This data shows the attendance boundaries used to assign students to feeder pattern schools based on their place of residence. These boundaries were adopted for the 2012-13 school year by the Pittsburgh Public Schools. The boundaries were drawn to align with major roads, neighborhood boundaries, and natural features. Efforts were also made to enable all students within an elementary school to move to the same middle school, and allow all students in a middle school to transition to the same high school.

This layer contains details about on-time high school graduation in King County. It has been developed for the Determinant of Equity - Education presentation. It includes information about On-Time High School Graduation equity indicator. Fields describe the total students in King County (Denominator), number of students graduated high school within 4 years (Numerator), the type of equity indicator being measured (Indicator), and the value that describes this measurement (Indicator Value).The data was compiled by Public Health Seattle & King County, Assessment, Policy Development & Evaluation Unit.Office of Superintendent of Public Instruction (OSPI)For more information about King County's equity efforts, please see:Equity, Racial & Social Justice VisionOrdinance 16948 describing the determinates of equityDeterminants of Equity and Data Tool

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations.

This dataset is a long file that contains multiple rows for each school and district, with rows for different years, different student groups, and a wide range of indicators.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

Below is a list of indicators that are included within the dataset. Note: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion" are available for download in this companion dataset. These two indicators are separate from the main DART: Success After High School download since the data are in a different format.

List of Indicators

Context

• Stability rate (enrolled all year)
• Student Enrollment

• 4-year cohort graduation rate
• 5-year cohort graduation rate
• 9th to 10th grade promotion rate (first-time 9th graders only)
• Annual dropout rate
• Chronically absent rate (% of students absent 10% or more each year)
• Student attendance rate
• Students absent 10 or more days each year
• Students suspended out-of-school at least once

• Average student growth percentiles (SGP) in ELA
• Average student growth percentiles (SGP) in mathematics
• Grade 10 students meeting or exceeding expectations in ELA
• Grade 10 students meeting or exceeding expectations in mathematics
• Jr / Sr AP test takers scoring 3 or above
• Jr / Sr enrolled in one or more AP / IB courses
• Jr / Sr who took AP courses and participated in one or more AP tests
• SAT average score - Mathematics
• SAT average score - reading
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - All subjects
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Art
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - English
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Foreign Language
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - History/Social Science
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Mathematics
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Science

• College students persistently enrolled in postsecondary education for the first two years
• Grade 12 students who completed FAFSA
• High school graduates enrolled in 2-year postsecondary education
• High school graduates enrolled in 4-year postsecondary education
• Students enrolled in postsecondary education in the immediate fall after high school graduation
• Student progression from high school through postsecondary degree completion
• Student progression from high school through second year of postsecondary education

• 12th graders passing a full year of mathematics coursework
• 12th graders passing a full year of science and technology/engineering coursework
• 9th graders completing and passing all courses
• High school graduates who completed MassCore

High School graduation rates for the 2016-2017 school year by school district for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in October 2018. Graduation rates were originally obtained on a school level and aggregated to school district by Data Driven Detroit. The graduation rates were calculated by Data Driven Detroit, using the count of students per cohort per school divided by the count of students who graduated.

The DART: Success After High School Dashboard is a tool used to support the self-evaluation process for whole districts as well as individual schools. It contains a set of data elements provide an indication of the overall condition of a district or school's efforts to ensure all students are ready for their next steps as productive and contributing members of society.

This tool contains data elements that cover a range of school, district, post-secondary and career readiness information including demographics, high school indicators, high school performance, programs of study, post-secondary education outcomes, and career development education.

The DARTs provide a gauge of the overall condition of a district or school, but do not have all available information. They should be treated as a good starting point for exploring the data and identifying areas of focus for further inquiry. Please see the Info tab on the dashboard for detailed data analysis considerations.

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations. The waterfall data shows a cohort of high school students and their progression through high school graduation, college enrollment and persistence in higher education to a second year or college completion.

This is a companion dataset to the main DART: Success After High School dataset. It contains two indicators published separately from the main dataset since the data are in a different format: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion". For all other DART: Success After High School indicators, please visit the main DART: Success After High School dataset.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

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

Snapshot img

North America Virtual Schools Market Size 2025-2029

The virtual schools market in North America size is forecast to increase by USD 2.24 billion billion at a CAGR of 14.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the need for cost-effective teaching models and the emergence of E-learning via mobile devices. The increasing popularity of open-source learning content is another key trend fueling market expansion. With budget constraints and the desire for flexible learning options, virtual schools offer an attractive solution for students and educators alike.
This shift towards virtual education is transforming the education landscape, presenting both opportunities and challenges.Staying abreast of these market dynamics is essential for stakeholders looking to capitalize on the potential of this rapidly evolving sector.

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

Request Free Sample

The market is experiencing significant growth, driven by the increasing adoption of online collaboration tools and educational innovation. Virtual school management systems facilitate online school choice for students, enabling personalized instruction and improved student retention. Educational research highlights the effectiveness of digital accessibility and virtual learning technology integration in enhancing learning outcomes. Student engagement strategies, such as educational video and interactive simulations, are essential components of virtual school design. The future of education lies in the development of digital learning ecosystems, which incorporate online reputation management, equity in education, and learning analytics. Virtual schools require robust online learning infrastructure to support student support systems and ensure digital accessibility for all students.
The integration of learning technology and online learning platforms into virtual schools is crucial for delivering effective instruction and promoting student success. Virtual school governance is a critical aspect of the virtual schools market, ensuring the provision of high-quality education and addressing the digital divide. Online learning platforms must prioritize student engagement and provide effective student support systems to mitigate potential challenges and promote positive learning experiences. The use of virtual schools and online learning infrastructure offers significant benefits, including increased flexibility, accessibility, and personalized instruction. However, challenges remain, including the need for effective online reputation management and ensuring equity in education.The market will continue to evolve, with a focus on developing innovative learning technologies and digital content to enhance the virtual learning experience.

How is this market segmented and which is the largest segment?

The market 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.

Type

  For-profit EMO
  Non-profit EMO


Application

  Elementary schools
  Middle schools
  High schools
  Adult education


Delivery Mode

  Online Courses
  Learning Management Systems
  Mobile Learning
  Virtual Classrooms


Subject Area

  STEM
  Business & Management
  Healthcare
  Creative Arts


Deployment Type

  Cloud-Based
  On-Premises


Geography

  North America

    US
    Canada
    Mexico

By Type Insights

The for-profit emo segment is estimated to witness significant growth during the forecast period.

For-profit Education Management Organizations (EMOs) are private entities that offer administrative and operational support, curriculum development, and teacher training to schools or districts while aiming for profit generation. These organizations have extensive experience and expertise in delivering virtual education programs. They invest in advanced technology infrastructure, learning management systems, and instructional resources to create engaging virtual learning experiences for students. For-profit EMOs prioritize personalized learning, student engagement, and parent involvement through digital textbooks, online curriculum, and interactive digital learning platforms. They also emphasize student success by providing online tutoring, adaptive learning, and data analytics. Virtual classrooms and mobile learning enable students to access education from anywhere, while virtual field trips offer immersive educational experiences.

For-profit EMOs build educational partnerships to expand their offerings, including virtual labs, online libraries, and virtual school networks. They also focus on online marketing, branding, and student recruitment to attract a diverse student population. Higher education institutions collaborate with for-pro

Metric scores are not reported for n-sizes under 10. Per OSSE's policy, secondary suppression is applied to all student groups when a complementary group has an n-size under 10 or is top/bottom suppressed to prevent the calculation of suppressed data. For more on statewide assessment results, see how this is measured.

Data Source: Office of the State Superintendent of Education

Why This Matters

Math proficiency is crucial for the future success of children. It is highly predictive of high school graduation and college enrollment rates. With an increasing number of jobs requiring digital, programming, and technological skills, math proficiency will continue to be important in years to come.

3rd-grade test scores have been shown to be as accurate as 8th-grade test scores in predicting long-term academic performance. Identifying disparities and intervening at younger ages gives more time to offer additional support and resources.

Nationally, fewer Black and Latino students receive proficient scores on statewide tests compared to white and Asian students. Racial disparities in socioeconomic status and resources, neighborhood and school racial segregation, and racial biases in educational spaces contribute to racial gaps in test scores.

The District Response

The Office of the State Superintendent of Education (OSSE) has awarded additional funding to Local Education Agencies (LEAs) for High-Impact Tutoring (HIT) in math and math High Quality Instructional Materials (HQIM).

DC Public Schools’ Five-year Strategic Plan identifies key actions for supporting math education including cultivating math educators expertise and shifting math-related mindsets to build math joy across school communities.

OSSE offers an array of instructional materials, distance learning tools, and educational resources to promote math learning.