndall National Institute 501100024242 Synthesis and Solid State Pharmaceutical Centre 501100024313 Irish Rugby Football Union AKA Academy of Finland ANR French National Research Agency (ANR) ARC Australian Research Council (ARC) ASAP Aligning Science Across Parkinson's CF Carlsberg Foundation CHISTERA CHIST-ERA CIHR Canadian Institutes of Health Research DFG Deutsche Forschungsgemeinschaft EC_ERASMUS+ European Commission - Erasmus+ funding stream EC_FP7 European Commission - FP7 funding stream EC_H2020 European Commission - H2020 funding stream EC_HE European Commission - HE funding stream EDTECH Teknologi Pendidikan ID EEA European Environment Agency EPA Environmental Protection Agency FBS The Foundation for Baltic and East European Studies FCT Fundação para a Ciência e a Tecnologia, I.P. FORMAS Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning FORTE Swedish Research Council for Health, Working Life and Welfare FWF Austrian Science Fund HL Swedish Heart-Lung Foundation HRB Health Research Board HRZZ Croatian Science Foundation INCA Institut National du Cancer IRC Irish Research Council IRFD Independent Research Fund Denmark IReL Irish Research eLibrary kawforskning Knut and Alice Wallenberg Foundation KKS Swedish Knowledge and Competence Development Foundation MESTD Ministry of Education, Science and Technological Development of Republic of Serbia MZOS Ministry of Science, Education and Sports of the Republic of Croatia (MSES) NHMRC National Health and Medical Research Council (NHMRC) NIH National Institutes of Health NNF Novo Nordisk Foundation NSERC Natural Sciences and Engineering Research Council of Canada NSF National Science Foundation NVV Swedish Environmental Protection Agency NWO Netherlands Organisation for Scientific Research (NWO) RCN The Research Council of Norway RI Taighde Eireann - Research Ireland RIF Research and Innovation Foundation RJ Riksbankens Jubileumsfond scilifelab Science for Life Laboratory scs Swedish Cancer Society SFI Science Foundation Ireland SNSF Swiss National Science Foundation ssf Swedish Foundation for Strategic Research SSHRC Social Sciences and Humanities Research Council ssmf Swedish Society for Medical Research TARA Tara Expeditions Foundation TIBITAK Türkiye Bilimsel ve Teknolojik Araştırma Kurumu UKRI UK Research and Innovation VINNOVA Swedish Governmental Agency for Innovation Systems VR Swedish Research Council wgf Wenner-Gren Foundations WT Wellcome Trust Each tar archive contains gzip files with one json record per line. Json records are compliant with the schema available at https://doi.org/10.5281/zenodo.14608710. You can also search and browse this dataset (and more) in the OpenAIRE EXPLORE portal and via the OpenAIRE API.

Background The geographical distribution of publications as an indicator of the research productivity of individual countries, regions or institutions has become a field of interest. We investigated the geographical distribution of contributions to the two leading journals in the field of medical education, Academic Medicine and Medical Education.

   Methods
   PubMed was used to search Medline. For both journals all journal articles in each year from 1995 to 2000 were included into the study. Then the affiliation was retrieved from the affiliation field of the MEDLINE format. If this was not possible, it was obtained from the paper version of the journal.


   Results
   Academic Medicine published contributions from 25 countries between 1995 and 2000. Authors from 50 countries contributed to Medical Education in the same period of time. Authors from the USA and Canada wrote ca. 95% off all articles in Academic Medicine, whereas authors from the UK, Australia, the USA, Canada and the Netherlands were responsible for ca. 74% of all articles in Medical Education in the investigated period of time.


   Conclusions
   While many countries contributed to both journals, only a few of them were responsible for the majority of all articles.

In the fiscal year 2023, *** tertiary education institutions researched medical sciences in Japan. That year, ***** higher education institutions in total conducted research on natural sciences and engineering, while ***** researched the field of social sciences and humanities.

The German part of the project ´Academic Profession in Knowledge Societies (APIKS)´ is part of an international comparative study of working conditions in science and the attitudes of scientists. The mixed-mode survey (CAWI/Paper-and-Pencil), which took place in winter 2017/18, was conducted for the third time. Similar surveys were conducted in 1992 (Carnegie Study) and 2007/2008 (The Changing Academic Profession; CAP). The project involves research teams from more than 30 countries organised as an APIKS consortium (including Argentina, Austria, Brazil, Canada, Chile, China, Croatia, Finland, Japan, Kazakhstan, Latvia, Malaysia, Mexico, Norway, Portugal, Russia, Slovenia, South Korea, Sweden, Taiwan, Turkey and the USA). For the German study, a stratified random sample of 24 higher education institutions was drawn, including 12 universities of applied sciences and 12 universities. The sample takes into account a regional distribution and differences in the size of HEIs. The organisational variables are not part of the scientific-use file due to anonymisation. The German sub-study of the APIKS project was funded by the Federal Ministry of Education and Research (BMBF) (funding code M522200).

The APIKS survey has two objectives. Firstly, the working conditions and attitudes of academics (professors and academic staff) at public universities are to be identified with a focus on research, teaching and university governance, including academic self-administration. Secondly, with the supplementary block of questions on knowledge and technology transfer, a current topic area on the so-called ´third mission´ or social relevance is added to the survey. The questions on knowledge and technology transfer activities are not limited to classic fields such as spin-offs and licences, but also include teaching and voluntary work. In addition, the German sub-study was expanded to include a block of questions on "Supervision practices in the doctoral phase". This short study asks about the experiences of professors regarding supervision activities in the context of their doctorate and is based on a transfer of qualitatively elaborated supervision types and practices into standardised survey instruments (Schneijderberg 2018, 2021).

A systematic review was conducted from October 2024 to January 2025. The final search was carried out on January 21st, 2025. Databases used for research were Pubmed, Scopus and Web of Science (WoS). The research question was structured using the PICO framework to ensure a clear and focused approach. The population of interest comprises students and professionals in the field of medicine, as these groups are directly involved in the educational processes being studied. The intervention focuses on the use of simulation and gamification as learning tools, highlighting their potential to enhance educational experiences and outcomes in medical training. The comparison is established against traditional teaching methods commonly employed in medical education, such as lectures and standard clinical training, to evaluate the added value of simulation and gamification. The outcomes of interest are centered on measurable improvements in key areas, including the acquisition of clinical skills, enhanced decision-making abilities, and increased performance in both simulated and real-world settings. This structure ensures that the research remains targeted and capable of addressing the effectiveness of these innovative educational strategies in medical education.

This dataset supports the systematic literature review titled “Inclusive Education as a Pathway to Social-Emotional Development in Children with Special Educational Needs and Regular Students.”

The dataset contains extracted data from selected peer-reviewed journal articles, including publication year, research design, participants, educational context, social-emotional learning outcomes, and key findings related to inclusive education.

The data were collected through a systematic search of academic databases and screened based on predefined inclusion and exclusion criteria. This dataset is intended to enhance transparency, replicability, and further research in the field of inclusive education and social-emotional development.

Rationale: The development of research skills in the higher education environment is a necessity because universities must be concerned about training professionals who use the methods of science to transform reality. Furthermore, within research competencies, consideration must be given to those that allow for the development of academic reading and writing in university students since this is a field that requires considerable attention from the educational field at the higher level.Objective: This study aims to conduct a systematic review of the literature that allows the analysis of studies related to the topics of research competencies and the development of academic reading and writing.Method: The search was performed by considering the following quality criteria: (1) Is the context in which the research is conducted at higher education institutions? (2) Is the development of academic reading and writing considered? (3) Are innovation processes related to the development of academic reading and writing considered? The articles analyzed were published between 2015 and 2019.Results: Forty-two papers were considered for analysis after following the quality criterion questions. Finally, the topics addressed in the analysis were as follows: theoretical–conceptual trends in educational innovation studies, dominant trends and methodological tools, findings in research competencies for innovation in academic literacy development, types of innovations related to the development of academic reading and writing, recommendations for future studies on research competencies and for the processes of academic reading and writing and research challenges for the research competencies and academic reading and writing processes.Conclusion: It was possible to identify the absence of studies about research skills to develop academic literacy through innovative models that effectively integrate the analysis of these three elements.

NSF Grant: SciSIP #1548288 (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548288). Co-PIs: Lauren Lanahan (UO) & Alexandra Graddy-Reed (USC). Material available: DB_Data Building (all original data are available); Analysis (econometric, regression analysis)

Romanian Higher Education Institutions: A Longitudinal Citing Index

Metrics, Authors, Citations and More

By [source]

About this dataset

Data concerning Romanian Higher Education Institutions (HEIs) is available in abundance these days, yet it’s never been easier to analyze trends and advancements in the research field than with this comprehensive indexed dataset. Spanning a period of twelve years since 2007 up until the present date, this ingenious collection contains citations for academic publications by HEIs in Romania, as well as authorship information, titles, sources, publishers and URLs for both articles and citations.

Researchers can use this data to observe performance variations throughout the years of HEIs with respect to publications, track changes within institutions from one year to the next and compare institutions between one another or against other research bodies worldwide. The indexing feature presents numerous benefits such as allowing users to find trends across Europe or globally that would’ve been difficult or time-consuming otherwise. As much as it provides insight into the Romanian academia landscape over time -since 2007- , it also becomes a viable tool for assessing progress achieved by any given higher education institution over a specific span of time.

This dataset compiles columns such as *_COD_*, *_UNIVERSITATE_*, *Cites*, *Authors*, Title , Year , Source , Publisher , ArticleURL , CitesURL​ ​, GSRank​ ​, QueryDate​​ , ​*Type​ ​*, ​DOI​​​​​ 08}>​​​3431 ??>?8<] 8012,,7 ?!<], {GSRahk:Integer}, {*QueryDate:Date}, {*Type:String}, {DOI:String}, {*ISSN:String}, {CitationURL:String}, {*Volume:Integer}, [*IssueoInteger>, #StartPagea Integer],[#EndPageg Intege1r],[2ECCt String][ CitesPerYear^ Inte ger][ CitesPerAuthor{f Integer]AUTHORCountj Intergerrf Age5 intergerl oAbstract5 Stringt| FullTextURLe String}{ RelatedURLu Strin g}and grants access not only numerical values regarding publication output per author per year but also info suchas abstracts when available

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How to use the dataset

Welcome to the Romanian Higher Education Institutions Index, a comprehensive indexed approach to Romanian Higher Education Institutions (HEIs)! In this guide, you'll be able to learn how to use this dataset and explore its features.

• Understand the columns: The data is organized into different column headings in order for easier filtering and searching of what you are looking for depending on your research topic or the subject matter you are investigating. Make sure that you get acquainted with each of these columns and understand what information they represent because some of the statistics may vary in certain circumstances.
• Filter your search: You can use any combination of filters such as COD, UNIVERSITATE, Cites and so on depending on your needs/topic to narrow down your search results as much as possible in order to achieve more accurate results for analysis purposes or just pure exploration if that is why you are looking at data from HEIs within Romania’s context!
• Analyze relevant trends: Once your filter is set up, then it is time for research – look at how many HEIs were listed this year compared with past ones; explore the citation progressions over years; compare publications between authors by using their respective publication counts per author; check out which sources had more citations than others across universities among other kind of comparisons!
• Use specific metrics: As mentioned above there are specialized metrics like Age, GSRank as well as slightly general ones like AuthorCount or Volume that allow comparison between institutions/authors so make sure not only focus on one type but rather cover all possibilities when exploring different trends in a wider scope while still having quantitative values associated with them!
• Utilize detailled abstracts & full texts: By clicking onto ArticleURL associated with each publication entry one can usually find detailed abstracts about them so if interested about certain work – it can be easily accessed! Also by writing relative queries on Google Search Engine people will also find out whether some articles offer free accesses directly from publisher websites/ platforms which sometimes provide full text overviews thanks also due citation links available through CitationURL columns provided by our dataset here too – another convenient resource worth checking out from time-to-time when necessary!

Research Ideas

• Comparing rese...

Vocational Education phenomenon dataset in Indonesia

The study on continuing education behavior in Germany was conducted by Verian (until November 2023: Kantar Public) on behalf of the Federal Ministry of Education and Research (BMBF). The German Adult Education Survey 2022 (AES 2022) is part of the EU-AES 2022. Based on survey-based information on participation in adult education, the research project provides information for educational monitoring and for the scientific community. In the survey period 12.07.2022 to 20.03. 2023, the German resident population aged 18 to 69 was surveyed in personal interviews (CAPI) and online interviews (CAWI) on the following topics job-related information, professional situation currently and in the last 12 months, origin (migration background); educational background, participation in formal education (FED) and non-formal further education (NFE) activities in the last 12 months, follow-up questions on NFE activities, (further) education barriers and needs, transparency and advice, informal learning (INF), digitalization and internet use, self-assessment of foreign language skills and health issues. The respondents were selected using a multi-stage random sample from population registers.

This dataset contains bibliometric data used for the study titled “Augmented Reality in Computer Network Education: A Bibliometric Analysis of Research Trends, Influential Sources and Conceptual Themes from 1999 to 2026”. The data were retrieved from the Scopus database on 23 May 2026 using the search query ALL("augmented reality") AND ALL(learning) AND ALL("computer networks"). After applying subject area filtering, document type screening and metadata quality assessment based on the PRISMA procedure, 295 publications were retained as the final dataset for bibliometric analysis.

The dataset includes bibliographic metadata related to publication year, source title, citation count, institutional affiliation, country contribution and author keywords. The cleaned dataset was analysed using biblioSpy® to generate descriptive bibliometric indicators, annual publication and citation trends, source statistics, institutional statistics, country statistics and keyword co-occurrence patterns.

This dataset is provided to support transparency, reproducibility and verification of the bibliometric analysis reported in the manuscript. The dataset should be used for academic and non-commercial research purposes with appropriate citation.

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839 According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching. Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project! I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it: To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results. Systematic literature review – an Introduction Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review. In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps: Selecting a research question. Selecting the bibliographic database. Choosing the search terms. Applying practical screening criteria. Applying methodological screening criteria. Doing the review. Synthesizing the results. I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made. Systematic literature review – decisions I made Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences? Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science. Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed. Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019. Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use. Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and evaluation by others, who are experts in the respective fields/disciplines.