Description of the Dataset and Research Context

This dataset was generated for a systematic review that investigated the positive and negative impacts of social media use on learning in higher education. The research hypothesized that the educational use of social media platforms can produce both beneficial and adverse effects on student engagement, academic performance, and cognitive development, depending on the platform type, pedagogical goals, and disciplinary context.

Data Collection Process

Data were gathered from peer-reviewed empirical studies published between 2011 and 2025. A systematic search was conducted in four databases: PubMed, Scopus, Web of Science, and ERIC. Eligible studies included those using qualitative, quantitative, or mixed-method approaches, focusing on social media use in higher education contexts. Only studies published in English or Spanish were included. The selection process followed PRISMA 2020 guidelines and was managed using the Rayyan platform. Calibration between two independent reviewers was carried out, and inter-rater agreement was measured using Cohen’s Kappa.

A standardized Excel spreadsheet was used to extract and structure the data, which included bibliographic details, study characteristics, country, academic field, education level, social media platforms used, educational purposes, and reported outcomes (positive or negative). Both qualitative and quantitative data were collected.

Key Findings

The data revealed that Instagram, WhatsApp, and YouTube were the most frequently used platforms. Positive outcomes often included increased student engagement, collaborative learning, and knowledge sharing. However, negative outcomes such as distraction, reduced academic focus, and information overload were also recurrent. Studies represented 38 countries, with Latin America, Europe, and Asia being the most represented regions.

A mixed-methods synthesis was performed. Quantitative patterns were analyzed using descriptive statistics in RStudio (version 2025.05.0), while qualitative data were inductively coded and grouped into thematic categories related to educational outcomes and social media use patterns.

Interpretation and Use

This dataset provides structured empirical evidence on how social media impacts university-level learning environments. It can be used by researchers conducting further meta-analyses, education policymakers exploring digital integration, and educators aiming to make informed decisions about platform use. All data were independently verified by two reviewers. The full dataset and codebook are included in the repository to support reproducibility and secondary analysis.

Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts

Overview

This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners working on natural language processing, academic research analysis, and knowledge discovery tasks.

Key Features

• 3.6+ million scientific papers with titles and abstracts
• Multi-domain coverage: Physics, Mathematics, Computer Science, Biology, Medicine, and more
• Standardized format: Consistent title and abstract columns
• Quality assured: Validated using Pydantic models and cleaned of duplicates/null values
• Ready-to-use: Pre-processed and formatted for immediate analysis
• Format: CSV
• Language: English

Dataset Statistics

Dataset Structure

cleaned_papers.csv
├── title (string): Scientific paper title
└── abstract (string): Scientific paper abstract

Data Processing Pipeline

The dataset underwent a rigorous cleaning and standardization process:

1. Data Import: Automated import from multiple sources (Kaggle API, Hugging Face)
2. Column Standardization: Mapping various column names to consistent title and abstract format
3. Data Validation: Pydantic model validation ensuring data quality
4. Duplicate Removal: Advanced deduplication based on title and abstract similarity
5. Null Value Handling: Removal of records with missing titles or abstracts
6. Quality Assurance: Final validation and statistics generation

Use Cases

This dataset is ideal for:

• Natural Language Processing: Text classification, sentiment analysis, topic modeling
• Scientific Literature Analysis: Trend analysis, domain classification, citation prediction
• Machine Learning Research: Training language models, text summarization, information extraction
• Academic Research: Bibliometric analysis, research trend identification
• Educational Applications: Building search engines, recommendation systems

Data Sources and Attribution

This dataset consolidates academic papers from the following sources:

Kaggle Datasets:

1. ArXiv Scientific Research Papers Dataset by @sumitm004
2. Cornell University ArXiv Dataset by @Cornell-University

Hugging Face Datasets:

1. ML-ArXiv-Papers by @CShorten
2. ArXiv Biology by @zeroshot
3. ArXiv Data Extended by @wrapper228
4. Stroke PubMed Abstracts by @Gaborandi
5. PubMed ArXiv Abstracts Data by @brainchalov
6. Abstracts Cleaned by @Eitanli

Update Schedule

This dataset represents a point-in-time consolidation. Future versions may include: - Additional academic sources - Extended fields (authors, publication dates, venues) - Domain-specific subsets - Enhanced metadata

License and Usage

Please respect the individual licenses of the source datasets. This consolidated version is provided for research and educational purposes. When using this dataset:

1. Citation: Please cite this dataset and acknowledge the original data sources
2. Attribution: Credit the original dataset creators listed above
3. Compliance: Ensure compliance with individual dataset licenses
4. Academic Use: Primarily intended for non-commercial, academic, and research purposes

Acknowledgments

Special thanks to all the original dataset creators and the academic communities that make their research data publicly available. This work builds upon their valuable contributions to open science and knowledge sharing.

Keywords: academic papers, research abstracts, NLP, machine learning, text mining, scientific literature, ArXiv, PubMed, natural language processing, research dataset

College Search and Fit Platforms Market Outlook

According to our latest research, the global College Search and Fit Platforms market size reached USD 1.52 billion in 2024, with robust momentum driven by increasing digitalization in the education sector and growing demand for personalized college selection tools. The market is expanding at a steady CAGR of 12.3% and is forecasted to reach USD 4.14 billion by 2033. The primary growth factor is the rapid adoption of innovative digital platforms that streamline the college application process and enhance the matching of students to institutions best suited to their academic and personal profiles.

The growth trajectory of the College Search and Fit Platforms market is significantly influenced by the global shift towards digital transformation in education. With the proliferation of smartphones, high-speed internet access, and the increasing sophistication of artificial intelligence, students and parents are now expecting more tailored, data-driven solutions for college selection. These platforms leverage advanced algorithms to analyze a student’s academic records, extracurricular activities, and personal preferences, thereby providing highly personalized recommendations. The integration of machine learning and big data analytics has further enhanced the accuracy and relevance of these recommendations, making the platforms indispensable tools for high school students navigating the complex landscape of college admissions. Additionally, the COVID-19 pandemic accelerated the adoption of virtual tools, pushing educational institutions and service providers to invest in more robust, user-friendly online platforms, which has further fueled market expansion.

Another key driver of market growth is the increasing competitiveness of college admissions globally. With more students applying to a growing number of institutions, the need for platforms that can offer comprehensive comparison tools, application tracking, and scholarship discovery has become paramount. Parents and educational consultants are also seeking platforms that offer holistic support, including access to virtual campus tours, peer reviews, and real-time updates on application statuses. The rising cost of higher education has made scholarship search features particularly attractive, with many platforms now incorporating financial aid calculators and merit-based scholarship matching to help families make informed decisions. As a result, the demand for all-in-one solutions that can handle every aspect of the college search and application process is at an all-time high, driving innovation and investment in this market.

The surge in international student mobility is also contributing to the expansion of the College Search and Fit Platforms market. As students increasingly look beyond their home countries for higher education opportunities, there is a growing need for platforms that can provide reliable information on a global scale. This includes insights into admission requirements, visa processes, campus life, and post-graduation opportunities across different regions. Many platforms are now partnering with universities worldwide to offer direct application portals, virtual counseling sessions, and multilingual support, catering to the diverse needs of an international user base. The globalization of higher education is thus creating new revenue streams and market opportunities for platform providers, while also raising the bar for service quality and data accuracy.

Regionally, North America continues to dominate the College Search and Fit Platforms market, thanks to its mature education technology ecosystem, high internet penetration, and early adoption of digital solutions. However, Asia Pacific is emerging as the fastest-growing region, driven by a burgeoning middle class, increasing cross-border student flows, and government initiatives to digitize education. Europe and Latin America are also witnessing steady growth, with rising awareness of the benefits of personalized college search tools. The Middle East & Africa, while still in the nascent stage, is expected to see accelerated adoption over the forecast period as educational infrastructure improves and digital literacy rates rise.

Product Type Analysis

The Product Type segment of the College Search and Fit Platforms market is broadly categorized into Web-based Platforms, Mobile Applications, and Hybrid Platforms. Web-based platforms have hi

Diversity enriches the educational experience by improving intellectual engagement, self-motivation, citizenship, cultural engagement, and academic skills like critical thinking, problem-solving, and writing for students of all races. Faculty role models from similar backgrounds are essential for students from traditionally underrepresented groups as it sends a powerful message of support, belonging, and the confidence to pursue higher education. However, in the biomedical sciences, the percentage of historically underrepresented tenure-track faculty is far lower than that of their white colleagues. For this to change, a strong strategic plan and commitment from the university are imperative. This scoping review will assess the size and scope of available peer-reviewed research literature on diversity programs that aim to increase the recruitment and retention of biomedical sciences research faculty and are implemented and evaluated at American Universities. The information provided in this scoping review will help universities identify novel, successful diversity-based approaches for recruiting and retaining biomedical science faculty that might suit their own unique academic and geographic needs and be incorporated into their diversity initiatives and policies. The review follows the Population-Concept-Context methodology for Joanna Briggs Institution Scoping Reviews. Relevant peer-reviewed studies published in English between June 1, 2012, to June 1, 2022, will be identified from the following electronic databases; MEDLINE (PubMed), Scopus (Elsevier), EMBASE (Elsevier), CINAHL (EBSCO), and ERIC (EBSCO). The search strings using the key variables “biomedical research faculty,” “recruitment/retention,” “diversity/ minority/ underrepresented, and “mentoring” will be conducted using Boolean logic. Two independent reviewers will conduct all title and abstract screening, followed by a full article screening and data extraction. Due to the possible heterogeneity of the studies, we hope to use either a narrative analysis and/or descriptive figures/tables to depict the results.

Academic Databas Market Overview:

The Academic Database Market Size was valued at 5.64 USD Billion in 2024. The Academic Database Market is expected to grow from 6.04 USD Billion in 2025 to 12 USD Billion by 2035. The Academic Database Market CAGR (growth rate) is expected to be around 7.1% during the forecast period (2025 - 2035).Key Academic Databas Market Trends Highlighted

The Global Academic Database Market is witnessing significant trends driven by the increasing digitalization of educational resources and the need for improved access to research information. Institutions are increasingly adopting online databases to facilitate research activities, which is primarily fueled by the growing importance of data-driven decision-making in academia. Moreover, the rise of open-access publishing is promoting the availability of scholarly content, making it essential for academic databases to integrate open-access resources to remain competitive. This shift not only enhances the scope of research materials available but also encourages collaboration among scholars globally.Opportunities to be explored in this market include the integration of advanced technologies like artificial intelligence and machine learning. These technologies can improve search capabilities, data management, and user experience in academic databases. Additionally, the expansion of educational programs in developing countries offers a fertile ground for service providers to introduce tailored academic database solutions, catering specifically to the unique needs of these markets. In recent times, theres been a noticeable trend towards consolidating multiple databases into comprehensive platforms, which allows users streamlined access to a vast array of information.This trend aligns with the growing demand for personalized learning experiences that cater to individual educational needs. Furthermore, collaborations between universities and database providers to create specialized content in areas like STEM and humanities are becoming increasingly common. These partnerships not only enhance the quality and relevance of databases but also help in addressing the distinct requirements of various academic fields. With a projected market revenue of 12.0 billion USD by 2035, the Global Academic Database Market is set to evolve significantly, shaped by these trends and the changing dynamics of the educational landscape.

Source: Primary Research, Secondary Research, WGR Database and Analyst Review Academic Databas Market Segment Insights: Academic Databas Market Regional Insights

The Global Academic Database Market exhibits notable regional variations, with North America dominating the landscape. This sector, valued at 2,014 USD Million in 2024 and expected to reach 4,286 USD Million in 2035, is critical due to its advanced infrastructure and adoption of digital resources in education and Research and Development practices. Europe is witnessing steady expansion, translating to increased digital resource utilization in academia, while APAC is experiencing moderate growth, driven by rising internet penetration and higher investments in educational technologies.South America shows signs of gradual increase, emphasizing the need for improved access to academic databases to enhance educational standards. Meanwhile, the Middle East and Africa (MEA) sector is also witnessing strong growth, reflecting a growing demand for quality educational resources to support regional development initiatives. Collectively, the diverse growth trends in these regions highlight the varied landscape of the Global Academic Database Market and underscore the opportunities that each area presents for growth and investment.

Source: Primary Research, Secondary Research, WGR Database and Analyst Review

North America: The Academic Database Market in North America is driven by advancements in AI and machine learning, supporting sectors like healthcare and education. The US government's investment in technology-enhanced learning via initiatives such as the National Education Technology Plan bolsters growth, enabling efficient access to academic resources and databases. Europe: Europe exhibits significant trends in enhancing academic databases through EU-funded projects and initiatives, such as the Horizon Europe program, focusing on research and innovation. Increased digitalization in educational institutions and strong emphasis on data protection laws, such as GDPR, drive market growth towards secure academic environments. APAC: The APAC region is marked by rapid digitization in education, with countries like China and India investing heavily in smart learning technologies. Government initiatives, such as the Digital India program, support the integration of academic databases in educational institutions, enhancing accessibility and collaboration among researchers and students.Academic Databas Market By Database Type Insights

In the Global Academic Database Market, the Database Type segment presents significant insights into how data management solutions are evolving within the educational sector. The leading segment, the Relational Database, is valued at 3 USD Billion in 2024 and is projected to reach 6 USD Billion in 2035, demonstrating its dominant position due to its well-structured data management capabilities. These databases are known for their efficient data handling, allowing academic institutions to streamline their information storage and retrieval, which is crucial for research and learning purposes.The NoSQL Database is also experiencing favorable trends, characterized by strong growth as it caters to the need for flexibility and scalability in data management, enabling handling of diverse data types commonly encountered in academic settings. Similarly, Cloud Database solutions are gaining traction with steady expansion, driven by the increasing demand for collaborative and accessible data storage options for researchers and students alike. This segment allows for enhanced data sharing capabilities, facilitating real-time access to information across various platforms.On the other hand, Distributed Database systems are gradually rising, benefiting from the need for enhanced performance and availability required in larger academic institutions. Overall, the Global Academic Database Market segmentation highlights a strategic shift towards more diverse and efficient data management solutions as educational institutions adapt to evolving technological landscapes.

Source: Primary Research, Secondary Research, WGR Database and Analyst ReviewAcademic Databas Market End User Insights

The Global Academic Database Market is characterized by a diverse mix of end users, including Educational Institutions, Research Organizations, Corporate Entities, and Libraries, each playing a critical role in driving market dynamics. Among these, Educational Institutions have historically shown strong growth, driven by increasing demands for online learning and accessible resources. Research Organizations continue to exhibit steady expansion due to rising investments in innovation and technology, necessitating robust academic databases for supporting their activities.Corporate Entities are experiencing a moderate increase in their utilization of academic databases, as more companies recognize the value of cutting-edge research for competitive advantage. Meanwhile, Libraries play an essential role in facilitating access to vast troves of academic data, maintaining relevance in an increasingly digital landscape. Overall, the Global Academic Database Market is poised to benefit from these evolving trends across its diverse end user categories, ensuring continued engagement and investment in academic resources and infrastructure to address the needs of a growing knowledge economy.

The Global Academic Database Market, particularly within the Functionality segment, encompasses various critical areas such as Data Management, Data Analytics, Information Retrieval, and Research Collaboration. Data Management has been a major player, showcasing substantial growth due to the escalating need for effective data governance and quality assurance processes. On the other hand, Data Analytics has seen strong growth as institutions increasingly leverage analytical tools to derive actionable insights from vast datasets, improving research outcomes and operational efficiency.Information Retrieval remains crucial, with steady expansion driven by the necessity for researchers to access relevant academic content swiftly amidst the burgeoning volume of published literature. Research Collaboration is gaining momentum, characterized by a rising significance as educational and research institutions aim to foster partnerships and share knowledge effectively. Collectively, these functionalities are vital in enhancing the performance and reach of academic databases globally, reflecting the ongoing transformation in how knowledge is created, shared, and utilized.

Academic Databas Market By Deployment Model InsightsThe Deployment Model segment within the Global Academic Database Market demonstrates diverse trends and growth potential. In 2024, the On-Premises model has been recognized for its robust acceptance among institutions seeking control over their data and infrastructure, showcasing a solid presence in the industry. Meanwhile, the Cloud-Based deployment model is experiencing significant adoption due to its cost-effectiveness and scalability, making it a preferred choice for many educational and research institutions. This model facilitates real-time data access and collaboration, aligning with modern needs for flexibility.The Hybrid model also plays an important role by combining the advantages of both On-Premises and Cloud-Based solutions, allowing organizations to optimize their resources efficiently. As institutions increasingly prioritize data

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.

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

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.

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.

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.

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.

Jisc's Digifest 2016 took place in Birmingham 2-3 March 2016. The official hashtag was #digitest16. This is a .csv file containing 12,499 tweets publicly published with the hashtag #digifest16.The Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. Only users with at least 3 followers were included in the archive. Retweets have been included. Data might require refining and deduplication. Please note that both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012). It cannot be guaranteed this file contains each and every Tweet tagged with #digifest16 during the indicated period, and is shared for comparative and indicative educational research purposes only. The data is shared as is. The sharing of this dataset complies with Twitter's Developer Rules of the Road. Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The profile_image_url and entities_str metadata were removed before public sharing.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors.Tweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question. The purpose of the hashtag is to organise and describe information under the relevant label. Those tagging their public tweets with a conference hashtag do so as a means to contribute to the scholarly conversation around conferences. Professional associations like the Modern Langauge Association recognise tweets as citeable scholarly outputs. Archiving scholarly tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely disappear as the time of the conference passes as Twitter's search API has known temporal limitations for retrospective historical search and collection. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. No sensitive information is contained in this dataset. This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter.

Research Hypothesis The hypothesis of this study posits that increased parental involvement in education significantly enhances student academic performance and behavioral outcomes. This involvement encompasses various strategies, including home-based support, effective communication with educators, and participation in school activities. Data Overview The data for this research was gathered through a systematic review of existing literature on parental involvement in education. Key steps included: Literature Search: Utilized academic databases such as ERIC, JSTOR, Google Scholar, and others to collect peer-reviewed articles focusing on parental engagement strategies. Selection Criteria: Studies included that examined the effects of parental involvement on student outcomes, with a focus on diverse demographics and contemporary contexts. Data Extraction: Information was systematically extracted regarding study objectives, methods, findings, and the effectiveness of various parental involvement strategies. Notable Findings Positive Impacts on Academic Performance: Direct parental involvement, such as homework support and attending school events, correlates with higher grades and test scores. Students with engaged parents exhibit greater motivation and positive attitudes toward school. Effective Engagement Strategies: Home-based involvement School-based strategies Contextual Variability: The effectiveness of parental involvement strategies varies based on cultural and socioeconomic factors. Schools that foster an inviting climate see higher rates of parental engagement. Barriers to Involvement: Common obstacles include lack of time due to work commitments, inadequate resources, and communication gaps between parents and schools. Innovative Approaches: The use of technology facilitates better communication and engagement between parents and schools. Interpretation of Data The findings underscore the critical role of parental involvement in enhancing educational outcomes. The data suggests that: Collaboration is Key: Effective partnerships between parents and educators Flexibility in Engagement: Schools should adopt flexible strategies that accommodate diverse family circumstances. Long-Term Benefits: Sustained parental engagement is linked to higher graduation rates . Practical Applications This research can inform educational policies by advocating for: Workshops for Parents: Providing resources to help parents understand curricula and support their children's learning at home. Improved Communication Channels: Utilizing technology for regular updates on student progress can bridge gaps caused by time constraints or language barriers. Inclusive School Policies: Schools create welcoming environments that encourage diverse forms of parental involvement.

Sciphi Textbooks Are All You Need

650,000 Unique Samples from K-12 to Grad School

By Huggingface Hub [source]

About this dataset

This dataset is your one-stop comprehensive resource for educational research. Featuring 650,000 unique textbook samples on a wide range of courses from the earliest days of K-12 to the most advanced graduate programs, dive deep into the educational ecosystem with an expansive library built for exploration and discovery.

Analyze course materials with confidence, examining their nuances through different perspectives and learning styles by leveraging prompted samples, completed versions, and even notes left by fellow researchers. And take your projects one step further with adjustable parameters such as models used and temperature settings aiding in optimization of results tailored to your work.

Whether you are trainer seeking fresh curriculum ideas or a student looking for primary source materials in history or literature classes, our open-source collection handles it all—one million pages strong!

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Featured Notebooks

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How to use the dataset

This comprehensive open-source textbook library for educational research is an invaluable and expansive resource for researchers, educators, and students alike. With 650,000 unique samples from K-12 to graduate school academic levels across a variety of courses, this dataset provides critical insights into the vast array of educational material available.

In order to use this dataset, there are several key columns to consider: formatted_prompt, completion, first_task, second_task, last_task , notes , title , model , and temperature . Each column contains valuable information that can help you better understand the sample textbooks included in the dataset. For example: -Formatted Prompt: The original prompt used to generate a given sample of textbook text. -Completion: The generated results from a given prompt based on the model used (the higher the temperature used when generating text output will result in more varied sentences). -Tasks: Each task corresponds with separate portions of a process that were completed (e.g.: first_task may have generated an introduction paragraph while last task may have summarized certain key points identified in earlier tasks). -Notes & Title : These two columns provide descriptive meta data about each sample including expert notes regarding further improvements or other additions that could be made as well as titles assigned by subject matter experts.

With accessibility to such informative data points users will be able to reproduce results or even start their own exploration using one cohesive dataset for all their drafting / programming needs!

Research Ideas

• Text classification for automatically assigning courses and topics to a given body of text.
• Generating natural language summaries of textbooks or educational material, such as short document descriptors for search engine optimization (SEO) purposes.
• Devising new tasks for which to train machine learning models, such as predicting the completed form of incomplete sentences in order to facilitate more accurate auto-fill capabilities when composing documents

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:---------------------|:------------------------------------------------------------------| | formatted_prompt | A prompt that has been formatted for use in the dataset. (String) | | completion | The completion of the prompt. (String) | | first_task | The first task associated with the prompt. (String) | | second_task | The second task associated with the prompt. (String) | | last_task | The last task associated with the prompt. (String) | | notes | Any additional notes associated with the prompt. (String) ...

Institute Graduation Rate Prediction Dataset is prepared from IPEDS[1] dataset by following proposed framework[2] by** Ms. Mala H. Mehta, Dr. N.C.Chauhan and Dr.Anu Gokhle** (Research Paper presented in ET2ECN-2021 International Conference). The paper will soon be published in Springer-Scopus Indexed publication.

The dataset consists of total 143 features and 11319 records of 8 student batches (from 2004 to 2011). How many students have successfully graduated within stipulated time period? Can we do the prediction of that? If low graduation rates are known in advance, institute can take prior steps to avoid low graduation rates.

Cite this dataset as - Ms. Mala Mehta Bhatt, Dr. N.C.Chauhan, & Dr. Anu Gokhale. (2021). Institute Graduation Rate Prediction Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/2914166

1 Objective 1.1 Context Education data mining (EDM) is a field related to generate useful,novel and actionable knowledge by applying miniing/ML algorithms on academic data. Knowledge generated could give unexpected benefit to education domain stakeholders.

EDM also known sometimes as Learning Analytics has various branches to work. Two Major branches are: 1. Student Performance related study 2. Institute Performance related study. Much research is done on the first aspect, however, the second aspect is not touched much.

This dataset is designed with aim of effectively predicting Institute Graduation Rates for Higher education institutions.

2 IPEDS [1] Dataset The National Centre for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations.

The Integrated Postsecondary Education Data System (IPEDS) surveys approximately 7,500 postsecondary institutions, including universities and colleges, as well as institutions offering technical and vocational education beyond the high school level. IPEDS, which began in 1986, replaced the Higher Education General Information Survey (HEGIS).

IPEDS consists of nine integrated components that obtain information on who provides postsecondary education (institutions), who participates in it and completes it (students), what programs are offered and what programs are completed, and both the human and financial resources involved in the provision of institutionally-based postsecondary education.

3 Approach 3.1 Feature Selection IPEDS dataset is a big dataset consisting of many tables and many years' databases. A framework[2] was designed to extract IGR related features and data. By following this framework, final file was created. 143 Features were selected out of which one is response variable. 3.1.1 Response Variable GBA4RTT - Graduation rate - bachelor's degree within 4 years 3.1.2 Predictor Variables 142 Predictor/Independent features are identified. (meta data is uploaded.)

3.2 Handling Missing Values Missing values are handled by applying statistical measure mean on each feature and the replacing missing values by them. 3.3 Splitting into Train-Validation-Test sets Data is split into training and testing set with 80-20% ratio. 3.4 Modeling AS Response variable considered in the study is a continuous variable. Regression Models are used to find the minimum error in prediction. 4 models are considered: Multiple linear regression, Support vector regression, Decision tree regression, XGBoost regression 4 Execution Execution process consists of below mentioned step by step procedure: 1. Preprocessing of data, 2. Splitting the data in training and testing sets, 3. Applying the models, 4. Measuring MSE,RMSE,R2, Adjusted R2 and program's running time. 5 Conclusion Mean Squared Error measured is considered here for comparison among 4 models. Minimum MSE is received in XGBoost regression algorithm followed by support vector regression, decision tree regression and multiple linear regression algorithms. Future Work Researchers could use the dataset for further analysis with different models, different dimensionality reduction techniques and education domain analysis. References [1] NCES, “National Center for Education Statistics”, Available at: https://nces.ed.gov/ipeds/use-the-data, Accessed at 2021. [2] "A Dataset preparation framework for education data mining" presented in 4th international conference on Emerging technology trends in electronics, communication and networking (ET2ECN-2021), SVNIT, Surat. Acknowledgements Thanks to NCES [1], for providing such huge open repository related to education available freely. I acknowledge all efforts put by Dr. N.C.Chauhan and Dr. Anu Gokhale in this work. Special Thanks to Vinay Bhatt, who found IPEDS repository for me, because of that only I was able to prepare this dataset.

The Finnish National Library, FinELib, conducted an Internet survey on the use of electronic publications. The survey was aimed at researchers working in universities, university hospitals and research institutes cooperating with the FinELib consortium. First questions focused on the type of research funding received by the respondents during the previous five years, the type of research (theoretical, empirical or planning and development) and the proportion of work time spent on several different tasks (e.g. reading scientific literature or source materials, teaching and guidance). One set of questions covered publications by the respondents, and media and language in which published. The respondents were asked about collaboration in publishing with other researchers in general and researchers from other fields of study, other organisations and abroad. The importance of this collaboration was surveyed. The respondents estimated what proportion of the publications they needed was in electronic form, how often they read different types of publications, how they read the publications (i.e. what means they used to view them), the age of the publications and whether they read publications outside their own discipline. Information retrieval was examined with questions about the most used sources or channels when initiating a search and which sources or channels they acquired the needed information from. The respondents were asked about using research funding to acquire electronic publications in their research projects, availability of such publications to other people, and financing of the acquisition of the publications. Views were probed on help and support wished from the library or information services of own organisation and experiences of library services abroad compared to Finnish ones. Background variables included the respondent's age, gender, organisation, highest passed examination, work experience in research profession, professional status, scientific discipline and field of research.

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 file 2: Retrieved documents. This data file lists 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.

The Anti Plagiarism Software for the Education Sector market is valued at USD 1.21 Billion in 2022 and will be USD 3.35 Billion by 2030 with a CAGR of 13.6% during the forecast period. Factors Affecting Anti Plagiarism Software for the Education Sector Market Growth

One of the main factors fuelling the market expansion is the increase in academic research by the education sector:

The primary market driver of the anti-plagiarism software market is the requirement for original and legitimate academic and research work. To publish and secure intellectual property rights over such publications, both the academic work that students submit and the research articles that researchers produce must be original. Due to this, there is a great need in the educational sector for anti-plagiarism software to ensure that the assignments and other works provided by the students are original works of their creation and not copies of the work of others. For instance, Dhaka University in Bangladesh has made anti-plagiarism software available to let students submit their assignments, theses, essays, and other writings in Bangla. Anti-plagiarism software is being used by an increasing number of market research and academic research companies globally to demonstrate the originality of their work and maintain its integration, as a proven plagiarism claim against an institution may also have legal repercussions.

Increasing government initiatives that boost the market expansion:

The usage of anti-plagiarism software is being encouraged by the government's research and education sectors to protect the integrity of academic research and stop plagiarism incidents inside their respective fields. To assess academic and research work in both Bangla and English, the university grants commissions of Bangladesh declared that it would centralize the installation of anti-plagiarism software across all universities in the nation in June 2021. Additionally, the All-India Council for Technical Education's ShodhShuddhi platform will be used by institutes and colleges across India in 2020, according to a statement from the Ministry of Human Resource Development of the Indian government.

The Restraining Factor of Anti Plagiarism Software for the Education Sector:

Software issues obstruct the market growth:

Despite the incredible benefits of plagiarism detection systems, there are still certain difficulties. For instance, universities and other educational institutions frequently struggle to incorporate anti-plagiarism technologies into their current infrastructure, which may include learning management systems, Microsoft Office, etc. To implement the software, teachers, and examiners must receive the appropriate training. Similar to the previous example, the software occasionally incorrectly flags a document as positive for plagiarism when lengthy institution or association names, quoted sentences, everyday expressions, or even reference data are utilized. For instance, the World Conference on Research Integrity revealed that in a study, the program identified 38 documents out of 449 as having been plagiarised. However, further examination revealed that just 15 documents were plagiarised.

Impact of the COVID-19 Pandemic on the Anti-Plagiarism Software for the Education Sector Market:

The epidemic of COVID-19 has heightened the demand for innovation in the school sector. The epidemic has provided chances for new technology to offer practical learning solutions, like content-checking tools, and virtual classrooms, and communication software. These solutions have been implemented by educational institutions so that teachers and students can engage in a way that is comparable to what happens in a traditional classroom. The epidemic has dramatically increased the use of remote learning tools.

Introduction of Anti Plagiarism Software for the Education Sector

Software programs are known as anti-plagiarism

instances of plagiarism in academic writings such as research papers, essays, and dissertations, among others. Through the detection and prevention of plagiarism in student works, these technologies assist educational institutions and educators in upholding academic integrity. Anti-plagiarism software often compares submitted work with existing content and utilizes vast databases and sophisticated algorithms to find similarities that could be signs of plagiarism. One o...

The supplementary materials provided in the repository support the transparency, reproducibility and robustness of the bibliometric analysis conducted in the manuscript “Political Legacies and Sustainability Education: A Bibliometric Analysis of Education for Sustainable Development Research in Higher Education.” The repository includes: (1) the full search strings used to retrieve data from Web of Science and Scopus databases, detailing all keywords, Boolean operators and filtering criteria applied, thereby enabling replication of the data collection process; (2) a .txt file containing the complete database of publications associated with higher education institutions from EU countries without a communist political legacy; and (3) a .txt file containing the corresponding database of publications from EU countries with an ex-communist political history. Both datasets consist of bibliographic records exported from the selected databases and structured according to the criteria described in the methodology, allowing direct comparison between the two corpora. Together, these materials ensure full access to the underlying data and methodological design of the study, facilitating reproducibility, transparency and further research on sustainability education and the influence of political legacies on academic knowledge production.

Scholarship Search Platforms Market Outlook

According to our latest research, the global Scholarship Search Platforms market size reached USD 1.21 billion in 2024, reflecting robust growth in response to increasing demand for digital solutions in the educational sector. The market is set to expand at a CAGR of 10.2% from 2025 to 2033, with the forecasted market size expected to reach USD 2.91 billion by the end of 2033. This growth is primarily fueled by the rising adoption of technology in education, the proliferation of scholarships globally, and the surging need for accessible, centralized platforms to connect students with funding opportunities.

A key growth driver for the Scholarship Search Platforms market is the rapid digital transformation witnessed across the education sector. With the increasing penetration of the internet and smartphones, students and educational institutions are shifting towards digital platforms for information dissemination and application processes. The convenience and efficiency offered by these platforms have led to a significant uptick in their adoption. Additionally, the growing awareness among students and parents about the availability of diverse scholarships, both locally and internationally, has further propelled the demand for centralized scholarship search services. The integration of advanced technologies such as artificial intelligence and machine learning into these platforms enhances personalization and improves the accuracy of scholarship matching, thereby attracting a larger user base.

Another significant factor contributing to market growth is the increasing number of scholarships being offered by governments, non-profit organizations, and private sector entities. As the cost of education continues to rise globally, scholarships are becoming essential for many students to access quality education. Scholarship search platforms play a crucial role in bridging the gap between scholarship providers and seekers by offering comprehensive databases and streamlined application processes. Moreover, the trend towards international education and student mobility has created a demand for platforms that can aggregate scholarships from multiple countries and present them in an accessible manner. This not only benefits students but also helps scholarship providers reach a broader, more diverse applicant pool.

The evolving regulatory landscape and policies promoting equal access to education are also driving the growth of the Scholarship Search Platforms market. Governments and educational institutions are increasingly recognizing the importance of providing transparent and equitable access to scholarship opportunities. Many regions are implementing policies that encourage the development and adoption of digital platforms to disseminate information about scholarships and financial aid. These initiatives are further supported by collaborations between educational institutions, technology providers, and scholarship agencies, resulting in the continuous enhancement of platform functionalities and user experiences. As a result, scholarship search platforms are becoming an integral part of the global education ecosystem, supporting both students and institutions in navigating the complex landscape of educational funding.

Regionally, North America currently leads the Scholarship Search Platforms market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of established educational institutions, high internet penetration, and a large number of scholarship programs contribute to North America's dominance. However, Asia Pacific is expected to witness the highest CAGR during the forecast period, driven by rapid digitalization, expanding student populations, and increasing investments in educational technology. Latin America and the Middle East & Africa are also emerging as promising markets, supported by government initiatives to improve access to education and the growing availability of scholarships for underprivileged students.

Platform Type Analysis

The Scholarship Search Platforms market is segmented by platform type into Web-based and Mobile Applications. Web-based platforms have historically dominated this segment, owing to their early adoption and widespread accessibility via desktop and laptop computers. These platforms offer robust functionality, comprehensive databases, and advanced search f