Scientists are increasingly engaging the web to provide formal and informal science education opportunities. Despite the prolific growth of web-based resources, systematic evaluation and assessment of their efficacy remains limited. We used clickstream analytics, a widely available method for tracking website visitors and their behavior, to evaluate >60,000 visits over three years to an educational website focused on ecology. Visits originating from search engine queries were a small proportion of the traffic, suggesting the need to actively promote websites to drive visitation. However, the number of visits referred to the website per social media post varied depending on the social media platform and the quality of those visits (e.g., time on site and number of pages viewed) was significantly lower than visits originating from other referring websites. In particular, visitors referred to the website through targeted promotion (e.g., inclusion in a website listing classroom teaching resources) had higher quality visits. Once engaged in the site's core content, visitor retention was high; however, visitors rarely used the tutorial resources that serve to explain the site's use. Our results demonstrate that simple changes in website design, content and promotion are likely to increase the number of visitors and their engagement. While there is a growing emphasis on using the web to broaden the impacts of biological research, time and resources remain limited. Clickstream analytics provides an easily accessible, relatively fast and quantitative means by which those engaging in educational outreach can improve upon their efforts.

Monthly statistics for pages viewed by visitors to the Queensland Government website—Education and training franchise. Source: Google Analytics

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

Data includes:

• board number
• board name
• board language
• board type
• district name
• city
• grade 6 EQAO reading results
• progress in Grade 6 EQAO reading results
• grade 10 OSSLT results
• progress in Grade 10 OSSLT results
• credit accumulation by the end of Grade 10 and 11
• progress in credit accumulation by the end of Grade 10 and 11
• primary class size with 20 students or less
• progress in primary grade classes with 20 students or less
• four-year and five-year graduation rates
• progress in the four-year and five-year graduation rates

This data is shown as reported by:

• the Education Quality and Accountability Office (EQAO) 2022-2023
• school boards in the Ontario School Information System (OnSIS) 2021-2022 (Credit Accumulation) and 2022-2023 (Graduation Rate)

This data is also available through the Ministry of Education's "https://www.app.edu.gov.on.ca/eng/bpr/index.html">School Board Progress Report website.

Abstract

This RCT tests the impacts of delivering educational podcasts with Math and English lessons in an Interactive Voice Response (IVR) system, an automated phone system technology that allows incoming callers to access information via pre-recorded messages without having to speak to a tutor. Baseline survey data collection occured in October 2022. The baseline included a school survey and assessement tests for both English and Mathematics. These examinations were administed in a classroom environment using standard textbook and Trends in International Mathematics and Science Study (TIMSS) problems.

Geographic coverage

The field aims to cover 9 districts in 3 provinces (Bagmati, Koshi, and Madhesh). Among these, Madhesh and Koshi belong to the Terai region (southern part of Nepal) while Bagmati belongs to the Hilly region.

Districts: - Dhading, Kavre, Nuwakot, Sindhuli, and Sindhupalchok in the Bagmati Province - Dhanusha, Sarlahi, and Sunsari in the Madhesh Province - Morang in the Koshi Province

Analysis unit

Students

Kind of data

Sample survey data [ssd]

Sampling procedure

This study uses a RCT to assess whether there is a causal link between the program and changes in outcomes. The evaluation of this employs a three-arm clustered RCT design (two treatment groups and one control group). Randomization is done in 2 stages: We first pick 223 schools from a pre-existing list provided by our partner organizations. We then randomly distribute these 223 schools into the three study arms: T1 (self-help), T2 (assisted), and C (control). T1 and T2 will have 74 schools each and C will have 75 schools. From each school, we will randomly select 15 students on average to participate in the program. We will ensure that about half of the students are female when we randomly select the students to be treated from each treatment school.

a) T1: Self-Help Group (74 schools, 1,057 students) b) T2: Assisted Group (74 schools, 1,060 students) c) T3: Control (75 schools, 2,168 students)

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three survey instruments were used for this study - a School Survey Questionnaire and two Assessment Test Questionnaires (English and Mathematics). The School Survey Questionnaire including questions on school infrastructure and the Assessment Test Questionnaires were reviewed by the authors. Questions in the Assessment Test instruments were selected based on the national curriculum of Nepal from a standard textbook and a few questions also came from the Trends in International Mathematics and Science Study (TIMSS). The questionnaires were administered in the Nepali language. They are provided in both English and Nepali and are available for download here.

Response rate

We conducted an assessment test for 6,980 students in 2022, but as the baseline was conducted a few months earlier than our intervention and the program started after the students progressed to the next level, we checked the final students list BEFORE the intervention started, and the final students who remained in the schools are 6,433 (547 students dropped out). We then did a balance check across different groups among the final list of students.

Abstract copyright UK Data Service and data collection copyright owner.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.
Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).
The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

School Survey:
A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:

• the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups
• school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems
• equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education

The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives.

A further round of school surveys took place during the 2016-2017 school year. The key focus areas for these were:

• benchmarking levels of student attainment and progress in key learning domains
• effects of school and teacher quality, and school effectiveness
• educational transitions at age 15

The 2016-2017 school surveys focused on the level of schooling accessed by 15-year-olds in each country, so including Grade 7 and 8 students in Ethiopia (upper primary level), Grade 9 students in India (lower secondary level), and Grade 10 students in Vietnam (upper secondary level).

The School Survey data are held separately for each country. The India data are available from the UK Data Archive under SN 7478 and SN 8359, the Vietnam data are available from SN 7663 and SN 8360, and the Peru data have been archived under SN 7479 (no 2016-2017 survey).

Further information is available from the Young Lives School Survey webpages.


Main Topics:

The Ethiopia survey included data collection at the school, class and pupil level, and involved the Director / Head teacher, the Maths and English teachers, and the Young Lives child. The instruments included in the survey were:

• Director questionnaire - collected background data on the director and the school
• Teacher questionnaire - collected background data on teachers, including teacher motivation and section-level information
• Student questionnaire - collected background data on students (including academic support within and beyond school, psychosocial measures and perceptions of the classroom instructional environment)
• School facilities observation - collected data on school infrastructure and facilities
• Teacher professional knowledge questionnaire - collected...

Abstract

Survey data collected for a randomized control trial testing impacts of SMS reminders to take-up remote instruction (radio broadcasts), phone tutorials from private school teachers, and phone tutorials from public school teachers; and for the publication 'Live tutoring calls did not improve learning during the COVID-19 pandemic in Sierra Leone'.

Geographic coverage

The survey covered 25 schools in four districts in Sierra Leone

Analysis unit

School, Student

Kind of data

Sample survey data [ssd]

Sampling procedure

All students present at the 25 study primary schools before all schools were closed for COVID were sampled

Mode of data collection

Other [oth]

Research instrument

The questionnaire is provided for download in English

Response rate

The response rate was 90%

Abstract

This impact evaluation was conducted by IDinsight for STIR Education in Delhi and Uttar Pradesh in India, and was funded by a World Bank Strategic Impact Evaluation Fund grant. The study seeks to evaluate the impact of STIR's purely motivational, pedagogically neutral, teacher-focused model on student learning levels. STIR works with teachers in low-cost and government schools in order to improve student learning by empowering teachers to act as change-makers and to innovate to overcome challenges in the classroom. IDinsight conducted two three-armed randomized control trials. The study looks at outcomes from 180 Affordable Private Schools (APS) in Delhi and 270 government schools in the Raebareli and Varanasi districts of Uttar Pradesh. The study began in early 2015, and lasted two academic years. In addition to measuring STIR's impact in two different contexts, the study simultaneously tests two iterations of STIR's model in these two contexts.

Geographic coverage

One district in Delhi - East Delhi, and two districts in Uttar Pradesh - Raebareli and Varanasi

Analysis unit

For student learning, the basic unit of analysis is students. For classroom practices, the basic unit of analysis is teachers. For teacher motivation, the basic unit of analysis is teachers.

Universe

• 180 Affordable Private Schools in Delhi, 540 teachers amongst these schools and 5,400 students
• 270 Government Schools in Uttar Pradesh, 810 teachers amongst these schools and 8,100 students

Kind of data

Sample survey data [ssd]

Sampling procedure

Baseline Respondent Identification and Sampling Strategy:

Delhi:

Teacher Motivation: STIR initially did a search process of several hundred Affordable Private Schools (APS) in east Delhi. From these schools, STIR passed school names onto IDinsight where the teachers might be interested in working with IDinsight. IDinsight attempted to sample all schools for the Teacher Motivation survey. In total, IDinsight interviewed 1,259 teachers for the Teacher Motivation survey.

Classroom Observation: From these 1,259 teachers, STIR did an additional round of screening to determine which teachers were the most interested and returned a list of 810 teachers to IDinsight. This list formed the basis of the classroom observation. However, due to attrition and refusals at the school level we were unable to meet our target of teachers and ended up surveying only 342 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes (of all teachers covered for the classroom observation) with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Uttar Pradesh:

Teacher Motivation: In Uttar Pradesh, IDinsight obtained a list of all clusters in Raebareli and Varanasi districts that STIR was working in. From this list, IDinsight selected all clusters with more than 16 schools. This was done to ensure that there would be enough schools in the cluster to assign some to the control group while also maintaining enough treatment schools for STIR to form a network. For the Teacher Motivation survey, IDinsight surveyed all teachers in the school, yielding 1,145 teachers.

Classroom Observation: For the classroom observation, IDinsight sampled roughly 2/3 of the teachers who completed the Teacher Motivation questionnaire, to get a final list of roughly 810 teachers. Teachers were added to this list due to teachers dropping out and the final number was 838 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Midline Respondent Identification and Sampling Strategy:

For midline, which took place at the beginning of the second academic year, we followed up with teachers and students surveyed at baseline. Teachers were added only in the case where the number of teachers still teaching in the school from our baseline lists fell below a certain number. In Delhi, teachers were added if less than two teachers from our list in a given school were available and in Uttar Pradesh, new teachers were added only if all teachers from our baseline lists in a given school dropped out.

The sampling strategy had two clear advantages: 1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 1,259 teachers surveyed at teacher motivation baseline, 453 teachers dropped out of schools during the academic year and hence were not available for surveying during midline. A further 65 teachers refused to participate and 84 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 657. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 811 teachers on the Delhi original list. For those schools where the number of teachers available from our 811 list fell below two, 148 new teachers were added based on a random selection from those teachers employed at that school as of 1 July 2015. A total of 459 teachers were surveyed as part of the classroom observation midline.

Student Testing: For testing of student learning levels, all students surveyed at baseline formed the potential sample at midline. Among the 3,367 students from baseline, 1,956 students were tracked and surveyed at midline. 1,127 students had dropped out from the schools. 40 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits. The remaining 244 students were in schools where we could not survey.

Uttar Pradesh:

Teacher Motivation: From the 1,145 teachers surveyed at baseline, 288 teachers dropped out of schools during the course of the academic year and were hence not available for data collection. An additional 61 refused to participate in the data collection and 41 were not available through the course of the data collection. The final number of teachers surveyed at midline were 755. This was the sample for analysis.

Classroom Observation: From the list of 838 teachers surveyed at baseline, we successfully observed the classrooms of 734 of these teachers at midline. Another 13 teachers were added in schools where all teachers from our 838 had dropped out. 12 of these 13 were in Raebareli and 1 was in Varanasi. In total, 747 teachers were surveyed. 82 teachers dropped out of the schools in our sample. 13 teachers refused to participate in the data collection and 14 teachers were absent throughout the survey period and were not available on either of our visits.

Student Testing: Of the 7,386 students tested at baseline, a total of 4,560 students were also tested at midline. 615 students were absent all days of visits to the schools. 149 students were in the four schools that refused data collection. 2,062 dropped out of the schools in our sample.

Endline Respondent Identification and Sampling Strategy:

For endline, which took place after the end of the second academic year, we followed up with teachers and students surveyed at midline. In Delhi, one teacher was added per school to the classroom observation sample where possible. Additional teachers were added to the teacher motivation sample by offering the survey to all the teachers in our sample schools. The sampling strategy had two clear advantages:

1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 657 teachers surveyed at teacher motivation midline, 101 teachers dropped out of schools during the academic year and hence were not available for surveying during endline. A further 25 teachers refused to participate and 50 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 481. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 459 teachers on the Delhi midline list as well as 102 teachers we surveyed at baseline and couldn't at midline but were hopeful of covering in the last survey. A new teacher was added to each school's sample where possible. A total of 376 teachers were surveyed as part of the classroom observation endline.

Student Testing: For testing of student learning levels, all students surveyed at midline formed the potential sample at endline. Among the 1,956 students from baseline, 1,843 students were tracked and surveyed at midline. 49 students had dropped out from the schools. 45 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits.

Uttar Pradesh:

Teacher Motivation: From the 967 teachers surveyed at midline, 105 teachers were transfered and 17 retired during the course of the academic year and were hence not available for data collection. An additional 36 refused to participate in the data collection and 26 were not available through

Snapshot img

Distance Learning Market Size 2024-2028

The distance learning market size is forecast to increase by USD 149.23 billion at a CAGR of 9.65% between 2023 and 2028.

The growing demand for distance learning, fueled by the continuous development of technology, is a key driver of the distance learning market. As technology improves, online education becomes more accessible, engaging, and effective, allowing students to learn remotely with ease. The integration of advanced tools such as video conferencing, AI-driven assessments, and interactive content is further enhancing the appeal of distance learning.
In North America, the market is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With a growing emphasis on flexible, personalized learning experiences, including self-paced e-learning, institutions are increasingly offering distance learning programs that cater to diverse student needs. This trend is expected to continue, contributing to the market's expansion in the region.

What will be the Size of the Distance Learning Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of remote learning solutions among K-12 students and higher education students. Online assessments, video conferencing sessions, and virtual schools are becoming popular flexible education options for students who require flexibility in their learning schedules. Website-based mediums and application-based mediums, such as e-learning platforms, are increasingly being used to deliver educational programs. Internet access is essential for distance learning, making online learning platforms an indispensable tool for universities and colleges.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Type

  Traditional
  Online


Method

  Synchronous distance learning
  Asynchronous distance learning


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Type Insights

The traditional segment is estimated to witness significant growth during the forecast period. The market encompasses various methods and technologies, including gamification, personalized learning pathways, educational environments, and remote learning techniques. Traditional distance learning, characterized by asynchronous online courses, pre-recorded lecture books, and minimal instructor interaction, remains a significant revenue contributor. This approach caters to a broad audience, particularly those with limited access to digital devices or high-internet connectivity. Academic institutions and the government sector continue to offer traditional distance learning programs, such as those provided by the Open University in the UK via mail. However, corporate blended learning, online education solutions, and personalized learning solutions are gaining popularity due to their interactive and technologically advanced nature.

These methods include learning management systems, virtual classrooms, mobile e-learning platforms, and cloud-based e-Learning platforms. Moreover, the use of intranet connection, computers, tutorials, podcasts, recorded lectures, e-books, and machine learning technology enhances the learning experience. The market also serves academic users and corporate users through service providers and content providers. The increasing literacy rate, internet penetration, and the need for continuous skill upgrading further fuel the market's growth.

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The traditional segment accounted for USD 152.29 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 34% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

The market in North America is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With the rise of gamification, personalized learning pathways, and educational environments, online education solutions have become increasingly popular. Academic institutions and the government sector are expanding their digital services, offering distance learning programs through Learning Management Systems and cloud-based e-Learning platforms. Remote learning methods, such as pre-recorded lectures, tutorials

The number of students starting in Ivy League schools for the Class of 2028 (those beginning in the Fall of 2024), varied from school to school. Cornell University had the largest Class of 2028 among the Ivy League schools, with 3,574 enrolled students.

Abstract

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

School surveys were introduced into Young Lives in 2010 in order to capture detailed information about children's experiences of schooling, and to improve our understanding of: - the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups. - school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems. - equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education

The survey allows us to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. Findings are all available on our Education theme pages and our publications page. Further information is available from the Young Lives http://www.younglives.org.uk/content/school-survey-0" title="School Survey">School Survey webpages.

Geographic coverage

Lao Cai Hung Yen Danang Phu Yen Ben Tre

Analysis unit

Individuals Institutions/organisations

Kind of data

Sample survey data [ssd]

Sampling procedure

Multi-stage stratified random sample The final sample is formed of 3,284 Grade 5 pupils in 176 classes in 92 school sites (both main and satellite sites); 1,138 of these pupils are Young Lives index children.

Mode of data collection

Face-to-face interview; Self-completion; Educational measurements; Observation

Research instrument

The instruments included in the survey are:

Questionnaires - Wave 1

• School roster
• Class and teacher roster
• Child questionnaire (background information)
• Child Maths test
• Child language test (Vietnamese)
• Teacher questionnaire
• Teacher content knowledge test (Maths)
• Teacher content knowledge test (Vietnamese)
• Head teacher questionnaire

Questionnaires - Wave 2

Child class and peers questionnaire Child Maths test Child language test (Vietnamese)

Survey documentation and questionnaires will be provided shortly at http://www.younglives.org.uk/content/vietnam-school-survey

This dataset contains the list of federal and state universities in Nigeria and the functional link to open education resources on their websites or repositories as at December 2023.

Abstract copyright UK Data Service and data collection copyright owner.

These teaching datasets, comprising a sub-set of a large-scale longitudinal study, the Longitudinal Study of Young People in England (LSYPE), were created as part of the NCRM Developing Statistical Modelling in the Social Sciences: Lancaster-Warwick-Stirling Node Phase 2 project, funded by the Economic and Social Research Council (ESRC). During the project, a web site was created with the aim to provide a web-based training resource about the use of statistical regression methods in educational research. The content is designed to teach users how to perform a variety of regression analyses using SPSS, starting with foundation material in basic statistics and working through to more complex multiple linear, logistic and ordinal regression models. Along with illustrated modules the site contains demonstration videos, interactive quizzes and SPSS exercises and examples that use these LSYPE teaching data. Further information and documentation may be found at the web site, Using Statistical Methods in Education Research. Throughout the site modules users are invited to use the datasets for either following the examples or performing exercises. Prospective users of the data will be directed to register an account in order to download the data.

The full LSYPE study is held at the Archive under SN 5545. The teaching datasets include information drawn from Wave 1 of LSYPE, conducted in 2004, with GCSE results matched from Wave 3 (2006). Further information about the NCRM Node project covering this study may be found on the Developing Statistical Modelling in the Social Sciences ESRC award web page.

Documentation
There is currently no discrete documentation currently available with these teaching datasets; users should consult the web site noted above. Documentation covering the main LSYPE study is available with SN 5545.

For the second edition (July 2011), updated versions of the SPSS data files were deposited to resolve minor anomalies.

Main Topics:

The teaching datasets include variables covering LSYPE respondents' educational test results, academic achievement and school life, and demographic/household characteristics including ethnic group, gender, social class and socio-economic status, computer ownership, private education, and mothers' occupational status and educational background.

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Abstract

The GSHS is a school-based survey which uses a self-administered questionnaire to obtain data on young people's health behaviour and protective factors related to the leading causes of morbidity and mortality among children and adults worldwide.

Geographic coverage

National coverage of CBSE schools

Analysis unit

Individuals

Universe

School-going adolescents aged 13-15 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2007 India (CBSE) GSHS was a school-based survey of students in classes 8, 9, and 10. A two-stage cluster sample design was used to produce data representative of all students in classes 8, 9, and 10 in India (CBSE). At the first stage, schools were selected with probability proportional to enrollment size. At the second stage, classes were randomly selected and all students in selected classes were eligible to participate.

Mode of data collection

self-administered

Research instrument

The following core modules were included in the survey: dietary behaviours hygiene mental health physical activity protective factors tobacco use

Cleaning operations

All data processing (scanning, cleaning, editing, and weighting) was conducted at the US Centers for Disease Control.

Response rate

The school response rate was 99%, the student response rate was 85%, and the overall response rate was 83%.