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.

The dataset contains information on a variety of student characteristics, including gender, home language, household occupation, mother's education, household size, school distance, means of transportation, grade, and dropout status. This information can be used to identify factors that are associated with a higher risk of dropout and to develop interventions to prevent students from dropping out.

The dataset is well-suited for analysis using statistical methods. The categorical variables can be used for descriptive analysis, such as frequency counts and cross-tabulations. The numerical variables can be used for more in-depth analysis, such as linear regression and logistic regression. The results of these analyses can provide insights into the factors that influence student dropout and can help to inform policy decisions about how to reduce dropout rates.

Dataset Column Descriptions

This dataset includes various features that were known at the time of student enrollment. Below is a description of each column in the dataset:

• Marital Status: The marital status of the student (e.g., single, married, divorced).

• Application Mode: Refers to the mode or type of application the student submitted to enroll in the course.

• Application Order: Indicates the order in which the student applied for the course. For example, whether it was the student’s first, second, or third choice.

• Course: The course or degree program the student is enrolled in (e.g., Computer Science, Engineering, etc.).

• Daytime/Evening Attendance: Specifies whether the student attends the course during the day or in the evening, representing their attendance schedule.

• Previous Qualification: The type of academic qualification the student had before enrolling in the course (e.g., high school diploma, vocational training).

• Previous Qualification (Grade): The final grade or score associated with the student's previous qualification.

• Nationality: The nationality of the student.

• Mother's Qualification: The highest academic qualification attained by the student's mother.

• Father's Qualification: The highest academic qualification attained by the student's father.

These features represent important demographic, academic, and socio-economic factors, which are crucial for predicting a student's academic outcome.

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...

Abstract (en): The primary purpose of the State Nonfiscal Survey is to provide basic information on public elementary and secondary school students and staff for each of the 50 states, the District of Columbia, and outlying territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands). The database provides the following information on students and staff: general information (name, address, and telephone number of the state education agency), staffing information (number of FTEs on the instructional staff, guidance counselor staff, library staff, support staff, and administrative staff), and student information (membership counts by grade, counts of high school completers, counts of high school completers by racial/ethnic breakouts, and breakouts for dropouts by grade, sex, race). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in the 50 states, the District of Columbia, United States territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands), and Department of Defense schools outside of the United States. 2006-01-18 File DOC2450.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-01-18 File CB2450.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. (1) Part 2, Imputed Data, is a different version of the data in Part 1, Reported Data. The National Center for Education Statistics (NCES) imputed and adjusted some reported values in order to create a data file (Part 2) that more accurately reflects student and staff counts and improves comparability between states. Imputations are defined as cases where the missing value is not reported at all, indicating that subtotals for the category are under-reported. An imputation by NCES assigns a value to the missing item, and the subtotals containing this item increase by the amount of the imputation. Imputations and adjustments were performed on the 50 states and Washington, DC, only. Since all states and Washington, DC, reported data in this survey, these imputations and adjustments were implemented to correct for item nonresponse only. This process consisted of several stages and steps, and varied as to the nature of the missing data. No adjustments or imputations were made to high school graduates or other high school completer categories, nor were any adjustments or imputations performed on the race/ethnicity data. (2) The Instruction Manual that is included with this data collection also applies to COMMON CORE OF DATA: PUBLIC EDUCATION AGENCY UNIVERSE, 1995-1996 (ICPSR 2468) and COMMON CORE OF DATA: PUBLIC SCHOOL UNIVERSE, 1995-1996 (ICPSR 2470). (3) The codebook, data collection instrument, and instruction manual are provided as two Portable Document Format (PDF) files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using the Adobe Acrobat Reader (version 3.0 or later). Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

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.

Regression analysis of HIV status outcome.

Descriptive characteristics of NHBS HET 4 cycle DC by HIV status.

Descriptive characteristics of HET cycle 4 of seeds and associates.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16192307%2Fb1a84007520b6f589dcb5d01b842d1b5%2FThinkstock_475924741-CampusUSA-multiethnic-college-students-in-caps-and-gowns-1068x712.jpg?generation=1712443672494495&alt=media" alt="">

A dataset created at a higher education institution - 'Polytechnic Institute of Portalegre (Portugal)' (derived from several disparate databases) relating to students studying in various undergraduate degrees such as agronomy, design, education, nursing, journalism, management, social services and technology. 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.

For what purpose was the dataset created?

The dataset was created in a project that aims to contribute to the reduction of academic dropout and failure in higher education, by using machine learning techniques to identify students at risk at an early stage of their academic path, so that strategies to support them can be put into place. The dataset includes information known at the time of student enrollment – academic path, demographics, and social-economic factors. The problem is formulated as a three category classification task (dropout, enrolled, and graduate) at the end of the normal duration of the course.

Columns:

Marital status: 1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated.

Application mode: 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: Application order (between 0 - first choice; and 9 last choice).

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).

Daytime/evening attendance: 1 – daytime 0 - evening.

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).

Previous qualification (grade): Grade of previous qualification (between 0 and 200).

Nacionality: 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: 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 (Old) 12 - Other - 11th Year of Schooling 14 - 10th Year of Schooling 18 - General commerce course 19 - Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv. 22 - Technical-professional course 26 - 7th year of schooling 27 - 2nd cycle of the general high school course 29 - 9th Year...

--- ### Context When India got independence from British in 1947 the literacy rate was 12.2% and as per the recent census 2011 it is 74.0%. Although it looks an accomplishment, still many people are there without access to education. It would be interesting to know the current status of the Indian education system. ### Content This dataset contains district and state wise Indian primary and secondary school education data for 2015-16. Granularity: Annual List of files: 1. 2015_16_Districtwise.csv ( 680 observations and 819 variables ) 1. 2015_16_Statewise_Elementary.csv ( 36 observations and 816 variables ) 1. 2015_16_Statewise_Secondary.csv ( 36 observations and 630 variables ) ### Acknowledgements Ministry of Human Resource Development (DISE) has shared the dataset [here](http://udise.in/src.htm) and also published some [reports](http://udise.in/AR.htm). Source of Banner [image](https://unsplash.com/photos/j9jZSqfH5YI). ### Inspiration This dataset provides the complete information about primary and secondary education. There are many inferences can be made from this dataset. There are few things I would like to understand from this dataset. 1. Drop out ratio in primary and secondary education. (Govt. has made law that every child under age 14 should get free compulsary education.) 2. Various factors affecting examination results of the students. 3. What are all the factors that makes the difference (in literacy rate) between Kerala and Bihar? 4. What could be done to improve the female literacy rate and literacy rate in rural area? ---

Description:

A good education offers individuals the opportunity to lead richer, more interesting lives. At a societal level, it creates opportunities for humanity to solve its pressing problems.

The world has gone through a dramatic transition over the last few centuries, from one where very few had any basic education to one where most people do. This is not only reflected in the inputs to education – enrollment and attendance – but also in outcomes, where literacy rates have greatly improved.

Getting children into school is also not enough. What they learn matters. There are large differences in educational outcomes: in low-income countries, most children cannot read by the end of primary school. These inequalities in education exacerbate poverty and existing inequalities in global incomes.

About Dataset: There are 4 dataset in this page: 1- share-of-the-world-population-with-at-least-basic-education:

Access to education is now seen as a fundamental right – in many cases, it’s the government’s duty to provide it.

But formal education is a very recent phenomenon. In the chart, we see the share of the adult population – those older than 15 – that has received some basic education and those who haven’t.

In the early 1800s, fewer than 1 in 5 adults had some basic education. Education was a luxury, in all places, it was only available to a small elite.

But you can see that this share has grown dramatically, such that this ratio is now reversed. Less than 1 in 5 adults has not received any formal education.

This is reflected in literacy data, too: 200 years ago, very few could read and write. Now most adults have basic literacy skills.

2- learning-adjusted-years-of-school-lays:

There are still significant inequalities in the amount of education children get across the world.

This can be measured as the total number of years that children spend in school. However, researchers can also adjust for the quality of education to estimate how many years of quality learning they receive. This is done using an indicator called “learning-adjusted years of schooling”.

On the map, you see vast differences across the world.

In many of the world’s poorest countries, children receive less than three years of learning-adjusted schooling. In most rich countries, this is more than 10 years.

Across most countries in South Asia and Sub-Saharan Africa – where the largest share of children live – the average years of quality schooling are less than 7.

3- number-of-out-of-school-children:

While most children worldwide get the opportunity to go to school, hundreds of millions still don’t.

In the chart, we see the number of children who aren’t in school across primary and secondary education.

This number was around 260 million in 2019.

Many children who attend primary school drop out and do not attend secondary school. That means many more children or adolescents are missing from secondary school than primary education.

4- gender-gap-education-levels:

Globally, until recently, boys were more likely to attend school than girls. The world has focused on closing this gap to ensure every child gets the opportunity to go to school.

Today, these gender gaps have largely disappeared. In the chart, we see the difference in the global enrollment rates for primary, secondary, and tertiary (post-secondary) education. The share of children who complete primary school is also shown.

We see these lines converging over time, and recently they met: rates between boys and girls are the same.

For tertiary education, young women are now more likely than young men to be enrolled.

Have a great analysis !

By Hannah Ritchie, Veronika Samborska, Natasha Ahuja, Esteban Ortiz-Ospina and Max Roser