We predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study courses. We model the dropout decision as a binary classification (graduate or dropout) and focus on very early prediction of student dropout by stepwise modeling students’ transition from school (pre-study) over the study-decision phase (decision phase) to the first semesters at university (early study phase). We evaluate how predictive performance changes over the three models, and observe a substantially increased performance when including variables from the first study experiences, resulting in an AUC (area under the curve) of 0.86. Important predictors are the final grade at secondary school, and also determinants associated with student satisfaction and their subjective academic self-concept and self-assessment. A direct outcome of this research is the provision of information to universities wishing to implement early warning systems and more personalized counseling services to support students at risk of dropping out during an early stage of study.

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

Dataset Overview:

This dataset provides valuable insights into predicting student dropout and academic success in higher education institutions. Collected from various disjoint databases, it includes data on students enrolled in a range of undergraduate programs, such as Agronomy, Design, Education, Nursing, Journalism, Management, Social Service, and Technologies.

The dataset contains a variety of features collected at the time of student enrollment, including academic path, demographics, and socio-economic factors. Additionally, it includes students' academic performance at the end of the first and second semesters. The goal is to build classification models that predict whether students are likely to drop out or succeed academically.

Key Features:

Academic path and degree program (Agronomy, Design, Nursing, etc.) Demographics (age, gender, etc.) Socio-economic factors (income level, family background, etc.) Academic performance at the end of first and second semesters Classification Task:

The dataset is structured for a three-category classification problem, where the target variable indicates the student's outcome (dropout, success, or other). Note that the data exhibits a strong class imbalance, which is a key challenge when developing predictive models.

A 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.

Content

The presented Data-set is collected from Official Gov website and provides information about Indian student's Drop-Out-Rate (DOR) in accordance with Indian States and years from 2012-13 to 2014-15.

This the data-set features Indian students** Male and female **separately from different level of education such as :

1. Primary_Boys
2. Primary_Girls
3. Primary_Total
4. Upper Primary_Boys
5. Upper Primary_Girls
6. Upper Primary_Total
7. Secondary _Boys
8. Secondary _Girls
9. Secondary _Total
10. HrSecondary_Boys
11. HrSecondary_Girls
12. HrSecondary_Total

Acknowledgements

Data-set is collected from Official Indian Gov website

Ignorance is Bliss’

Of course, you might have. It’s a very popular phrase that is derived from the likes of cowardice and selfishness. It is often used by people who like to run away from reality and are often deluded with what actually makes them happy. Ignorance is not at all bliss. All man-made catastrophes such as illiteracy, inequality, harassment, suppression and everything that dwells in the dark side arise from ignorance.

Recently, I came across an article by The Hindu titled – What is the dropout rate among school children in India? And I was shocked to see the statistics that the majority of regions such as Jharkhand, Arunachal Pradesh, Nagaland, Bihar, Mizoram, etc. have high dropout rates. On an average only 70 students out of 100 finish school in India. While in the above the mentioned regions the condition is even worse, only 30 – 50 students complete schooling. More than half of the students enrolled at the elementary level leave school till they reach 12th.

Therefor through this shared data-set i would like to motivate every person to know and to understand the situation.

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.

Dropout Prevention Services and Programs (FRSS 99), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at https://nces.ed.gov/surveys/frss/downloads.asp. FRSS 99 (https://nces.ed.gov/surveys/frss/index.asp) is a sample survey that provides national estimates on how public school districts identify students at risk of dropping out, programs used specifically to address the needs of students at risk of dropping out of school, the use of mentors for at-risk students, and efforts to encourage dropouts to return to school. The study was conducted using mail, surveys via the web, and telephone follow-up for survey nonresponse and data clarification. Superintendents of public school districts were sampled. The study's weighted response rate was 89 percent. Key statistics produced from FRSS 99 were information on various services or programs offered by districts specifically to address the needs of students at risk of dropping out of school, and types of transition support services used to help all students transition from a school at one instructional level to a school at a higher instructional level. Data on the various factors used to identify students who were at risk of dropping out were also collected.

Full Description The variable examined is graduation status after four years of high school. Early and summer graduates are considered graduates after four years. The "other" rate includes students who dropped out of high school, enrolled in a GED program, transferred to post-secondary education, or have unknown status. Special education students in school after four years but subsequently graduated are not included in the "still enrolled" rate due to Individuals with Disabilities Education Act (IDEA) restrictions. The subgroups reported are gender, race/ethnicity, English language learners, special education students, and students eligible for free or reduced-price meals (FRPM). The data replace the rate of students enrolled in 12th grade in September who graduated the following June. Connecticut State Department of Education (SDE) collects data longitudinally by four-year cohorts. SDE reports and CTdata.org carries graduation rates of four-year cohorts annually.

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center

From 2006 to 2022, the rate of high school dropouts in the United States has significantly decreased. In 2022, the high school drop out rate was five percent, a notable decrease from 9.7 percent in 2006.

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, 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.

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.

🎓 About the Dataset This dataset simulates academic and behavioral data of 5,000 students preparing for the JEE (Joint Entrance Examination)—one of the toughest entrance exams in India. It includes metrics like JEE scores, mock test performance, study hours, mental health, family background, and more.

Use Case The goal is to predict whether a student is likely to drop out after class 12. Beginner friendly dataset to play with it. Recommend you to go with the visualization.

How Was It Created? The dataset is synthetically generated using Python to reflect realistic distributions based on anecdotal knowledge, common trends, and field intuition. Some data points include outliers to mimic real-life unpredictability.

Suggested ML Tasks

Binary classification (dropout prediction)

Feature importance analysis

Educational policy modeling

Handling imbalanced datasets

Want Real Data? You can adapt this schema and collect real data anonymously using Google Forms in your circle.

This dataset provides a comprehensive analysis of factors influencing student dropout rates in secondary education. It includes demographic information, academic performance, and social conditions that may contribute to a student's likelihood of dropping out.

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.

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, 2016-17 (EDFacts GD:2016-17) is one of 17 “topics" identified in the EDFacts documentation (in this database, each “topic" is entered as a separate study). EDFacts GD:2016-17 (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:2016-17 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:2016-17 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.

The Virginia Department of Education (VDOE) calculates two cohort graduation statistics annually. The Virginia On-Time Graduation Rate defines graduates as students who earn Advanced Studies, Standard, IB, or Applied Studies Diplomas for students who entered the ninth-grade for the first time together and were scheduled to graduate four years later. The formula also recognizes that some students with disabilities and limited English proficient (EL) students are allowed more than the standard four years to earn a diploma and counts those students as 'on-time' graduates.

The Federal Graduation Indicator limits graduates to students who earn Advanced Studies, Standard, or IB Diplomas. And, it does not include any allowances for students that are allowed more than four years to graduate. The FGI is solely used for required federal accountability reporting.