At a time when OECD and partner countries are trying to figure out how to reduce burgeoning debt and make the most of shrinking public budgets, spending on education is an obvious target for scrutiny. Education officials, teachers, policy makers, parents and students struggle to determine the merits of shorter or longer school days or school years, how much time should be allotted to various subjects, and the usefulness of after-school lessons and independent study. This report focuses on how students use learning time, both in and out of school. What are the ideal conditions to ensure that students use their learning time efficiently? What can schools do to maximise the learning that occurs during the limited amount of time students spend in class? In what kinds of lessons does learning time reap the most benefits? And how can this be determined? The report draws on data from the 2006 cycle of the Programme of International Student Assessment (PISA) to describe differences across and within countries in how much time students spend studying different subjects, how much time they spend in different types of learning activities, how they allocate their learning time and how they perform academically.

The USAID Opportunities for Equitable Access to Quality Basic Education (OPEQ) activity was a school-based intervention implemented by the International Rescue Committee (IRC) in partnership with the DRC’s Ministry of Education and focusing on early grade reading and math skills. The IRC integrated into OPEQ its social-emotional skills building curriculum which has resulted from the IRC’s education research and experience in conflict and crisis-affected areas. The OPEQ activity had four components: (1) informing national level in-service teacher-training policy and systems, (2) community mobilization and engagement, (3) alternative education and vocational training for out-of-school youth, and (4) Learning in a Healing Classroom (LHC), an in-service teacher professional development and integrated curricular program. The impact evaluation of OPEQ’s LHC activity employed a 3-year cluster-randomized trial, measuring the impact of LHC in treatment schools to wait-list control schools. The intervention was assigned to two cohorts defined by geographic location and timing of implementation. The Katanga 4 cohort had schools that received one year and two years of LHC located in Katanga province. The Kivu+ cohort had schools that received one year of the LHC intervention located in South Kivu and some parts of Katanga province. At each school in the sample, field researchers surveyed 81 students randomly selected from the student enrollment register. For the baseline and midline phases, students in the 2nd, 3rd, and 4th grades were tested. For the endline phase, students in 3rd, 4th, and 5th grades were tested.

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all : Access to education has improved, shown through increased attendance levels in early childhood, primary and secondary school in the Pacific region. Goal 4 highlights the need to focus on improving the quality and relevance of education and cognitive learning outcomes, since literacy and numeracy improvements have not made the expected gains for all. There is also a renewed focus on lifelong learning with early childhood care education and post-secondary education and training needing priority attention; The quality of educational facilities in some countries in the region, especially for girls and students with disabilities, is below standard.

Find more Pacific data on PDH.stat.

Quality criteria, analysed data sets, and criteria assessment results of a study in Jul 2019 and Mar 2021. The study was part of a Master Thesis in Information Science (Autor: Gabriel Schneider, Titel: Qualität von Forschungsdaten der Bildungsforschung in offenen Repositorien).

This data set contains an expanded data set and a sub-set (14) of the original 16 quality criteria. Original criteria were translated into English for a presentation at LIDA 21.

Quality criteria were developed based on expertise from the Research Data Centre for Education and the FAIR principles.

In the first study 2019, 29 data sets from Zenodo (search=keyword:Education and type:dataset) were analysed according to the criteria. 20 data sets were excluded due to access restrictions or topic (non educational research).

In 2021, 11 data sets uploaded at Zenodo 2021 were analysed according to the criteria. As search function at Zenodo changed, the search was adapted ((search=keyword:*Education OR keyword:*education) AND type:dataset AND accessright:open). Some data sets were excluded due to topic (non educational research), year published (2020 excluded), language barrieres or insufficient avalaible data.

The files include:

- Criteria: The criteria and assessment points applied

- Dataset: The search terms for the retrieved data sets, and exclusion criteria

- Results: The assessment points given for each criteria to each data set (without further details on decision with regard to specifics of data sets)

Data on long-form data quality indicators for 2021 Census education content, Canada, provinces and territories, census divisions and census subdivisions.

Provides the statistics related to Quality Education Fund projects in recent five school years, including number of applications received and processed, and number of projects approved.

Education is important for social mobility, participation and employment opportunity. High educational attainment level directly impacts quality of life, since it is closely linked to the ability to take advantage of employment opportunities and for social mobility.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. Collections are available for the following years:

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. Due to size constraints only partial data is reflected, to view entire data open up the excel file that shown with data set name. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which includes six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

ABSTRACT This study problematizes the understanding of quality of education/physical education in schools from the interlocution with public university researchers in Latin American countries. In order to achieve this goal, the authors made theoretical incursions into the literature and collected data through an online questionnaire, answered by seven researchers from Higher Education Institutions in Argentina, Chile and Colombia, whose work is connected to issues on the formation of teachers. It was concluded that the discourses of the Latin American interlocutors are approaching an understanding of quality in education/physical education in schools that does not fragment the subject in its relation with the world, but that values it in its diversity, corporality and autonomy, distancing themselves from market approaches that assess quality in the form of tests, competition and supremacy of one another.

University Experience Survey

Data Quality Tools Market Outlook

The global data quality tools market size was valued at $1.8 billion in 2023 and is projected to reach $4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.9% during the forecast period. The growth of this market is driven by the increasing importance of data accuracy and consistency in business operations and decision-making processes.

One of the key growth factors is the exponential increase in data generation across industries, fueled by digital transformation and the proliferation of connected devices. Organizations are increasingly recognizing the value of high-quality data in driving business insights, improving customer experiences, and maintaining regulatory compliance. As a result, the demand for robust data quality tools that can cleanse, profile, and enrich data is on the rise. Additionally, the integration of advanced technologies such as AI and machine learning in data quality tools is enhancing their capabilities, making them more effective in identifying and rectifying data anomalies.

Another significant driver is the stringent regulatory landscape that requires organizations to maintain accurate and reliable data records. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States necessitate high standards of data quality to avoid legal repercussions and financial penalties. This has led organizations to invest heavily in data quality tools to ensure compliance. Furthermore, the competitive business environment is pushing companies to leverage high-quality data for improved decision-making, operational efficiency, and competitive advantage, thus further propelling the market growth.

The increasing adoption of cloud-based solutions is also contributing significantly to the market expansion. Cloud platforms offer scalable, flexible, and cost-effective solutions for data management, making them an attractive option for organizations of all sizes. The ease of integration with various data sources and the ability to handle large volumes of data in real-time are some of the advantages driving the preference for cloud-based data quality tools. Moreover, the COVID-19 pandemic has accelerated the digital transformation journey for many organizations, further boosting the demand for data quality tools as companies seek to harness the power of data for strategic decision-making in a rapidly changing environment.

Data Wrangling is becoming an increasingly vital process in the realm of data quality tools. As organizations continue to generate vast amounts of data, the need to transform and prepare this data for analysis is paramount. Data wrangling involves cleaning, structuring, and enriching raw data into a desired format, making it ready for decision-making processes. This process is essential for ensuring that data is accurate, consistent, and reliable, which are critical components of data quality. With the integration of AI and machine learning, data wrangling tools are becoming more sophisticated, allowing for automated data preparation and reducing the time and effort required by data analysts. As businesses strive to leverage data for competitive advantage, the role of data wrangling in enhancing data quality cannot be overstated.

On a regional level, North America currently holds the largest market share due to the presence of major technology companies and a high adoption rate of advanced data management solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing digitization across industries, coupled with government initiatives to promote digital economies in countries like China and India, is driving the demand for data quality tools in this region. Additionally, Europe remains a significant market, driven by stringent data protection regulations and a strong emphasis on data governance.

Component Analysis

The data quality tools market is segmented into software and services. The software segment includes various tools and applications designed to improve the accuracy, consistency, and reliability of data. These tools encompass data profiling, data cleansing, data enrichment, data matching, and data monitoring, among others. The software segment dominates the market, accounting for a substantial share due to the increasing need for automated data management solutions. The integration of AI and machine learning into these too

Abstract

1.1 Preambule

This study was funded by Google.org. The study began in 2008 and will end in 2011. Field work was done between May and July 2009 for the first round and February and March 2010 for the second round. The purpose of this field report is (1) to document how the data was collected; (2) to act as a reference to those who will be writing scientific papers, processing, and analyzing the data; and (30 consolidate the findings for purposes of sharing with key stakeholders including teachers and Ministry of Education. The report has five sections: Section 1 presents the study background. Section two presents data collection issues. Section three outlines the district and individual school reports. Section four captures the challenges experienced. Section five outlines the lessons learnt and recommendations for future classroom-based studies.

1.2 Purpose of the study

The purpose of this study was to examine the teaching process and generate information relevant to objective policy advice on the quality of teaching and learning. The intention is that by sharing the evidence generated by this study with policy makers, it is hoped that it will lead to the improvement of the quality of teaching in primary schools in Kenya. It sought to understand whether classroom interactions, including how aspects such as 'Opportunity to Learn' explain learning achievement.

1.3 Research questions guiding the study

The following are the main research questions guiding the study. However, the data collected is rich on teaching practice information and will make it possible to answer several other research questions.

a). What are the differences and similarities in teaching practice among teachers in high and low performance schools?

b). Does the observed teaching practice explain student achievement?

c). Do teacher attributes explain student's learning achievement?

d). What policy recommendations on teaching practices can improve the quality of teaching in primary education?

Based on the guiding research questions, the following research papers have been conceptualized and are being finalized for publication as publicly available and accessible APHRC Working Papers.

a) Do teachers who have a good understanding of maths demonstrate better teaching practice in the classrooms?

b) Does teaching practice explain differences in learner achievement in low and high performing schools?

c) Social relations as predictors of achievement in maths in Kenya primary schools.

Other questions that the data may help to answer

a) Do opportunities to learn (measured by teacher absenteeism, curriculum completion, and bullying and class size) explain learning gains.

b) To what extent do student characteristics, classroom sitting arrangements and classroom participation explain learning gains?

c) Assess whether female and male teachers differ in mathematics teaching and content knowledge, and whether this is reflected in pupils' mathematics performance.

Geographic coverage

Six districts in Kenya: Embu, Nairobi, Gucha, Garissa, Muranga and Baringo and 12 schools in each district

Analysis unit

Pupils

Schools

Universe

Grade 6 pupils in the selected schools, the headteacher and Math, English and Science Teachers

Sampling procedure

The target was districts that had consistently perfomed at the bottom, middle and top for 5 consective years. The selection of the best and poor performing districts and schools, the Kenya Certificate of Primary Education (KCPE) results of the last five years available were used to rank districts (nationally) and schools (at district level). School performance in national examinations (a proxy indicator for student achievement) in Kenya varies by geographical and ecological regions of the country. Based on the distribution of school mean scores in a district, schools were categorized as low performing and high performing schools in any given year.

Specifically, six districts in Kenya, two that have consistently been ranked in the bottom 10% of the KCPE examinations over the past 4 years, two that have been consistently ranked within the middle 20% and another two that have consistently been ranked in the top 10% over the same period were selected for the study. A total of 72 schools, 12 in each of the six districts were randomly selected for the study. The schools selected for the study included six that had consistently been ranked in the bottom 20%, and six that had consistently been ranked in the top 20%. A further selection criterion for the schools ensured a mix of rural, peri-urban and urban schools in the sample. While taking a national representation in to account, the sample size was influenced by resource availability.

In the selected schools, grade six pupils were included. In case of multi-streams one grade was randomly selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

Survey instruments:

· Head teacher questionnaire: This instrument solicited information on school management, staffing, enrolment and parental participation in school affairs, among others.

· Teacher questionnaire: This solicited for information on biodata, qualification and training, discipline and syllabus coverage. The questionnaire was administered to grade six Maths, English and Science teachers.

· Learner questionnaire: The questionnaire solicited information on social economic background of the grade six learners and the school environment. This questionnaire was administered to grade six pupils in the selected schools.

Assessment tools:

· Mathematics teacher assessment tool, for grade six math teachers.

· Learner mathematics assessment tool, for pupils in the selected grade six streams.

Classroom observation and checklist tools:

· Classroom observation checklist: The checklist solicited information on availability of relevant textbooks, teacher and student made teaching and learning materials, other teaching resources, enrolment, learner absenteeism and lesson preparation.

· Opportunity to Learn (OTL) form: This form collected information from grade six exercise books that a learner used between January and November 2009. The information collected included date when the lesson was taught, and the main topic and subtopic as defined in grade six subject syllabus. In the absence of a main topic or subtopic, some contents of the lesson were recorded. These were later to be matched with main topic and subtopic from the s

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding

b) During data entry

c) Structure checking and completeness

d) Secondary editing

Response rate

Total of 72 schools, all the head teachers interviwed, 2436 pupils, 213 teachers

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which includes six elements—Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized allowing comparison between and within countries over time.

The Education SDIs include teacher effort, teacher knowledge and ability, and the availability of key inputs (for example, textbooks, basic teaching equipment, and infrastructure such as blackboards and toilets). The indicators provide a snapshot of the learning environment and the key resources necessary for students to learn.

Madagascar Service Delivery Indicators Education Survey was implemented from April 2016 (for enumerator training and pre-testing of the instruments) to May and June 2016 (for fieldwork and data collection) by CAETIC Development, a strong local think-tank and survey firm. The sampling strategy was done by INSTAT the national institute for statistics. Information was collected from 473 primary schools, 2,130 teachers (for skills assessment), 2,475 teachers (for absence rate), and 3,960 pupils across Madagascar. The survey also collected basic information on all the 3,049 teachers or staff that teach in the 473 primary schools visited or are non-teaching directors.

Geographic coverage

National

Analysis unit

Schools, teachers, students.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling method was adopted. First, in each stratum schools were chosen within the selected councils. Once at a selected school, the enumerator selected teachers and pupils depending on the structure of the classrooms.

The schools were chosen using probability proportional to size (PPS), where size was the number of standard two pupils as provided by the 2014 EMIS database. As for the selection of the cluster, the use of PPS implied that each standard four pupil within a stratum had an equal probability for her school to be selected.

Finally, within each school, up to 10 standard four pupils and 10 teachers were selected. Pupils were randomly selected among the grade-four pupil body, whereas for teachers, there were two different procedures for measuring absence rate and assessing knowledge. For absence rate, 10 teachers were randomly selected from the teachers’ roster and the whereabouts of those teachers was ascertained in a return surprise visit. For the knowledge assessment, however, all teachers who were currently teaching in primary four or taught primary three the previous school year were included in the sample. Then a random number of teachers in upper grades were included to top up the sample. These procedures implied that pupils across strata, as well as teachers across strata and within a school (for the knowledge assessment) did not all have the same probability of selection. It was, therefore, warranted to compute weights for reporting the survey results.

The sampling strategy for the SDI in Madagascar was done by INSTAT the national statistics office.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Education Survey Questionnaire consists of six modules:

Module 1: School Information - Administered to the head of the school to collect information about school type, facilities, school governance, pupil numbers, and school hours. Includes direct observations of school infrastructure by enumerators.

Module 2a: Teacher Absence and Information - Administered to headteacher and individual teachers to obtain a list of all school teachers, to measure teacher absence, and to collect information about teacher characteristics.

Module 2b: Teacher Absence and Information - Unannounced visit to the school to assess absence rate.

Module 3: School Finances - Administered to the headteacher to collect information about school finances (this data is unharmonized).

Module 4: Classroom Observation - An observation module to assess teaching activities and classroom conditions.

Module 5: Pupil Assessment - A test of pupils to have a measure of pupil learning outcomes in mathematics and language in grade four.

Module 6: Teacher Assessment - A test of teachers covering mathematics and language subject knowledge and teaching skills.

Cleaning operations

Data quality control was performed in Stata.

This dataset consists of an evaluation form, resulting data, and a slide deck from a case study describing the development, implementation, and evaluation of a 1.5 hour clinical research data management workshop for an academic medical center research community. This workshop was developed by the health sciences library.

This is an official UN page showing the overall information on SDG 4 Targets and Progress.

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...

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the 2021-2022 CCD collection. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp. Notes: -1 or MIndicates that the data are missing. -2 or N Indicates that the data are not applicable. -9 Indicates that the data do not meet NCES data quality standards. All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

New York City Department of Education 2016 - 2017 Elementary , Middle School Quality Reports. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.