The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

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The QS Rankings, renowned for its esteemed university evaluations, annually releases the QS World University Rankings. The 2024 edition comprises a dataset encompassing the top 100 universities globally, with each entry defined by 12 features.

The 'rank' feature denotes the university's position in the QS rankings, offering a quantitative representation of its standing. The 'university' column identifies the institution by name. The 'overall score' is a floating-point value derived from various contributing factors, reflecting the comprehensive evaluation undertaken by QS.

Academic reputation, an integral aspect, is quantified in the 'academic reputation' feature, while 'employer reputation' gauges the institution's standing in the professional realm. The 'faculty student ratio' is calculated by dividing the faculty count by the number of students, a metric often indicative of the learning environment's quality.

'Citations per faculty' delves into the scholarly impact, measuring the total citations received by an institution's papers over five years, normalized by faculty size. The 'international faculty ratio' and 'international students ratio' shed light on the global diversity of the academic community, capturing the proportion of foreign faculty and students.

The 'international research network' employs a formula to quantify the institution's global partnerships and collaborations. 'Employment outcomes' are assessed through a formula involving alumni impact and graduate employment indices, providing insights into the professional success of graduates.

Finally, the 'sustainability' feature evaluates an institution's commitment to environmental sciences, considering alumni outcomes and academic reputation within the field. It also examines the inclusion of climate science and sustainability in the curriculum, reflecting the growing emphasis on environmental consciousness in higher education.

In essence, this dataset encapsulates a multifaceted evaluation of universities worldwide, encompassing academic, professional, and sustainability dimensions, making it a valuable resource for individuals and institutions navigating the dynamic landscape of global higher education. VALUE FOUNDS IS HIPOTICALY data 2021

CompanyData.com, (BoldData), is a leading provider of verified global business data sourced exclusively from official government and trade registries. Our global education dataset features 4.8 million schools across 190+ countries—offering accurate, up-to-date information on institutions from primary schools to universities. This makes us the ideal data partner for organizations targeting the education sector at scale.

Our school database includes detailed firmographics, institutional hierarchies, contact names, email addresses, phone and mobile numbers, type of school, language of instruction, and geographic location. Every record is verified and regularly updated to meet the highest standards of data quality, accuracy, and compliance. Whether you're targeting public or private schools, regional networks, or specific education levels, our data supports precise segmentation and engagement.

This dataset supports a wide range of use cases: international sales and marketing campaigns, CRM enrichment, compliance and KYC verification, market research, AI training, and education-focused outreach. Whether you're an EdTech provider, academic publisher, or enterprise service platform, we provide the data foundation to help you grow.

Delivery is flexible and tailored to your needs—via custom CSV exports, API integration, enrichment services, or access to our self-service platform. Backed by our broader database of over 380 million verified companies and institutions worldwide, CompanyData.com (BoldData) empowers your organization with the insights and precision needed to succeed in the global education market.

For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Content

A data frame with 777 observations on the following 18 variables.

Private A factor with levels No and Yes indicating private or public university

Apps Number of applications received

Accept Number of applications accepted

Enroll Number of new students enrolled

Top10perc Pct. new students from top 10% of H.S. class

Top25perc Pct. new students from top 25% of H.S. class

F.Undergrad Number of fulltime undergraduates

P.Undergrad Number of parttime undergraduates

Outstate Out-of-state tuition

Room.Board Room and board costs

Books Estimated book costs

Personal Estimated personal spending

PhD Pct. of faculty with Ph.D.’s

Terminal Pct. of faculty with terminal degree

S.F.Ratio Student/faculty ratio

perc.alumni Pct. alumni who donate

Expend Instructional expenditure per student

Grad.Rate Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.

--- Original source retains full ownership of the source dataset ---

India Number of Universities data was reported at 1,168.000 Unit in 2021. This records an increase from the previous number of 1,113.000 Unit for 2020. India Number of Universities data is updated yearly, averaging 282.000 Unit from Sep 1950 (Median) to 2021, with 40 observations. The data reached an all-time high of 1,168.000 Unit in 2021 and a record low of 27.000 Unit in 1950. India Number of Universities data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD001: Number of Universities.

Attitudinal/Behavioural Questions Personal educational policy priorities. Opinion on: effect of expansion (e.g. quality of students); whether colleges of advanced technology should be awarded university status; whether rural or urban university locations favoured, optimum proportion of foreign students of various age groups (following Robbins Report); quality of own department in relation to departments of the same subject at other British universities; reputation of department compared to personal assessment (for a variety of aspects). Occupation: university/college; position held; starting date; grade; promotion prospects and expectations (full details of promotion in last five years, number of times promoted, age at most recent promotion); previous appointment; previous employment (including work overseas). Degree obtained (level, where, when). Department size, satisfaction with size of university/college/department, whether pressure to do more research than respondent would like, whether adequate resources exist, whether research done during term, leave of absence (details), attitude to present university (any other preferred), any application for posts within past year, intention to make application in next three years, attitude to joining staff of another university/a new university at a higher rank/at present rank, satisfaction with town where university is located, expectation to remain at present university until retirement. Ever considered a permanent post abroad, ever considered leaving academic life permanently, ever held office in a national/ international/ academic/ learned/ professional society, details of articles/books published or in preparation, sources of information for keeping up with subject, visits abroad for academic reasons, most enjoyable aspects of work, public activities outside college. Respondents asked to agree/disagree with a number of statements concerning the academic profession, opinion on amount of support given to various subjects and facilities. Political support and interest, religious affiliation (as a child and at present). Background Variables Age, sex, marital status, children (age, sex) type of secondary school attended (self and children). Father's occupation and employment status, age parents left school, whether parents received further education, whether wife is a graduate.

This table gives an overview of government expenditure on regular education in the Netherlands since 1900. All figures presented have been calculated according to the standardised definitions of the OECD.

Government expenditure on education consists of expenditure by central and local government on education institutions and education. Government finance schools, colleges and universities. It pays for research and development conducted by universities. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances to households as well as subsidies to companies and non-profit organisations.

Total government expenditure is broken down into expenditure on education institutions and education on the one hand and government expenditure on student grants and loans and allowances for school costs to households on the other. If applicable these subjects are broken down into pre-primary and primary education, special needs primary education, secondary education, senior secondary vocational and adult education, higher professional education and university education. Data are available from 1900. Figures for the Second World War period are based on estimations due to a lack of source material.

The table also includes the indicator government expenditure on education as a percentage of gross domestic product (GDP). This indicator is used to compare government expenditure on education internationally. The indicator is compounded on the basis of definitions of the OECD (Organisation for Economic Cooperation and Development). The indicator is also presented in the StatLine table education; Education expenditure and CBS/OECD indicators. Figures for the First World War and Second World War period are not available for this indicator due to a lack of reliable data on GDP for these periods.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP has been adjusted upwards as a result of the revision. The revision has not been extended to the years before 1995. In the indicator “Total government expenditure as % of GDP”, a break occurs between 1994 and 1995 as a result of the revision.

Data available from: 1900

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes on 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also showed that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further...

Analysis of ‘QS World University Rankings 2017 - 2022’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/padhmam/qs-world-university-rankings-2017-2022 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

QS World University Rankings is an annual publication of global university rankings by Quacquarelli Symonds. The QS ranking receives approval from the International Ranking Expert Group (IREG), and is viewed as one of the three most-widely read university rankings in the world. QS publishes its university rankings in partnership with Elsevier.

Content

This dataset contains university data from the year 2017 to 2022. It has a total of 15 features. - university - name of the university - year - year of ranking - rank_display - rank given to the university - score - score of the university based on the six key metrics mentioned above - link - link to the university profile page on QS website - country - country in which the university is located - city - city in which the university is located - region - continent in which the university is located - logo - link to the logo of the university - type - type of university (public or private) - research_output - quality of research at the university - student_faculty_ratio - number of students assigned to per faculty - international_students - number of international students enrolled at the university - size - size of the university in terms of area - faculty_count - number of faculty or academic staff at the university

Acknowledgements

This dataset was acquired by scraping the QS World University Rankings website with Python and Selenium. Cover Image: Source

Inspiration

Some of the questions that can be answered with this dataset, 1. What makes a best ranked university? 2. Does the location of a university play a role in its ranking? 3. What do the best universities have in common? 4. How important is academic research for a university? 5. Which country is preferred by international students?

--- Original source retains full ownership of the source dataset ---

The study used an explanatory sequential mixed method design. This method is appropriate for examining the employment status of STEM graduates in terms of gender as well as the time it takes for graduates to secure their first job after graduating. The method is also employed to look at how staff in higher education supports female graduates in their search for employment after graduation. By design, this study collects data in a sequential fashion, starting with quantitative data and moving on to qualitative data that provide context for the quantitative data.Both primary and secondary sources of data were employed in the study (See Figure A). While information from secondary sources was gathered using Eric, Scopus, and Google search engines, information from primary sources was gathered through questionnaires and interviews. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used to conduct the analysis. Using the keywords employment status, duration of job search, and gender-responsive support of higher education, the first 221 articles were collected. Only 15 articles were chosen when PRISMA used the inclusion and exclusion criteria to filter out publications gathered between 2012 and 2024. The information gathered from secondary sources was utilized to triangulate the findings of the primary data sources. The following figure shows the data sources.Figure A: Data sources for the study (see the Description Word Doc. in the dataset)Based on the explanatory sequential mixed method design, quantitative data analysis was first carried out. In order to determine whether there were statistical differences in the employment status and the time it took for male and female STEM engineering graduates to find jobs, the chi square test was employed. An analysis of the degree to which higher education institutions assist female graduates in their job search was also done using an independent samples t-test. The viewpoints of academics from these related universities and prospective employers of STEM graduates were captured through the use of qualitative data.

Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

This dataset contains data on number of students in Latvia in 1919-1939. Dataset "Number of Students in Universities in Latvia, 1919-1939" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

The Global Longitudinal University Enrolment Dataset (GLUED) has been deaccessioned. Under no circumstances may a list of GLUED hitherto downloaded be disseminated, published or used for commercial purposes.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location.

For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions.

For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

For our school survey, we select only schools that are supervised by the Minsitry or Education or are Private schools. No schools supervised by the Ministry of Defense, Ministry of Endowments, Ministry of Higher Education , or Ministry of Social Development are included. This left us with a sampling frame containing 3,330 schools, with 1297 private schools and 2003 schools managed by the Minsitry of Education. The schools must also have at least 3 grade 1 students, 3 grade 4 students, and 3 teachers. We oversampled Southern schools to reach a total of 50 Southern schools for regional comparisons. Additionally, we oversampled Evening schools, for a total of 40 evening schools.

A total of 250 schools were surveyed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below:

• School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey questions.

• Survey of Public Officials: The Survey of Public Officials collects information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey is a streamlined and education-focused version of the civil-servant surveys that the Bureaucracy Lab (a joint initiative of the Governance Global Practice and the Development Impact Evaluation unit of the World Bank) has implemented in several countries. The survey includes questions about technical and leadership skills, work environment, stakeholder engagement, impartial decision-making, and attitudes and behaviors. The survey takes 30-45 minutes per public official and is used to interview Ministry of Education officials working at the central, regional, and district levels in each country.

Sampling error estimates

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level.

Online education has become more prevalent in the 21st century, especially after the COVID-19 pandemic. One of the major trends is the learning via Massive Open Online Courses (MOOCs), which is increasingly present at many universities around the world these days. In these courses, learners interact with the pre-designed materials and study everything mostly by themselves. Therefore, gaining insights into their satisfaction of such courses is vitally important to improve their learning experiences and performances. However, previous studies primarily focused on factors that affected learners’ satisfaction, not on how and what the satisfaction was. Moreover, past research mainly employed the narrative reviews posted on MOOC platforms; very few utilized survey and interview data obtained directly from MOOC users. The present study aims to fill in such gaps by employing a mixed-methods approach including a survey design and semi-structured interviews with the participation of 120 students, who were taking academic writing courses on Coursera (one of the world-leading MOOC platforms), at a private university in Vietnam. Results from both quantitative and qualitative data showed that the overall satisfaction of courses on Coursera was relatively low. Furthermore, most learners were not satisfied with their learning experience on the platform, primarily due to inappropriate assessment, lack of support, and interaction with teachers as well as improper plagiarism check. In addition, there were moderate correlations between students’ satisfaction and their perceived usefulness of Coursera courses. Pedagogically, teachers’ feedback and grading, faster support from course designers as well as easier-to-use plagiarism checking tools are needed to secure learners’ satisfaction of MOOCs.

An Taisce, in co-operation with the Local Authorities, run the national Green Schools 9-flag program. Schools carry out a number of tasks, run educational programs and environmental projects which are incorporated into everyday school-life. The themes are:Litter & Waste ,Energy, Water, Travel, Biodiversity, Global Citizenship Litter & Waste, Global Citizenship Energy ,Global Citizenship Marine Environment, Global Citizenship TravelThere are 145 schools in Fingal registered as active in the An Taisce Green Schools Program. Got to www.fingal.ie for more information on Green Schools Programme details.. Fingal County Council invests in a number of school projects every year.Each flag has to be maintained as the school progresses towards the new flags. Fingal County Council assists the schools by providing information and support and environmental presentations to the students. In Feb/ March we also carry out all the Green Flag Assessments.We congratulate all the schools on their efforts – the green schools coordinators and committees, the teachers and students and caretakers and all involved put in a lot of work to make this possible.

Despite extensive critiques of university rankings highlighting their emphasis on reputation metrics over teaching quality and equity, empirical validation remains limited. This study addresses this gap by analysing relationships between QS World University Rankings indicators and overall scores for Australian universities (2025 dataset). Using correlational analyses on publicly available data, the findings identify Academic Reputation, Employer Reputation, and Employment Outcomes as influential metrics, while Faculty-to-Student Ratio and Sustainability show limited or negative correlations. Results further suggest systemic biases favouring larger, research-intensive institutions, potentially disadvantaging smaller or specialised universities regardless of academic quality. Although focused on the Australian higher education context, this research contributes timely empirical insights relevant globally. The findings inform university leaders, policymakers, and scholars, providing evidence to critically evaluate ranking methodologies and advocating for transparent, equitable, and pedagogically inclusive approaches to assessing institutional excellence.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

The research project Monitoring is dedicated to the various aspects of educational and occupational choices of high-school graduates and freshmen. Since 1995 high-school students who were in their senior year and college and university freshmen have been interviewed (telephone interview or written interview) in the form of representative surveys. The question topics were motives, information behaviour, decision-making and current developments in the education sector and the professional world. The main aim of the survey is to gain a scientific basis for the information service. Plans and their realisation are analysed in the temporal longitudinal section. The focus points of the survey are decision making and the satisfaction with the occupational choice as well as attitudes and values.