In 2023, 24 percent of prospective graduate business students in the United States were interested in hybrid programs, an increase from 16 percent in 2019. However, the overall preference in 2023 was for in-person business school programs, at 60 percent.

A file that holds the master records for all online training courses nominated for reimbursement.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Between 2015 and 2024, the number of bachelor's students who graduated from online universities in Italy steadily increased. In 2015, less than ***** people obtained their bachelor's from an online university. After nine years, the number of students more than doubled, reaching ****** graduates. In Italy, bachelor's students represented the largest group of e-learning university students, ******* people.

The COVID-19 data sets and associated Jupyter Hub notebooks are support for a manuscript describing how data science was shown to be effective in developing a transdisciplinary team and the production of novel outputs in part due to the common learning process of all team members being part of an online professional data science and analytics master’s degree program. This online curriculum helped the team members to find a common process that allowed them learn in common (Kläy, Zimmermann, & Schneider, 2015), transdisciplinary learning a key component of transdisciplinary teamwork (Yeung, 2015). Our team's Jupyter Hub files with complete coding and data set explanations are uploaded to document this teamwork and the outputs of the team.

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

According to a 2023 survey, 70 percent of undergraduate students who were studying online in the United States were White while 23 percent were Black or African-American. In comparison, 69 percent of graduate students studying online in the United States in that year were White while 24 percent were Black or African American.

Table View of Master_OP_EXP - Budgets and Actuals from FY 2016, 2017, 2018, 2019, and FYTD 2020. This View is the data source for Expense Dashboards. Update Schedule: Once per Month.

This data collection contains energy commodity production statistics for approximately 200 United Nations reporting countries for the years 1970-1979. In this file, each record refers to an individual reporting country and the quantity of its various transactions (e.g., production, imports, exports, bunkers, additions to stocks, and capacity) for a given energy commodity in a given year. Only annual data are included. The 70 types of commodities reported include solid fuels (e.g., coal, peat, and charcoal), liquid fuels (e.g., crude petroleum, gasoline, and kerosene), gases, uranium, and both industrial and public types of geothermal, hydro, and nuclear generated electricity. Information is also included on the population (in thousands) of the reporting country, the quantity of the commodity per transaction, and the date of the transaction. Supplementary data not contained in this data collection are in the introduction and footnotes of the individual tables published in the YEARBOOK OF WORLD ENERGY STATISTICS, 1979.

This deidentified Excel qualitative data set contains graduate outcomes and graduates' views on the skills they acquired while completing the Women's Health Minor (WHM) at the University of Western Australia (UWA) between 2018 and 2023. Data showed that this self-selected sample of graduates (N=38) had acquired new and diverse skills while completing the WHM.

The global massive open online course (MOOC) market size is calculated to advance at a CAGR of 32% through 2034, which is set to increase its market value from US$ 13.2 billion in 2024 to US$ 212.7 billion by the end of 2034.

Report Attribute	Detail
MOOC Market Size (2024E)	US$ 13.2 Billion
Projected Market Value (2034F)	US$ 212.7 Billion
Global Market Growth Rate (2024 to 2034)	32% CAGR
China Market Value (2034F)	US$ 23.3 Billion
Japan Market Growth Rate (2024 to 2034)	32.6% CAGR
North America Market Share (2024E)	23.9%
East Asia Market Value (2034F)	US$ 49.1 Billion
Key Companies Profiled	Alison; Coursera Inc; edX Inc; Federica.EU; FutureLearn; Instructure; Intellipaat; iverity; Jigsaw Academy; Kadenze.

Country Wise Insights

Attribute	United States
Market Value (2024E)	US$ 1.4 Billion
Growth Rate (2024 to 2034)	32.5% CAGR
Projected Value (2034F)	US$ 23.6 Billion

Attribute	China
Market Value (2024E)	US$ 1.5 Billion
Growth Rate (2024 to 2034)	32% CAGR
Projected Value (2034F)	US$ 23.3 Billion

Category-wise Insights

Attribute	xMOOC
Segment Value (2024E)	US$ 9.3 Billion
Growth Rate (2024 to 2034)	30.8% CAGR
Projected Value (2034F)	US$ 136.1 Billion

Attribute	Degree & Master Programs
Segment Value (2024E)	US$ 6.4 Billion
Growth Rate (2024 to 2034)	30.2% CAGR
Projected Value (2034F)	US$ 89.3 Billion

This resource contains the survey questions, compiled results, and code for Fisher's exact test, as associated with the following manuscript:

"Faculty Perspectives on a Collaborative, Multi-Institutional Online Hydrology Graduate Student Training Program" by Anne J. Jefferson, Steven P. Loheide, and Deanna H. McCay. Submitted to Frontiers in Water, in the research topic: “Innovations in Remote and Online Education by Hydrologic Scientists", May 2022

Abstract: The CUAHSI Virtual University is an interinstitutional graduate training framework that was developed to increase access to specialized hydrology courses for graduate students from participating institutions. The program was designed to capitalize on the benefits of collaborative teaching, allowing students to differentiate their learning and access subject matter experts at multiple institutions, while enrolled in a single course at their home institution, through a framework of reciprocity. Although the CUAHSI Virtual University was developed prior to the covid-19 pandemic, the resilience of its online education model to such disruptions to classroom teaching increases the urgency of understanding how effective such an approach is at achieving its goals and what challenges multi-institutional graduate training faces for sustainability and expansion within the water sciences or in other disciplines. To gain faculty perspectives on the program, we surveyed water science faculty who had served as instructors in the program, as well as water science faculty who had not participated and departmental chairs of participating instructors. Our data show widespread agreement across respondent types that the program is positive for students, diversifying their educational opportunities and increasing access to subject matter experts. Concerns and factors limiting faculty participation revolved around faculty workload and administrative barriers, including low enrollment at individual institutions. If these barriers can be surmounted, the CUAHSI Virtual University has the potential for wider participation within hydrology and adoption in other STEM disciplines.

Descriptive statistics-aggregate data.

THIS DATASET IS FINAL AND NO FURTHER UPDATES ARE EXPECTED. This is a report for all the relevant columns of FEMA Individual Assistance Program- amount obligated and disbursed down by program, county and municipality.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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.

According to an online survey conducted in February 2025 in the United States, ********* of LinkedIn users held a bachelor degree or equivalent. Additionally, ** percent of LinkedIn users in the U.S. held a masters degree or equivalent.

These data files contain detailed information about each public and nonpublic school in New York State, and about each school district and BOCES district in New York State. Included in the file for individual schools is information on: enrollment by race and grade, professional staff by race, professional development, para-professional staff by race and employment status, students repeating 9th grade, Title 1 status, television for instruction, internet connectivity, business/employee/community involvement, incidences of prohibited conduct, career awareness/exploration, graduate distribution, volunteer staff, free and reduced lunch, number of dropouts and GEDs, percent on welfare, limited English proficiency, special services, library media resources, computer resources, student suspension. Included for the file on each school district is information on: location, enrollment of schools within the district, counts of staff by categories, bilingual service, staff development for IT, student information systems, home to school telecommunications, children in home school, child care and early childhood programs, etc. Included in the file for each BOCES district is information on: counts of staff by categories, part-time staff, and enrollment of schools within the district.

Ces données sont basées sur les données collectées dans le cadre de l'opération nationale de collecte de données sur l’insertion professionnelle des diplômés de Master.2 nouveaux indicateurs ont été ajoutés pour la session 2020 :Taux d'emploi : part des diplômés en emploi parmi l'ensemble des diplômés actifs (en emploi ou en recherche) ou inactifsTaux d'emploi salarié en France : part des diplômés en emploi salarié en France parmi l'ensemble des diplômés actifs (en emploi ou en recherche) ou inactifsCette enquête a été menée en décembre 2022, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2020.en décembre 2021, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2019.en décembre 2020, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2018.en décembre 2019, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2017.en décembre 2018, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2016.en décembre 2017, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2015.en décembre 2016, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2014.en décembre 2015, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2013.en décembre 2014, 18 et 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2012.en décembre 2013, 30 mois après l’obtention de leur diplôme, auprès des diplômés de Master de la session 2011.Le taux d’insertion est défini comme étant le pourcentage de diplômés occupant un emploi, quel qu’il soit, sur l’ensemble des diplômés présents sur le marché du travail. Il est calculé sur les diplômés de nationalité française, issus de la formation initiale, entrés immédiatement et durablement sur le marché du travail après l’obtention de leur diplôme en 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ou 2020.L’information collectée sur le salaire porte sur le salaire net, primes comprises. Les salaires affichés correspondent aux valeurs médianes sur les emplois à temps plein. A partir de ces valeurs, on estime un salaire brut annuel, sur la base d’un taux forfaitaire de passage du net au brut de 1,3 (donnée moyenne constatée sur les salaires du secteur privé).L’enquête a été menée par les universités dans le cadre d’une charte dont les dispositions visent à garantir la comparabilité des résultats entre les établissements. La coordination d’ensemble et l’exploitation de l’enquête sont prises en charge par le ministère en charge de l'Enseignement supérieur et de la Recherche.Sources des données complémentaires :% de diplômés boursiers : données observées sur la population de l'enquête sur l'insertion professionnelle.Taux de chômage régional : INSEE - 4ème trimestre 2013 pour la session 2011, INSEE - 4ème trimestre 2014 pour la session 2012, INSEE - 4ème trimestre 2015 pour la session 2013, INSEE - 1er trimestre 2017 pour la session 2014, INSEE - 4ème trimestre 2017 pour la session 2015, INSEE - 4ème trimestre 2018 pour la session 2016, INSEE - 4ème trimestre 2019 pour la session 2017, INSEE - 4ème trimestre 2020 pour la session 2018, INSEE - 4ème trimestre 2021 pour la session 2019, INSEE - 4ème trimestre 2022 pour la session 2020.Salaire net mensuel médian régional : INSEE DADS 2011 pour la session 2011, INSEE DADS 2012 pour la session 2012, INSEE DADS 2013 pour la session 2013, INSEE DADS 2014 pour la session 2014, INSEE DADS 2015 pour la session 2015, INSEE DADS 2016 pour la session 2016, INSEE DADS 2017 pour la session 2017, INSEE DADS 2018 pour la session 2018, INSEE DADS 2019 pour la session 2019, INSEE DADS 2020 pour la session 2020 pour les 25-29 ans employés à temps plein dans les catégories socioprofessionnelles "Cadres et professions intellectuelles supérieures" et " Professions intermédiaires.Légende : nd = non disponible (aucun répondant) ns = non significatif (nombre de répondants inférieur à 30).Source : enquête d'insertion professionnelle à 18 et 30 mois des diplômés de l’université 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 et 2020 et enquête d'insertion professionnelle à 30 mois des diplômés de l’université 2011Collecte : enquête effectuée par les universités, traitements et synthèse réalisés par MESR-SIESChamp : diplômés de Master 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 et 2020 des universités de France métropolitaine et des DOM (hors Paris-Dauphine et l'Université Gustave Eiffel (pour les diplômés 2020)), de nationalité française, issus de la formation initiale, entrés immédiatement et durablement sur le marché de l’emploi après l’obtention de leur diplôme.