Decrease the high school dropout rate from 2.3% in 2013 to 1.5% by 2018.

This dataset provides the number and percentage Massachusetts public high school students who dropped out of high school since 2008. It also includes the percentage of dropouts by grade.

Dropout rate is calculated as the percentage of students in a given grade who dropped out of school between July 1 and June 30 prior to the listed year and who did not return to school by the following October 1. Dropouts are defined as students who leave school prior to graduation for reasons other than transfer to another school. Dropout rates are not reported for any student group where the number of students is less than 6.

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset contains the same data that is also published on our DESE Profiles site: Dropout Report

The percentage of 9th through 12th graders who withdrew from public school out of all high school students in a school year. Withdraw codes are used as a proxy for dropping out of school based upon the expectation that withdrawn students are no longer receiving educational services. A dropout is defined as a student who, for any reason other than death, leaves school before graduation or the completion of a Maryland-approved education program and is not known to enroll in another school or State-approved program during a current school year. Source: Baltimore City Public School System Years Available: 2009-2010, 2010-2011, 2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, 2016-2017, 2018-2019, 2019-2020, 2020-2021

This dataset includes annual rates for students who are identified as dropouts from grades 9-12. The dropout rate is calculated by the Missouri Department of Elementary and Secondary Education as: the number of dropouts divided by the total of September enrollment, plus transfers in, minus transfers out, minus dropouts, added to September enrollment, then divided by two.

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

See the Splitgraph documentation for more information.

From 2006 to 2022, the rate of high school dropouts in the United States significantly decreased. In 2022, the high school drop out rate was **** percent, a notable decrease from *** percent in 2006.

The percentage of 9th through 12th graders who withdrew from public school out of all high school students in a school year. Withdraw codes are used as a proxy for dropping out of school based upon the expectation that withdrawn students are no longer receiving educational services. A dropout is defined as a student who, for any reason other than death, leaves school before graduation or the completion of a Maryland-approved education program and is not known to enroll in another school or State-approved program during a current school year. Source: Baltimore City Public School System Years Available: 2009-2010, 2010-2011, 2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, 2016-2017, 2018-2019, 2019-2020, 2020-2021

In-session dropout prediction model

This project describes an in-session prediction model that predicts student early dropout from online learning exercises.
Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in
the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,
school homework and assignments are supposed to be completed by all learners. In the pandemic, online
learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to
transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting
learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the
online language learning platform orthografietrainer.net. We calculated time-progressive machine learning
models that predict dropout after each step (completed sentence) in the assignment using learning process
data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention
strategy that distinguishes between motivational and subject-specific interventions.
A random state is not set, thus, results might differ marginally.

Whole project described in:
N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart
Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios
Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,
SciTePress, 2022, ISBN 978-989-758-562-3

EDFacts Graduates and Dropouts, 2017-18 (EDFacts GD:2017-18) is one of 17 “topics" identified in the EDFacts documentation (in this database, each “topic" is entered as a separate study). EDFacts GD:2017-18 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about student who graduate or receive a certificate of completion from secondary education or students who dropped out of secondary education at the school, LEA, and state levels. EDFacts GD:2017-18 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts GD:2017-18 are from 6 data groups with information on Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Graduation Rate; Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Student Counts; Graduation Rate; Graduates/Completers; Regulatory Cohort Graduation Rate-Flex; and Regulatory Cohort Graduation Rate Student Counts-Flex. For the purposes of this system, data groups are referred to as variables, as a result of the structure and format of EDFacts' data.

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

In 2012, more than three million students dropped out from high school. At this pace, we will have more than 30 million Americans without a high school degree by 2022 and relatively high dropout rates among Hispanic and African American students. We have developed and analysed a data-driven mathematical model that includes multiple interacting mechanisms and estimates of parameters using data from a specifically designed survey applied to a certain group of students of a high school in Chicago to understand dynamics of dropouts. Our analysis suggests students' academic achievement is directly related to the level of parental involvement more than any other factors in our study. However, if the negative peer influence (leading to lower academic grades) increases beyond a critical value, the effect of parental involvement on the dynamics of dropouts becomes negligible.

EDFacts Graduates and Dropouts, 2015-16 (EDFacts GD:2015-16) is one of 17 “topics" identified in the EDFacts documentation (in this database, each “topic" is entered as a separate study). EDFacts GD:2015-16 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about student who graduate or receive a certificate of completion from secondary education or students who dropped out of secondary education at the school, LEA, and state levels. EDFacts GD:2015-16 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts GD:2015-16 are from 6 data groups with information on Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Graduation Rate; Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Student Counts; Graduation Rate; Graduates/Completers; Regulatory Cohort Graduation Rate-Flex; and Regulatory Cohort Graduation Rate Student Counts-Flex. For the purposes of this system, data groups are referred to as 'variables', as a result of the structure and format of EDFacts' data.

India School Drop Out Rate: 6-11 Years Old data was reported at 19.800 % in 2013. This records a decrease from the previous number of 21.300 % for 2012. India School Drop Out Rate: 6-11 Years Old data is updated yearly, averaging 36.945 % from Sep 1960 (Median) to 2013, with 24 observations. The data reached an all-time high of 67.000 % in 1970 and a record low of 19.800 % in 2013. India School Drop Out Rate: 6-11 Years Old data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA002: School Drop Out Rate: 6-11 Years Old.

EDFacts Graduates and Dropouts, 2012-13 (EDFacts GD:2012-13) is one of 17 “topics" identified in the EDFacts documentation (in this database, each “topic" is entered as a separate study). EDFacts GD:2012-13 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about student who graduate or receive a certificate of completion from secondary education or students who dropped out of secondary education at the school, LEA, and state levels. EDFacts GD:2012-œ13 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts GD:2012-13 are from 6 data groups with information on Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Graduation Rate; Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Student Counts; Graduation Rate; Graduates/Completers; Regulatory Cohort Graduation Rate-Flex; and Regulatory Cohort Graduation Rate Student Counts-Flex. For the purposes of this system, data groups are referred to as 'variables', as a result of the structure and format of EDFacts' data.

The data are from a longitudinal study, investigating predictors for dropout in upper secondary education. They were collected in autumn 2010 on first year students. School status and GPA was retrieved from county school registers. This particular data set contains data used in the paper Internalised mental health problems and general health in first year upper secondary school students do not predict school dropout when controlling for grades: A five-year prospective study. Abstract Background: In Norway, 1 out of 4 is dropping out from upper secondary education. It is well-known that academic performance is a predictor for dropout. Studies have shown that mental and general health also play a role in the dropout process, but this relationship is not fully explored. Method: A comprehensive questionnaire was distributed to a North-Norwegian sample of students recently entered upper secondary education (N=1676, 69% response rate). We tested a range of predictors for dropout five years later, related to mental and general health, demographics and academic performance. Results: A regression analysis showed that grades from lower secondary education predict dropout. Self-rated mental and general health reported at the beginning of the first year of school were not significant predictors when adjusting for grades and track. However, subgroup analyses showed that students in the vocational track reported poorer mental and general health, compared to students in the general track. Conclusion: Grades from lower secondary education are well suited to function as a warning flag for school dropout in upper secondary education. On the other hand, internalised mental problems when tested in the first months of upper secondary school do not predict dropout, and might not be a valid warning flag.

EDFacts Graduates and Dropouts, 2011-12 (EDFacts GD:2011-12), is one of 17 'topics' identified in the EDFacts documentation (in this database, each 'topic' is entered as a separate study); program data is available since 2005 at . EDFacts GD:2011-12 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about student who graduate or receive a certificate of completion from secondary education or students who dropped out of secondary education at the school, LEA, and state levels. EDFacts GD:2011-12 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts GD:2011-12 are from 6 data groups with information on Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Graduation Rate; Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Student Counts; Graduation Rate; Graduates/Completers; Regulatory Cohort Graduation Rate-Flex; and Regulatory Cohort Graduation Rate Student Counts-Flex. For the purposes of this system, data groups are referred to as 'variables', as a result of the structure and format of EDFacts' data.

The average annual dropout rate for secondary schools in India was over ** percent in 2022, a significant decline from the academic year 2019, the highest among school levels. Primary schools had the lowest dropout rates. Early childhood education    India’s infant mortality rates in India have decreased over the years with the help of government initiatives. The government launched “Anganwadis” in 1975, to provide adequate medical care and to combat hunger and malnutrition in children. These government-funded childcare centers enroll children as young as six months old. Across India, there are more than a million Anganwadis that deliver early education, health, and nutrition services. These centers also provide pre-primary education for children below five years. Free education and midday meals    With low levels of reading literacy among eighth graders, faring well in the upcoming at the later stage, in the secondary school classes could be challenging. The government-run public schools provide free and compulsory education as a fundamental right to children between the ages of *** and ********. To improve the nutritional status and attendance of school children, the Indian government implemented the "Midday Meal Scheme" that offers free lunch to all students on working days. While simplistic in its approach, one meal taken care of during the day helps parents in the lower income groups, specifically those that depend on daily/hourly wages.

This dataset contains special education indicators since 2017. It is a long file that contains multiple rows for each district, with rows for different years, comparing students with disabilities, students without disabilities, and all students on a wide range of indicators. Not all indicators are available for all years. For definitions of each indicator, please visit the RADAR Special Education Dashboard.

Resource Allocation and District Action Reports (RADAR) enable district leaders to compare their staffing, class size, special education services, school performance, and per-pupil spending data with similar districts. They are intended to support districts in making effective strategic decisions as they develop district plans and budgets.

This dataset is one of five containing the same data that is also published in the RADAR Special Education Dashboard: Special Education Program Characteristics and Student Demographics Special Education Placement Trajectory Students Moving In and Out of Special Education Services Special Education Indicators Special Education Student Progression from High School through Postsecondary Education

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 Special Education Indicators 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 in school at least once
• Students suspended out-of-school at least once

• Average student growth percentiles (SGP) in ELA (Grades 3-8)
• Average student growth percentiles (SGP) in ELA (Grade 10)
• Average student growth percentiles (SGP) in math (Grades 3-8)
• Average student growth percentiles (SGP) in math (Grade 10)
• Meeting or exceeding expectations on ELA (Grades 3-8)
• Meeting or exceeding expectations on ELA (Grade 10)
• Meeting or exceeding expectations on math (Grades 3-8)
• Meeting or exceeding expectations on math (Grade 10)
• Meeting or exceeding expectations on science (Grades 5 and 8)
• Meeting or exceeding expectations on science (Grade 10)

• 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

• 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

• College students persistently enrolled in postsecondary education for the first two years
• 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

• Special education director FTE
• Special education teachers per 100 SWD
• Special education paraprofessionals per 100 SWD
• Special education support staff per 100 SWD

This data set contains the Federal Three-Year Graduation, Other Completer, Continuing Student and Dropout Counts and Rates by School District and High School for the 2012 school year Cohort. The three-year cohort rate (for 2012 this includes all students who started 10th grade in 2009-2010 plus students who transferred into the Utah public education system during high school) enables a comparison among all Utah local education agencies (LEAs), since approximately one-half of Utah high schools serve only grades 10-12. This section includes all schools and is therefore the most accurate for in state comparisons.

This data set contains the Federal Three-Year Graduation, Other Completer, Continuing Student and Dropout Counts and Rates by School District and High School for the 2012 school year Cohort. The three-year cohort rate (for 2012 this includes all students who started 10th grade in 2009-2010 plus students who transferred into the Utah public education system during high school) enables a comparison among all Utah local education agencies (LEAs), since approximately one-half of Utah high schools serve only grades 10-12. This section includes all schools and is therefore the most accurate for in state comparisons.

EDFacts Graduates and Dropouts, 2014-15 (EDFacts GD:2014-15) is one of 17 “topics" identified in the EDFacts documentation (in this database, each “topic" is entered as a separate study). EDFacts GD:2014-15 (ed.gov/about/inits/ed/edfacts) annually collects cross-sectional data from states about student who graduate or receive a certificate of completion from secondary education or students who dropped out of secondary education at the school, LEA, and state levels. EDFacts GD:2014-15 data were collected using the EDFacts Submission System (ESS), a centralized portal and their submission by states is mandatory and required for benefits. Not submitting the required reports by a state constitutes a failure to comply with law and may have consequences for federal funding to the state. Key statistics produced from EDFacts GD:2014-15 are from 6 data groups with information on Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Graduation Rate; Regulatory Cohort Graduation Rate (Four, Five, and Six Year)-Student Counts; Graduation Rate; Graduates/Completers; Regulatory Cohort Graduation Rate-Flex; and Regulatory Cohort Graduation Rate Student Counts-Flex. For the purposes of this system, data groups are referred to as 'variables', as a result of the structure and format of EDFacts' data.