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

Summary

dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.

Introduction

This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents. The dataset aims to provide valuable insights for educators, policymakers, and researchers to enhance strategies for fostering student retention and academic achievement.

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Dataset

The dataset includes information known at the time of student enrollment – academic path, demographics, and social-economic factors.

- Marital status: Categorical variable indicating the marital status of the individual. (1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated)

- Application mode: Categorical variable indicating the mode of application. (1 - 1st phase - general contingent 2 - Ordinance No. 612/93 5 - 1st phase - special contingent (Azores Island) 7 - Holders of other higher courses 10 - Ordinance No. 854-B/99 15 - International student (bachelor) 16 - 1st phase - special contingent (Madeira Island) 17 - 2nd phase - general contingent 18 - 3rd phase - general contingent 26 - Ordinance No. 533-A/99, item b2) (Different Plan) 27 - Ordinance No. 533-A/99, item b3 (Other Institution) 39 - Over 23 years old 42 - Transfer 43 - Change of course 44 - Technological specialization diploma holders 51 - Change of institution/course 53 - Short cycle diploma holders 57 - Change of institution/course (International)).

- Application order: Numeric variable indicating the order of application. (between 0 - first choice; and 9 last choice).

- Course: Categorical variable indicating the chosen course. (33 - Biofuel Production Technologies 171 - Animation and Multimedia Design 8014 - Social Service (evening attendance) 9003 - Agronomy 9070 - Communication Design 9085 - Veterinary Nursing 9119 - Informatics Engineering 9130 - Equinculture 9147 - Management 9238 - Social Service 9254 - Tourism 9500 - Nursing 9556 - Oral Hygiene 9670 - Advertising and Marketing Management 9773 - Journalism and Communication 9853 - Basic Education 9991 - Management (evening attendance)).

- evening attendance: Binary variable indicating whether the individual attends classes during the daytime or evening. (1 for daytime, 0 for evening).

- Previous qualification: Numeric variable indicating the level of the previous qualification. (1 - Secondary education 2 - Higher education - bachelor's degree 3 - Higher education - degree 4 - Higher education - master's 5 - Higher education - doctorate 6 - Frequency of higher education 9 - 12th year of schooling - not completed 10 - 11th year of schooling - not completed 12 - Other - 11th year of schooling 14 - 10th year of schooling 15 - 10th year of schooling - not completed 19 - Basic education 3rd cycle (9th/10th/11th year) or equiv. 38 - Basic education 2nd cycle (6th/7th/8th year) or equiv. 39 - Technological specialization course 40 - Higher education - degree (1st cycle) 42 - Professional higher technical course 43 - Higher education - master (2nd cycle)).

- Nationality: Categorical variable indicating the nationality of the individual. (1 - Portuguese; 2 - German; 6 - Spanish; 11 - Italian; 13 - Dutch; 14 - English; 17 - Lithuanian; 21 - Angolan; 22 - Cape Verdean; 24 - Guinean; 25 - Mozambican; 26 - Santomean; 32 - Turkish; 41 - Brazilian; 62 - Romanian; 100 - Moldova (Republic of); 101 - Mexican; 103 - Ukrainian; 105 - Russian; 108 - Cuban; 109 - Colombian).

- Mother's qualification: Numeric variable indicating the level of the mother's qualification.
(1 - Secondary Education - 12th Year of Schooling or Eq. 2 - Higher Education - Bachelor's Degree 3 - Higher Education - Degree 4 - Higher Education - Master's 5 - Higher Education - Doctorate 6 - Frequency of Higher Education 9 - 12th Year of Schooling - Not Completed 10 - 11th Year of Schooling - Not Completed 11 - 7th Year (...

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

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

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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...

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.

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.

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

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.

In this paper we describe methods for predicting distributions of outcome gains in the framework of a latent variable selection model. We describe such procedures for Student-t selection models and a finite mixture of Gaussian selection models. Importantly, our algorithms for fitting these models are simple to implement in practice, and also permit learning to take place about the non-identified cross-regime correlation parameter. Using data from High School and Beyond, we apply our methods to determine the impact of dropping out of high school on a math test score taken at the senior year of high school. Our results show that selection bias is an important feature of this data, that our beliefs about this non-identified correlation are updated from the data, and that generalized models of selectivity offer an improvement over the textbook Gaussian model. Further, our results indicate that on average dropping out of high school has a large negative impact on senior-year test scores. However, for those individuals who actually drop out of high school, the act of dropping out of high school does not have a significantly negative impact on test scores. This suggests that policies aimed at keeping students in school may not be as beneficial as first thought, since those individuals who must be induced to stay in school are not the ones who benefit significantly (in terms of test scores) from staying in school.

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 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

description: EDFacts Graduates and Dropouts, 2010-11 (EDFacts GD:2010-11), 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 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.

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.

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.