College Search and Fit Platforms Market Outlook

According to our latest research, the Global College Search and Fit Platforms market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.8% during 2024–2033. The primary catalyst for this remarkable growth is the increasing digitization of the higher education journey, with students, parents, and educational institutions alike demanding more personalized, data-driven, and accessible tools to navigate the complex landscape of college admissions and fit assessment. As the global student population grows and competition among educational institutions intensifies, the need for comprehensive, AI-powered platforms that can match students with best-fit colleges based on academic, social, and financial criteria is driving sustained investment and innovation in this market.

Regional Outlook

North America currently dominates the College Search and Fit Platforms market, accounting for the largest share at over 40% of global revenue in 2024. This leadership is attributed to the region’s mature higher education ecosystem, widespread internet penetration, and early adoption of digital platforms for college admissions. The United States, in particular, is home to a vast network of colleges and universities, each with unique admissions processes and requirements. This complexity has driven demand for sophisticated search and fit solutions among students, parents, and counselors. Furthermore, supportive government policies, a high level of technological literacy, and the presence of several leading market players have contributed to North America’s continued dominance. The region also benefits from a culture that prioritizes higher education, robust funding for EdTech innovation, and a competitive admissions environment that incentivizes students to leverage every available advantage.

Asia Pacific is projected to be the fastest-growing region, with a forecasted CAGR exceeding 20% through 2033. This rapid expansion is fueled by the region’s burgeoning middle class, increasing focus on international education, and the proliferation of mobile and cloud technologies. Countries such as China, India, and Southeast Asian nations are witnessing a surge in outbound students seeking higher education abroad, as well as a growing number of domestic institutions seeking to attract diverse talent. Governments and private sector stakeholders are investing heavily in digital infrastructure and educational technology, recognizing the potential of college search and fit platforms to streamline the admissions process, improve student outcomes, and enhance institutional competitiveness. The rising adoption of English-medium programs and the growing influence of global university rankings are further accelerating demand for these platforms across Asia Pacific.

Emerging economies in Latin America, the Middle East, and Africa are also showing promising adoption trends, though they face unique challenges. In these regions, the expansion of college search and fit platforms is often hampered by limited digital infrastructure, varying levels of internet accessibility, and diverse regulatory environments. However, localized demand is growing as more students aspire to study at top regional or international institutions, and as governments implement policies to promote higher education access and quality. Platform providers are increasingly tailoring their offerings to address language barriers, cultural differences, and specific admissions processes. Strategic partnerships with local educational institutions and governments are proving essential for overcoming market entry barriers and ensuring long-term success in these emerging markets.

Report Scope

The recent technologies rise today as a tool of significant importance today, especially in the educational context. In this sense, Augmented Reality (AR) is a technology that is achieving a greater presence in educational centers in the last decade. However, Augmented Reality has not been explored in depth at the Secondary Education stage. Due to this, it is essential to analyze and concentrate the scientific research developed around this educational technology at that stage. Therefore, the aim of this research is to describe the influence that Augmented Reality shows on the motivation and academic performance of students in the Secondary Education stage. In relation to the methodology, a systematic review of the literature has been conducted using the Kitchenham protocol, where several factors have been analyzed, such as subjects, activities, and electronic implementation devices, together with the effects on motivation and student's academic performance. The Scopus and Web of Science (WoS) databases have been used to search for scientific papers, with a total of 344 investigations being analyzed between 2012 and 2022. The methodological stages considered were the formulation of research questions, the choice of data sources, search strategies, inclusion and exclusion criteria and quality assessment, and finally, data extraction and synthesis. The results obtained have shown that the use of AR in the classroom provides higher levels of motivation, reflected by factors such as attention, relevance, confidence, and satisfaction, and reflects better results in the tests carried out on the experimental groups compared to the control groups, which means an improvement in the academic performance of students. These results supply a fundamental theoretical basis, where the different teachers should be supported for the incorporation of AR in the classroom, since how this educational technology has been shown offers great opportunities. Likewise, the development of research in areas not so addressed can further clarify the generality of AR based on its influence on learning. In addition, the fields of natural sciences and logical-mathematical have been the most addressed, managing to implement their contents through object modeling. In short, this research highlights the importance of incorporating Augmented Reality into all areas and educational stages, since it is a significant improvement in the teaching and learning process.

This dataset comprises metadata of 453 scientific publications on creativity in education within ASEAN countries, published between 2000 and May 2024. The data were retrieved from the Scopus database using a targeted search query focusing on creativity-related terms, education-related terms, and ASEAN country affiliations. This dataset formed the basis for a bibliometric analysis examining publication trends, influential authors and journals, collaborative networks, and thematic evolution in the field. The analysis employed Biblioshiny (R package) and VOSviewer software.

Previous research offers insight on different factors that explain academic performance during the first year of university, however analyzing each one independently. This study aims to tap on this research gap analysing the impact of gender, course content nature, and semester timing on academic performance of first-year students enrolled in various majors within the Faculty of Social Sciences at the Universidad Europea de Canarias (UEC). A conditional quantile regression analysis was conducted in order to assess the degree of association among the variables under investigation. The data were collected across two semesters (autumn and winter-spring) within the academic year 2022-2023, focusing on first-year students. The study aimed to identify patterns and trends that can guide the development of academic policies, and teaching practices aimed at promoting instructional effectiveness and supporting student success during the critical transition period of their first year. Results con..., The authors picked up the data from their own students and other colleagues' students during an academic year. There was a previous informed consent from the students and we also received the approval of the European University Ethical Comittee with reference code 2024-744., , # The effect of gender over performance in higher education

https://doi.org/10.5061/dryad.69p8cz99z

Description of the data and file structure

Our database (Microsoft Excel format) contains data from 116 students at the European University of the Canary Islands during the course 2022-23. Each row corresponds to a student, they are numbered anonymously and consecutively (column A) and their gender appears in column B. Every precaution has been taken to ensure the students' anonymity.

Data is collected for six subjects: Subject 1 (columns C-F), Subject 2 (columns G-J), Subject 3 (columns K-N), Subject 4 (columns O-R), Subject 5 (columns S-V), Subject 6 (columns W- Z). Each subject occupies 4 columns corresponding to each of the variables collected.

For each subject, the variables collected are: Semester [1 (1st Semester) or 2 (2nd semester)]; Theoretical (0) or quantitative (1) subject; Attendance (%); and Final qualification (out of 10 p...

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).

Key statistics on AI search adoption in prospective student research behavior, sourced from UPCEA and Search Influence 2025, OHO Interactive AI College Search Survey 2025, Lumina Foundation-Gallup 2026 State of Higher Education, and Copyleaks 2025 AI in Education Trends Report.

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.

It also let me find employment that fits with my morals and beliefs. I want other people to find work that they can really care about.

This dataset is created for students, researchers, and educators interested in analysing learning behaviour, academic performance, and assignment outcomes. It can be used for educational research, machine learning, predictive modelling, and data visualisation projects.

The data is suitable for analysing student engagement, assignment completion patterns, academic performance, and learning trends across different educational environments. Researchers can also use it to build predictive models and compare student outcomes using various analytical techniques.

Students studying vocational education and qualification frameworks in Ireland may find this dataset useful for academic research and coursework. It provides valuable insights that can support educational analysis and research-based projects.

For additional educational resources related to QQI studies, students may also explore QQI Assignment Help Ireland for general academic guidance and learning support.

A random sampling technique was used to select second to fourth year undergraduate nursing students at a university campus in Namibia. Data collection was done through a Google online questionnaire. Participants were thoroughly screened to determine their eligibility for inclusion in the study, and were informed of the study's procedures. Written consent was obtained from those who agreed to participate.

Open Educational Resources

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 research was carried out by Northumbria University Library's Learning and Research Skills team. We wanted to understand what students find difficult about writing, from both the student’s and tutor’s perspective to shape the support we provide. Between January and August 2024, we collected data through surveys and discussions.The data here includes:all of the raw data collected (.csv files).the raw data with analysis (.xlsx files).A research article based on this data is being submitted and a link will be placed here when appropriate.This research was conducted as an internal service review. Participants consented to their data being used for service improvement purposes.SurveysSurveys were distributed electronically to staff and both electronically and physically to students. These are the questions asked:Staff (tutors)What forms of academic writing do you set your students?Three things your students generally do well?Three things your students often struggle with?What aspects of academic writing could the Library teach better to support your students?StudentsWhen you sit down to write an assessment, what do you find easy about it?When you sit down to write an assessment, what do you find difficult about it?If you are struggling to write an assessment, what do you do?If you need help with academic writing from us [the Library] which format do you prefer?WhiteboardsWhiteboards were then used alongside our surveys to continuously capture student perceptions. This has resulted in a random and varied sample, albeit still limited to Northumbria University.All physical surveys and photographs of the whiteboard comments were transcribed.Data analysisIn order to conduct a thematic analysis, we used a complete coding process where we categorised the results into multiple relevant codes. From these codes, we identified key themes and sub-themes within them.

The rapid integration of data-intensive, AI-powered technologies in education, often driven by non-EU tech industries, has raised concerns about their social impact, sustainability, and alignment with ethical principles (Rivera-Vargas, 2023; Selwyn, 2023; Williamson, 2023). While the EU has advanced regulatory efforts, such as the AI Act and Ethical Guidelines for AI in Education (Directorate-General for Education, 2022), translating ethical principles into practice remains ambiguous and fragmented (Morley et al., 2023). Despite the proliferation of over 80 ethical frameworks by 2019 (Morley, op.cit), operationalizing these into meaningful educational practices is fraught with ambiguities and challenges. Ethical guidelines are often portrayed as “complementary” tools to mitigate technological risks, yet their transformative potential remains limited (Green, 2021). The funding landscape for projects aimed at fostering knowledge generation, innovation, transformation, and research in the educational sector also encounters significant challenges. The European Union, through funding programs such as Erasmus+ and Horizon Europe, supports educational projects addressing key challenges. Recently, these programs have emphasized the ethical dimension, highlighting the need to align technological and educational advancements with robust principles. However, this focus raises questions about how these values are effectively implemented in practice.

In this context, the present study examines how EU-funded educational projects address ethical principles through a Mixed Methods approach embedding Text-mining and Discourse Analysis. Through a documentary investigation of the Erasmus+ project database, four key searches were conducted, revealing significant gaps. Among the more than 2,000 completed projects, few included ethical reflections on AI or data use, and none explicitly addressed critical issues such as digital sovereignty, platformization, or activism. The initiatives predominantly focused on technical skills (e.g., coding, data analysis), while overlooking critical competencies such as resistance and ethical-political engagement.

Preliminary findings suggest a persistent reliance on techno-solutionist narratives, where ethical guidelines are often reduced to mere compliance checklists, offering minimal transformative value. This misalignment between EU ethical frameworks and project outcomes raises critical concerns regarding the reinforcement of corporate interests and techno-deterministic approaches. The study underscores the necessity of bridging this gap, ensuring that public funding supports socially just, sustainable, and inclusive educational practices. It advocates for funding criteria that emphasize critical perspectives on technology, advancing meaningful agency and systemic transformation beyond superficial ethical commitments (Floridi, 2023).

This record contains:

The presentation used during the Conference

The dataset adopted with 3204 EU-Project metadata

An R script with the preliminary analysis adopted - This is also published on RPUBS

A Python script and the resulting HTML with the creation of an interactive bipartite graph.

References

Directorate-General for Education, Y. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/153756

Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges , and Opportunities. Oxford University Press.

Green, B. (2021). The Contestation of Tech Ethics: A Sociotechnical Approach to Ethics and Technology in Action. http://arxiv.org/abs/2106.01784

Jacovkis, J., Rivera-Vargas, P., Parcerisa, L., & Calderón-Garrido, D. (2022). Resistir, alinear o adherir. Los centros educativos y las familias ante las BigTech y sus plataformas educativas digitales. Edutec. Revista Electrónica de Tecnología Educativa, 82, Article 82. https://doi.org/10.21556/edutec.2022.82.2615

Morley, J., Kinsey, L., Elhalal, A., Garcia, F., Ziosi, M., & Floridi, L. (2023). Operationalising AI ethics: Barriers, enablers and next steps. AI & SOCIETY, 38(1), 411–423. https://doi.org/10.1007/s00146-021-01308-8

Raffaghelli, J. E. (2022). Educators’ data literacy: Understanding the bigger picture. In Learning to Live with Datafication: Educational Case Studies and Initiatives from Across the World (pp. 80–99). Routledge. https://doi.org/10.4324/9781003136842

Rivera-Vargas, C. C., Pablo. (2023). What is ‘algorithmic education’ and why do education institutions need to consolidate new capacities? In The New Digital Education Policy Landscape. Routledge.

Selwyn, N. (2023). Lessons to Be Learnt? Education, Techno-solutionism, and Sustainable Development. In Technology and Sustainable Development. Routledge.

Williamson, B. (2023). The Social life of AI in Education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00342-5

This dataset contains 50,000 learner records with 27 columns, designed for educational resource recommendation, learner behavior analysis, engagement assessment, and personalized learning research. It combines demographic information, learning preferences, academic performance, resource interactions, engagement metrics, and feedback attributes to provide a comprehensive view of learners and their educational experiences. The dataset enables the study of factors that influence educational resource relevance and supports the development of intelligent learning analytics and recommendation systems.

Key Features (Columns) Learner_ID – Unique identifier assigned to each learner. Age – Age of the learner. Education_Level – Current educational stage of the learner. Learning_Style – Preferred learning approach. Device_Type – Primary device used for learning activities. Internet_Quality – Quality of internet connectivity during learning. Preferred_Study_Time – Preferred time of day for studying. Subject_Interest – Subject area the learner is most interested in. Preferred_Resource_Type – Preferred educational content format. Resource_Difficulty – Difficulty level of learning materials. Preferred_Content_Length – Preferred duration or length of educational resources. Weekly_Learning_Hours – Average hours spent learning each week. Courses_Completed – Number of completed learning courses. Quiz_Accuracy – Average quiz performance. Average_Completion_Rate – Percentage of completed learning activities. Average_Watch_Percentage – Percentage of educational content viewed. Resource_Rating – Rating assigned to learning resources. Click_Frequency – Frequency of learner interactions with educational resources. Search_Frequency – Number of resource searches performed. Bookmark_Count – Number of saved learning resources. Discussion_Participation – Participation in learning discussions or forums. Assignment_Submission_Rate – Percentage of submitted assignments. Learning_Consistency – Consistency of learning activities over time. Engagement_Score – Overall learner engagement indicator. Feedback_Sentiment – General learner feedback sentiment. Region – Geographical region associated with the learner. Recommendation_Quality (Target) – Overall recommendation outcome with categories such as Highly_Relevant, Relevant, and Needs_Improvement. Target Column

Recommendation_Quality

Highly_Relevant – The recommended educational resources closely match the learner's interests, engagement, and learning behavior. Relevant – The recommended resources are generally appropriate for the learner with moderate personalization. Needs_Improvement – The recommended resources require better alignment with the learner's preferences and learning patterns.

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.

Abstract (en): Replication materials for AERA Open Publication:Curran, F. C. (2016). The State of Abstracts in Educational Research. AERA Open, 2(3), 2332858416650168.Abstract:Background:There is a well-documented divide between education research and practice. In 2004, Mosteller, Nave, and Miech argued for a focus on the research abstract, particularly structured abstracts, to improve the translation of research into practice. Since their call, no study has systematically examined the quality of abstracts in education research or the degree to which structured abstracts are utilized.Purpose:This study addresses two questions. First, what are the characteristics of the research abstracts required by journals in the field of education research? Second, to what extent do research abstracts in the field of education research contain the basic components of a research study?Data:Original data are drawn from the top 150 education research journals. Data include the instructions to authors regarding abstracts for each journal (n = 150) and a random sample of abstracts (n = 189).Methods:Journal instructions and abstracts were coded. Codes included whether they were structured and whether they included components of a research study, such as the data or findings.Results:A nontrivial proportion of abstracts fail to include important components of a research study. More than one in three lacked information regarding the background, and a similar proportion lacked information on conclusions. Over one quarter omitted information regarding the data, and a similar proportion lacked information on methodology. Only 7% of the top 150 journals explicitly require a structured abstract.Conclusions:The quality of abstracts in educational research could be improved. Suggestions for improving abstracts, such as shifting toward structured abstracts, are offered.

List and links to statistical publications on higher education and research issued by the ministerial statistical services of the French Ministry of National Education, Higher Education and Research since 1999.This list is also available in the statistical publications search engine for higher education and research.

Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This dataset analyzed integrated computing curricula to determine which CS practices and concepts they teach and how extensively and, thus, how they prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming conc..., Search and Inclusion Criteria While the current dataset used many of the same tools as a systematic literature review to find curricula, it is not a systematic review. Unlike in literature reviews, there are no databases of integrated computing curricula to search systematically. Instead, we searched the literature for evidence-based curricula. We first searched the ACM Digital Library for papers with "(integration OR integrated) AND (computing OR 'computer science' OR CS) AND curriculum" to find curricula that had been studied. We repeated the search with Google Scholar in journals that include "(computing OR 'computer science' OR computers) AND (education OR research)" in their titles, such as Computer Science Education, Computers & Education, and Journal of Educational Computing Research. Last, we examined each entry in CSforAll's curriculum directory for curricula that matched our inclusion criteria. We used four inclusion criteria to select curricula for analysis. Our first cri..., , # Extended computing integrated curricula scored for K-12 CS standards

https://doi.org/10.5061/dryad.j6q573nnt

Framework Development and Scoring Training

Full details about the framework development and training for the scorers can be found at Margulieux, L. E., Liao, Y-C., Anderson, E., Parker, M. C., & Calandra, B. D. (2024). Intent and extent: Computer science concepts and practices in integrated computing. ACM’s Transactions on Computing Education. doi: 10.1145/3664825

Description of the data and file structure

The listed computing integrated extended curricula were scored for which concepts and practices they included. The concepts and practices are based on the K-12 CS framework.

Computer Science Practices & Non-Programming Concepts

1 = Present, Blank = Not present

Programming Concept Codes

Mutually exclusive codes

• Nothing - students do not use the concept, or they use a program ...

This document provides an overview of the characteristics of 49 empirical papers and 63 studies that were reviewed for "Virtual Reality Research in Marketing Focusing on Consumers”. This study used two distinct strategies to identify relevant research articles for inclusion in the analysis. Strategy A: Utilizing the SCImago Institutions Rankings as a reference, we targeted the top 50 ranked academic journals and extracted articles that incorporated "Virtual Reality" within their titles, abstracts, or keywords. Articles were obtained directly from the publisher of each journal. In cases where keyword search functionality was absent (e.g., Emerald), we limited the search to papers containing "Virtual Reality" in either their titles or abstracts. This approach resulted in the identification of 65 articles. Strategy B: Our search extended to various databases featured on the EBSCOhost platform, including "Academic Search Premier," "Business Source Premier," "Psychology and Behavioral Sciences Collection," "ERIC," "EconLit with Full Text," and "Teacher Reference Center." We focused on articles that pertained to both "Marketing" and "Virtual Reality," ensuring that they were peer-reviewed and available in full text. After removing duplicates identified in Strategy A, we extracted an additional 58 articles. In total, 123 articles were retrieved from both strategies. We meticulously reviewed the abstracts and keywords of each article to exclude those unrelated to Virtual Reality or not targeting consumers (e.g., articles on education, research, and development). Consequently, our final dataset included 51 articles (49 empirical papers, comprising 48 quantitative and 1 qualitative study, and 2 framework papers) for further examination.