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

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.

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 Withdraw codes are used as a proxy for dropping out of school based upon the expectation that withdrawn students are no longer receiving educational services.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

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

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](https://creativecommons.org/public...

Dropout of Students in Secondary School by gander across various region.

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, 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.

On-time and extended-time graduation rates by gender, collected very year by the Council of Ministers of Education, Canada (CMEC) for the true cohort high school graduation rate data collection.

This dataset includes publicly available data published primarily by the Pennsylvania Department of Education and the Pennsylvania Office of Safe Schools. The dataset was created by combining several publications by the Pennsylvania Department of Education, including the 2017 School Fast Fact database, 2016-2017 Academic Performance database, and the 2017 Keystone Score database. The dataset includes institutional (school-wide) variables for every public high school in Pennslyvania (n = 407 ). The data includes information surrounding each institution's socio-economic status, racial composition, academic performance, and type of and total use of exclusionary discipline (in-school suspension, out-of-school suspension, and expulsion) for the school year 2016-2017. The dataset also includes neighborhood information for each school location. This data was collected from AreaVibes, a website known for its ability to guide individuals in their search for ideal residential areas in the United States and Canada. AreaVibes deploys a unique algorithm that evaluates multiple different data points for each location, including amenities, cost of living, crime rates, employment, housing, schools, and user ratings. This dataset deployed AreaVibes to input the physical addresses of each high school in order to retrieve the livability score for the surrounding neighborhoods of these educational institutions. Furthermore, the website was instrumental in collecting neighborhood crime scores, offering valuable insights into the levels of criminal activity within specific geographic zones. The crime score takes into account both violent crime and property crime. However, higher weights are given to violent crimes (65%) than property crime (35%) as they are more severe. Data for calculation by Areavibes is derived from FBI Uniform Crime Report.School discipline is crucial for ensuring safety, well-being, and academic success. However, the continued use of exclusionary discipline practices, such as suspension and expulsion, has raised concerns due to their ineffectiveness and harmful effects on students. Despite compelling evidence against these practices, many educational institutions persist in relying on them. This persistence has led to a troubling reality—a racial and socioeconomic discipline gap in schools. This data is used to explore the evident racial and socioeconomic disparities within high school discipline frameworks, shedding light on the complex web of factors that contribute to these disparities and exploring potential solutions. Drawing from social disorganization theory, the data explores the interplay between neighborhood and school characteristics, emphasizing the importance of considering the social context of schools.

Number of persons aged 15 and over in private households with or without a high school diploma or equivalency certificate, and high school completion rate (measured using the variable Secondary (high) school diploma or equivalency certificate) by sex, age group and selected demographic characteristics, Canada, provinces and territories.

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.

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

This dataset tracks annual distribution of students across grade levels in Nacogdoches High School

This dataset tracks annual total students amount from 1987 to 2023 for Nacogdoches High School

This dataset tracks annual distribution of students across grade levels in Camden High School

This dataset tracks annual total students amount from 1990 to 2023 for Camden High School