According to a 2022 survey, an increasing share of learning and development (L&D) professionals intended to deploy large scale upskilling or reskilling programs compared to 2021. Furthermore, ********* of respondents said that they intended to deploy data analysis or analytics training programs at their organizations in 2022.

In 2022, the biggest reason for employee training not reaching its desired outcome was ************************************************************************. This meant that new employees had to find a balance between their work schedule and training, with this often proving to be a challenge.

Between 2021 and 2022, organizations' talent strategies changed significantly, with ** percent claiming they at least had on in 2021, with this figure dropping to ** percent in 2022. In 2022, approximately ** percent of respondents said that their talent strategy completely aligned with their business strategy, an increase of almost ** percent since 2021.

In 2022, approximately ** percent of learning and development (L&D) professionals in the United States cited leadership and management training as their top priority. During the same period, tracking skills development was also regarded as a top priority for ** percent of U.S.-based L&D professionals.

Workplace learning and development spending per employee has seen fluctuations over the years, with a notable decrease in 2022. Despite this recent dip, the overall trend shows a commitment to employee growth, with spending reaching ***** U.S. dollars per worker in 2023. This investment in human capital reflects the growing importance of continuous learning in today's rapidly evolving work environment. Adapting to new technologies As companies navigate the integration of artificial intelligence into their operations, learning and development strategies are evolving. In 2023, U.S. companies planned to invest in online courses as a primary method for AI training, while also valuing face-to-face training and live events. This balanced approach to learning reflects the complex nature of new technologies and the need for diverse training methods. Interestingly, by 2024, AI had become a significant tool in human resources, with ** percent of HR professionals reporting its use in recruiting, interviewing, and hiring processes. (1413448, 1500122) Measuring impact and optimizing resources Organizations are increasingly focused on measuring the impact of their learning and development initiatives. In 2023, L&D professionals identified performance reviews as the most useful method for assessing the impact on overall business performance, followed by employee productivity metrics. This emphasis on measurable outcomes aligns with the need to optimize training expenditures, especially in light of fluctuations in corporate training budgets. For instance, U.S. corporate training expenditure decreased by almost **** billion U.S. dollars in 2024 compared to the previous year, highlighting the importance of efficient and effective learning strategies. (1472187, 788521)

The Higher Education Research and Development (HERD) Survey is the primary source of information on R&D expenditures at U.S. colleges and universities. The survey collects information on R&D expenditures by field of research and source of funds and also gathers information on types of research, expenses, and R&D personnel. The survey is an annual census of institutions that expended at least $150,000 in separately accounted for R&D in the fiscal year. This dataset includes HERD assets for 2022.

The main aim of this work was to explore how online training events, outside of traditional academic courses, can support Social Science, Arts and Humanities (SHAPE) students to develop data skills.

There is international demand for data skills in the workplace, and Governments are increasingly concerned with how quantitative data skills are acquired for 21st Century jobs. If SHAPE students are trained in data skills they can enter into statistical professions and data careers. Online learning has risen significantly in the last decade, and even more so since the COVID-19 pandemic. Within the field of social research methods, online learning has grown rapidly and there is an emerging literature on the pedagogy. Data skills are vital for research and there is acknowledgement that online learning can play a role in data skills training.

The data collection contains anonymised transcripts from 8 qualitative semi-structured individual interviews with SHAPE students who attended UK Data Service online training events. The interviews explored why the student had attended the UKDS event, what they learnt from it, how it has supported the development of data skills and how this will help their career. Ten students participated in the original study – we have consent from eight of ten the students to share their anonymised data in this repository.

OECD Education statistics database includes the UNESCO/OECD/EUROSTAT (UOE) database on education covering the outputs of educational institutions, the policy levers that shape educational outputs, the human and financial resources invested in education, structural characteristics of education systems, and the economic and social outcomes of education, learning and training throughout life, including on employment and unemployment. Also included in the database are the PISA 2015 dataset, Teaching and Learning International Survey (TALIS) data, the annual Education at a Glance data and data relating to Gender equality in education.

Snapshot img

Corporate Training Market Size 2025-2029

The corporate training market size is forecast to increase by USD 43.86 billion, at a CAGR of 7.8% between 2024 and 2029.

The market is experiencing significant shifts driven by the emergence of cost-effective e-learning training modules and the integration of IoT and wearable devices. Companies are increasingly adopting e-learning solutions due to their affordability and flexibility, enabling employees to access training materials anytime, anywhere. This trend is particularly relevant in today's remote work environment. Meanwhile, the Internet of Things (IoT) and wearable devices are revolutionizing corporate training by providing real-time data and analytics, allowing organizations to monitor employee performance and tailor training programs accordingly. However, technical constraints pose a challenge to implementing these advanced training methods. Companies must ensure their IT infrastructure can support these technologies and that employees have the necessary skills and resources to utilize them effectively. Addressing these challenges will be crucial for organizations seeking to stay competitive and provide their workforce with the most effective and up-to-date training solutions.

What will be the Size of the Corporate Training Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic market activities unfolding across various sectors. Training materials are no longer static documents, but rather interactive and accessible through learning platforms that support professional development and training administration. Virtual classrooms and instructor-led training are seamlessly integrated with e-learning platforms, enabling mobile learning and sales training. Compliance training, assessment tools, and performance support are essential components, ensuring training effectiveness and adherence to learning objectives. Knowledge management and training content development are crucial for management training and skill development. Course authoring tools and e-learning authoring tools facilitate rapid elearning and video conferencing, while learning analytics and xapi compliance offer valuable insights into training delivery methods and training needs analysis. The integration of collaboration tools, self-paced learning, and online courses fosters employee engagement and career advancement. Talent management and certification programs leverage adaptive learning and api integrations to provide personalized learning experiences. Data security and training budget considerations are essential aspects of this evolving landscape. The market is characterized by ongoing innovation, with emerging trends such as AI-powered learning, simulation training, and on-the-job training shaping the future of professional development. The continuous integration of these technologies and methodologies ensures that corporate training programs remain effective and adaptable to the ever-changing business landscape.

How is this Corporate Training Industry segmented?

The corporate training industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductTechnical trainingNon-technical trainingTypeBlendedE-learningIndustriesRetailPharmaceutical and HealthcareFinancial ServicesProfessional ServicesPublic EnterprisesInformation TechnologyRetailPharmaceutical and HealthcareFinancial ServicesProfessional ServicesPublic EnterprisesInformation TechnologyOthers IndustriesTraining PurposeLeadership DevelopmentComplianceSkill DevelopmentCompany SizeSmall BusinessesMedium BusinessesLarge EnterprisesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalySpainUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Product Insights

The technical training segment is estimated to witness significant growth during the forecast period.The market for non-technical skills is experiencing significant growth due to the increasing importance of soft skills in driving employee productivity. This market segment includes training in intangible areas such as leadership development, team-building, problem-solving, and management techniques. Training programs in this sector are facilitative in nature, focusing on learner-centric outcomes defined by behavioral objectives. Organizations are increasingly prioritizing employee skills development for personal and professional growth. Learning platforms and training materials play a crucial role in delivering non-technical training. Compliance with privacy regulations and accessibility standards is essential for these

In 2022, learning and development professionals considered ************************* to be the most important aspect of workplace training. Between 2021 and 2022, two new areas of focus deemed to be important were introduced into the survey in diversity, equity, and inclusion, and digital upskilling/digital transformation.

United States - Employed full time: Wage and salary workers: Training and development managers occupations: 16 years and over: Men was 23.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Training and development managers occupations: 16 years and over: Men reached a record high of 37.00000 in January of 2022 and a record low of 15.00000 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Training and development managers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on June of 2025.

Learning & development facilities for students in schools, colleges, and universities in India - access to computers, internet, scholarships, playground, etc.

Human Development Index 2022 (HDI)

This dataset is derived from the United Nations Development Programme (UNDP) Human Development Reports, focusing on the latest Human Development Index (HDI) for the year 2022. It has been formatted into easy-to-process CSV tables to facilitate analysis and exploration in Data Science projects.

The dataset provides information on HDI rankings and various social factors that influence this key performance indicator, including:

1. Social Inequality: Metrics reflecting disparities in access to resources and opportunities.
2. Gender Inequality: Indicators measuring inequities between genders in health, education, and economic participation.
3. Trends in HDI: Historical data showcasing changes in human development over time.

Suggestions for Potential Uses in Data Science: 1. Exploratory Data Analysis (EDA): Visualize global patterns and regional variations in HDI and its influencing factors. 2. Predictive Modeling: Use trends to predict future HDI rankings or identify factors with the highest impact on human development. 3. Clustering and Segmentation: Identify groups of countries with similar HDI profiles using unsupervised learning methods. 4. Correlations and Insights: Explore relationships between HDI and socioeconomic factors such as education levels, healthcare access, and income equality. 5. Policy Recommendations: Analyze insights to suggest targeted interventions for sustainable human development.-

This dataset serves as a resource for researchers, data enthusiasts, and policymakers aiming to understand and improve human development across the globe.

Data Source: https://hdr.undp.org/data-center/documentation-and-downloads

🇸🇦 사우디아라비아

Workplace safety and injury data for Learning Center, Child Development Preschool

# Title
Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0)

# Research audience
Education researchers interested in the application of the “value creation stories” (VCS) conceptual framework elaborated by Etienne Wenger et al. in the study of communities of practice and other types of digital communities.

# Credits

## Author
The Geneva Learning Foundation
18 avenue Louis Casaï
CH-1209 Geneva, Switzerland
research@learning.foundation

### Principal Investigator and corresponding author
Reda Sadki, The Geneva Learning Foundation (TGLF)
reda@learning.foundation

## Project partners
Bridges to Development
University of South Australia Centre for Change and Complexity in Learning (C3L)

## Roles and responsibilities
- Design: The Geneva Learning Foundation
- Implementation (sample collection): The Geneva Learning Foundation
- Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L)
- Anonymization: The Geneva Learning Foundation and Bridges for Development
- Data cleaning: Bridges to Development
- Submission: The Geneva Learning Foundation

## Funding sources or sponsorship that supported the data collection
Wellcome, Bill & Melinda Gates Foundation (BMGF)

## Recommended citation
The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: 10.5281/zenodo.7763922

# Description of the sample

## File list:

This file is IA2030_FLC_2022_Value_Creation_Stories.README.md

IA2030-EN_FLC_2022_Value_Creation_Stories-questions_mapping.csv : List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.

IA2030-EN_FLC_2022_Value_Creation_Stories.csv : Dataset Response of participants that replied in English. (n: 2101, obs:5601) - Version 1: Geneva Learning Foundation, 31 March 2023.

IA2030-FR_FLC_2022_Value_Creation_Stories-questions_mapping.csv: List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.

IA2030-FR_FLC_2022_Value_Creation_Stories-Google_translation.csv: Dataset Response of participants that replied in French translated to English using “Google Translate” (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023.

IA2030-FR_FLC_2022_Value_Creation_Stories.csv: Dataset Response of participants that replied in French (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. Relationship between files: The questions codes data set are the same code as the column variables and can be connected.

## Relationship between files
The questions codes data set are the same code as the column variables and can be connected.

## Related data sets
This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities.

Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit [insights@learning.foundation](insights@learning.foundation)

## Other publicly accessible locations of the data
The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/

## 1. Purpose and Objectives

### Primary goal of the survey:
This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022):
1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and
2. collect and measure programme participants’ value creation stories (VCS) during the programme.

Martin de Laat’s “value creation stories” (VCS) has been used primarily in small-scale, qualitative studies of communities of practice, online forums, and education activities.

This data set includes both quantitative (Likert) and qualitative (open text) responses to the VCS questions, collected over a period of four months (7 March – 20 June 2022) from a cohort that began with 6,185 participants on the start date.

## 2. Population and Sample

The target population were participants of the Geneva Learning Foundation’s Movement for Immunization Agenda 2030 (IA2030) learning programme. The initial cohort admitted to the programme was 6,185 individuals from 99 countries. Only participants who were formally admitted to the programme received the invitation to complete the survey.

Programme participants were free to choose if and when to report (self-selection), and their responses were not checked against any other measures (self-reporting).

### Languages: French and English

## 3. Survey Design and Methods

Data collection period: 7 March 2022 – 20 June 2022

Between 7 March and 20 June 2023, participants in the Geneva Learning Foundation’s “Immunization Agenda 2030” (IA2030) Movement Full Learning Cycle (FLC) were asked to respond to a questionnaire titled “How are you doing?”.

Participants received a personalized email with the request to share feedback about their experience during the week. The link to share feedback was also included in other reminder and information emails sent in response to participant needs.

The first survey was launched on the 11 of March 2022 and the last at 17 of March 2022, totalizing 15 requests. Participants could answer the survey at any time and as many times that they wished.

The group of 6,185 participants grew over the course of the Cycle, as additional participants were able to join the initiative throughout the four-month period.

### Software- or Instrument-specific information needed to interpret the data
- Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs)
- Methods used for removing or anonymizing personal identifiers or sensitive information:
- Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site [Miracle Salad](https://www.miraclesalad.com/webtools/md5.php.).Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.*Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.**
- Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) The macro did not account for typos, in case any country information is found please contact: [research@learning.foundation](research@learning.foundation)

### Data collection start and end dates:
7 March 2023 until 20 June 2023

#### Events or circumstances during data collection that may have influenced results:
No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022.

## 4. Data Processing and Cleaning

- Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions.
- Data transformations or imputations: None
- Treatment of outliers or extreme values: None

## 5. Variables and Measures

The survey included Likert scale questions and qualitative open texts based the conceptual framework for Value Creation Stories (VCS) developed by Wenger et. al. (2011). There are no derived or calculated variables. Items are Likert scale, multiple choice, and open text.

## 6. Data Quality and Reliability
All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample.

## 7. Data Privacy and Anonymization

### Methods used for removing or anonymizing personal identifiers or sensitive information:
- Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site https://www.miraclesalad.com/webtools/md5.php. Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.
- Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.
- Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.)

## 8. Data Availability and Accessibility
This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)”
https://zenodo.org/communities/ia2030/

Requests for additional information should be addressed to research@learning.foundation.

This is a subset of data collected by The

Development Plan 2022-2028 - Proposed Education Sites DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Proposed Education Sites as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

1. Reference Year: 2022/20232. Continuous Professional Development of Teachers (CPD) is provided to teachers and schools through three core services: Professional Development Service for Teachers (PDST), Junior Cycle for Teachers (JCT) and National Induction Programme for Teachers (NIPT)3. The indicators included focus on the CPD provided by PDST only and do not include individual teacher engagements with seminars or online courses provided by PDST or cover teacher training provided through the other services4. As such, these indicators should be considered as being under development5. CPD is essential to ensure all teachers are equipped with the knowledge and skills for an evolving teaching and learning environment, in particular it is required to support major areas of curricular development and reform6. ICT: Information and Communications Technology

United States US: Government-Financed GERD: % of GDP data was reported at 0.650 % in 2022. This records a decrease from the previous number of 0.654 % for 2021. United States US: Government-Financed GERD: % of GDP data is updated yearly, averaging 0.786 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 1.245 % in 1985 and a record low of 0.650 % in 2022. United States US: Government-Financed GERD: % of GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Gross Domestic Expenditure on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

RU: Higher Education Total R&D Personnel: Compound Annual Growth Rate data was reported at -6.057 % in 2020. This records a decrease from the previous number of 8.330 % for 2019. RU: Higher Education Total R&D Personnel: Compound Annual Growth Rate data is updated yearly, averaging -0.111 % from Dec 1995 (Median) to 2020, with 26 observations. The data reached an all-time high of 8.330 % in 2019 and a record low of -12.590 % in 1995. RU: Higher Education Total R&D Personnel: Compound Annual Growth Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Russian Federation – Table RU.OECD.MSTI: Number of Researchers and Personnel on Research and Development: Non OECD Member: Annual.

In response to Russia's large-scale aggression against Ukraine, the OECD Council decided on 8 March 2022 to immediately suspend the participation of Russia and Belarus in OECD bodies. In view of this decision, the OECD suspended its solicitation of official statistics on R&D from Russian authorities, leading to the absence of more recent R&D statistics for this country in the OECD database. Previously collected and compiled indicators are still available.

The business enterprise sector includes all organisations and enterprises whose main activity is connected with the production of goods and services for sale, including those owned by the state, and private non-profit institutions serving the above-mentioned organisations. In practice however, R&D performed in this sector is carried out mostly by industrial research institutes other than enterprises. This particularity reflects the traditional organisation of Russian R&D.

Headcount data include full-time personnel only, and hence are underestimated, while data in full-time equivalents (FTE) are calculated on the basis of both full-time and part-time personnel. This explains why the FTE data are greater than the headcount data.

New budgetary procedures introduced in 2005 have resulted in items previously classified as GBARD being attributed to other headings and have affected the coverage and breakdown by socio-economic objective.