This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to provide a biennial count of the total number of private schools, teachers, and students. The PSS school _location and associated geographic area assignments are derived from reported information about the physical _location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the 2021-2022 PSS school collection, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The Ministry of Educations' - Basic Education Statistical Booklet captures national statistics for the Education Sector in totality. This dataset captures enrollment details, gender and class of boys and girls in both private and public schools across the counties Source: Table 36 Boys Public Primary Enrolment by Class Table 37 Girls Public Primary Enrolment by Class Table 39 Boys Private Primary Enrolment by Class Table 40 Girls Private Primary Enrolment by Class

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to generate biennial data on the total number of private schools, teachers, and students, and to build an accurate and complete list of private schools to serve as a sampling frame for NCES surveys. The PSS school location and associated geographic area assignments are derived from reported information about the physical location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the 2017-2018 PSS school sample, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to generate biennial data on the total number of private schools, teachers, and students, and to build an accurate and complete list of private schools to serve as a sampling frame for NCES surveys. The PSS school _location and associated geographic area assignments are derived from reported information about the physical _location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the 2015-2016 PSS school sample, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Student Performance & Behavior Dataset

This dataset is real data of 5,000 records collected from a private learning provider. The dataset includes key attributes necessary for exploring patterns, correlations, and insights related to academic performance.

Columns: 01. Student_ID: Unique identifier for each student. 02. First_Name: Student’s first name. 03. Last_Name: Student’s last name. 04. Email: Contact email (can be anonymized). 05. Gender: Male, Female, Other. 06. Age: The age of the student. 07. Department: Student's department (e.g., CS, Engineering, Business). 08. Attendance (%): Attendance percentage (0-100%). 09. Midterm_Score: Midterm exam score (out of 100). 10. Final_Score: Final exam score (out of 100). 11. Assignments_Avg: Average score of all assignments (out of 100). 12. Quizzes_Avg: Average quiz scores (out of 100). 13. Participation_Score: Score based on class participation (0-10). 14. Projects_Score: Project evaluation score (out of 100). 15. Total_Score: Weighted sum of all grades. 16. Grade: Letter grade (A, B, C, D, F). 17. Study_Hours_per_Week: Average study hours per week. 18. Extracurricular_Activities: Whether the student participates in extracurriculars (Yes/No). 19. Internet_Access_at_Home: Does the student have access to the internet at home? (Yes/No). 20. Parent_Education_Level: Highest education level of parents (None, High School, Bachelor's, Master's, PhD). 21. Family_Income_Level: Low, Medium, High. 22. Stress_Level (1-10): Self-reported stress level (1: Low, 10: High). 23. Sleep_Hours_per_Night: Average hours of sleep per night.

The Attendance is not part of the Total_Score or has very minimal weight.

Calculating the weighted sum: Total Score=a⋅Midterm+b⋅Final+c⋅Assignments+d⋅Quizzes+e⋅Participation+f⋅Projects

Dataset contains: - Missing values (nulls): in some records (e.g., Attendance, Assignments, or Parent Education Level). - Bias in some Datae (ex: grading e.g., students with high attendance get slightly better grades). - Imbalanced distributions (e.g., some departments having more students).

Note: - The dataset is real, but I included some bias to create a greater challenge for my students. - Some Columns have been masked as the Data owner requested. "Students_Grading_Dataset_Biased.csv" contains the biased Dataset "Students Performance Dataset" Contains the masked dataset

For what purpose was the dataset created?

The purpose is to predict students' end-of-term performances using ML techniques.

Additional Information

1-10 of the data are the personal questions, 11-16. questions include family questions, and the remaining questions include education habits.

Class Labels

Student ID

1- Student Age (1: 18-21, 2: 22-25, 3: above 26)

2- Sex (1: female, 2: male)

3- Graduated high-school type: (1: private, 2: state, 3: other)

4- Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full)

5- Additional work: (1: Yes, 2: No)

6- Regular artistic or sports activity: (1: Yes, 2: No)

7- Do you have a partner: (1: Yes, 2: No)

8- Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410)

9- Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other)

10- Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other)

11- Mothers’ education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)

12- Fathers’ education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.)

13- Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above)

14- Parental status: (1: married, 2: divorced, 3: died - one of them or both)

15- Mothers’ occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other)

16- Fathers’ occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other)

17- Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours)

18- Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often)

19- Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often)

20- Attendance to the seminars/conferences related to the department: (1: Yes, 2: No)

21- Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral)

22- Attendance to classes (1: always, 2: sometimes, 3: never)

23- Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable)

24- Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never)

25- Taking notes in classes: (1: never, 2: sometimes, 3: always)

26- Listening in classes: (1: never, 2: sometimes, 3: always)

27- Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always)

28- Flip-classroom: (1: not useful, 2: useful, 3: not applicable)

29- Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)

30- Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)

31- Course ID

32- OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)

Citation Requests/Acknowledgements

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham

Abstract

The Master datasets comprise of four datasets: on children, schools, teachers and households. These master datasets contain key variables and identifiers which will allow users of the data to determine the progression of sample sizes and attrition of children, households, schools and teachers across the four years of the LEAPS panel data.

The children dataset contains round-by-round status of children's grades, enrollment, promotion etc. It also has variables indicating the panel child belongs to (the first panel being grade 3 children LEAPS started following in 2003, and the second one being 3rd graders followed starting in 2005 i.e. round 3 of the survey) as well as whether child was randomly selected for child questionnaire in class. The school dataset contains information such as school type, survey status, construction date. Note that there is only one schoolid variable and it is constant across all rounds. To capture the fact that there is merging of some schools going on across the rounds, refer to the school_merged_into and school_merged_with variables. The school_merged_into variable only exists for the small schools that merged into a larger school whereas the school_merged_with variable exists for the larger schools that the smaller schools merged in to. The teachers dataset contains information such as their round-by-round school, teaching status. The household dataset contains a Mauza indicator, and a variable on whether the household was surveyed in a particular round.

Geographic coverage

Rural Punjab, Pakistan

Sampling procedure

The sample comprises 112 villages in 3 districts of Punjab-Attock, Faisalabad and Rahim Yar Khan. The districts represent an accepted stratification of the province into North (Attock), Central (Faisalabad) and South (Rahim Yar Khan). The 112 villages in these districts were chosen randomly from the list of all villages with an existing private school. This allows us to look at differences between private and public schools in the same village. Although these villages are thus bigger and richer than average villages in these districts, we believe this is a forward-looking strategy and the insights earned here will soon be applicable to a significant fraction of all villages in the country.

Sampling deviation

None

Response rate

The attrition has been remarkably small, averaging 3-4 percent in each year.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

This dataset is designed to represent and identify the general locations of public school facilities within Lexington-Fayette County. The dataset is programmatically created and updated by converting the polygon centroids of the LFUCG School boundary polygon layer to a point layer. The location of the public school facilities is updated through public record and coordination with the Fayette County Public School. The location for the certified private schools is updated through public record for certified private schools from the Kentucky Department of Education. The public school facilities are continuously updated. This dataset participates in a topology with the parcel dataset to assure coincident geometry during parcel editing.The data is in ESRI feature class format, but can be exported to any number of supported formats, including shapefile and dxf. The native projection for the data is Kentucky State Plane North (NAD83), but may have been reprojected for use in other applications. Please check metadata to determine current projection.

Ethiopian Students Dataset Description

Overview

This dataset offers a comprehensive perspective on the academic performance and demographic attributes of Ethiopian students from grades 1 through 12. It includes key indicators such as test scores, attendance, homework completion, and class participation across core subjects like English, Math, Amharic, Affan Oromoo, and more. Accompanying this academic data are demographic details—gender, region, parental education, and school characteristics—as well as National Exam results for higher grades. The dataset serves as a rich resource for educational research and policy analysis.

Source and Purpose

This dataset is a simulated representation modeled after real student records from Ethiopian schools. It is designed to reflect actual educational contexts, supporting investigations into performance trends, socio-economic influences (e.g., parental involvement, internet access), and regional disparities. Inspired by Ethiopia’s education system, the dataset aims to inform policymakers, educators, and researchers seeking data-driven insights.

Key Features

• Demographic Information: Student ID, gender, date of birth, region, health conditions, parental education level, and parental involvement.
• Academic Records: Subject-wise test scores, attendance, homework completion, class participation, and access to textbooks across all grade levels.
• School Characteristics: School ID, type (public/private), location (urban/rural), teacher-student ratio, and available academic resources.
• Upper-Grade Focus (Grades 9–12): Includes performance data in specialized subjects such as Biology, Physics, and Chemistry, along with National Exam scores.
• Career Preferences: Students’ chosen educational tracks (Natural Sciences or Social Sciences) and long-term career aspirations.

Potential Use Cases

• Educational Research: Explore trends in student performance by subject, grade level, or geographic region.
• Policy Development: Assess the influence of school resources, parental engagement, and access to technology on academic outcomes.
• Predictive Modeling: Train machine learning models to forecast student achievement or dropout risk.
• Regional Analysis: Examine educational inequalities among regions such as Addis Ababa, Oromia, and Amhara.

Notes

• Missing values are represented as "N/A" where certain subjects or grade-level metrics are not applicable to all students.
• The dataset is provided in a single CSV file (ethiopian_students_dataset.csv), comprising 634 columns and numerous student entries.
• Analysts should apply appropriate handling for missing data and categorical variables (e.g., gender, school type) during preprocessing.

The dataset displays information regarding the number of students by year of course, class and gender in equal schools in the Municipality of Milan. The data specifically found in the dataset are: * SchoolYear: Numeric Reference school year in the school registry; * CodiceScuola: Code text of the school (plexus); * DenominazioneScuola: Name (name) of the school (plexus) AddressSchool: Delivery address of the school * AnnoCorsoClasse: Indicates the year of the course with reference to the school order and class. For primary school from 1 to 5 or 7 in the case of multiple classes, i.e. classes where several years of the course are grouped together. For lower secondary school from 1 to 3. For upper secondary school from 1 to 6 * Classes: Number of classes * Male Pupils: Number of male pupils * Female Pupils: Number of female pupils * ZIP code: Postal code * TOWN HALL : Municipality * ID_NIL: Local identity unit identifier * NIL: Local identity unit * LONG_X_4326: Longitude * LAT_Y_4326: Latitude * Location: Latitude and Longitude

The dataset displays information regarding the number of students by year of course, class and gender in private schools in the Municipality of Milan. The reference period is the 2020-2021 school year. The data specifically found in the dataset are: * School Year: Numeric Reference school year in the school registry; * CodiceScuola: Code text of the school (plexus); * DenominazioneScuola: Name (name) of the school (plexus) AddressSchool: Delivery address of the school * AnnoCorsoClasse: Indicates the year of the course with reference to the school order and class. For primary school from 1 to 5 or 7 in the case of multiple classes, i.e. classes where several years of the course are grouped together. For lower secondary school from 1 to 3. For upper secondary school from 1 to 6 * Classes: Number of classes * Male Pupils: Number of male pupils * Female Pupils: Number of female pupils * ZIP code: Postal code * TOWN HALL : Municipality * ID_NIL: Local identity unit identifier * NIL: Local identity unit * LONG_X_4326: Longitude * LAT_Y_4326: Latitude * Location: Latitude and Longitude

Schools In India

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• India has nearly more than 20 lac schools today. These schools are Govt. Private and partially aided by the state govt. or Central Govt. 29 States and 7 Union Territories have varying no. of schools from 45 to two and a half lacs. Uttar Pradesh is having the most no schools more than 2.5 lac in the state. Lakshadweep is having merely 45 schools which seems sufficient for this small Union Territory.

Datasets:

1. Schools_In_India.csv:

 - This is the partial record of the data
 - Column Description:

| Sr No | Column Name | Description | --- | --- | | 1 | School | Partial url link of School| | 2 | Location | Partial url link of Address| | 3 | SchoolName | Name of School| | 4 | State | State name of school| | 5 | District | District name of school| | 6 | Block | Block name of school| | 7 | Cluster | Cluster name of school|

2. Schools_in_India_Full_informations.csv

Abstract

The data was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The purpose is to predict students' end-of-term performances using ML techniques.

Attribute Information:

Student ID 1- Student Age (1: 18-21, 2: 22-25, 3: above 26) 2- Sex (1: female, 2: male) 3- Graduated high-school type: (1: private, 2: state, 3: other) 4- Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full) 5- Additional work: (1: Yes, 2: No) 6- Regular artistic or sports activity: (1: Yes, 2: No) 7- Do you have a partner: (1: Yes, 2: No) 8- Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410) 9- Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other) 10- Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other) 11- Mother's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.) 12- Father's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.) 13- Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above) 14- Parental status: (1: married, 2: divorced, 3: died - one of them or both) ***Listed as "Kids"...woops 15- Mother's occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other) 16- Father's occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other) 17- Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours) 18- Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often) 19- Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often) 20- Attendance to the seminars/conferences related to the department: (1: Yes, 2: No) 21- Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral) 22- Attendance to classes (1: always, 2: sometimes, 3: never) 23- Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable) 24- Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never) 25- Taking notes in classes: (1: never, 2: sometimes, 3: always) 26- Listening in classes: (1: never, 2: sometimes, 3: always) 27- Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always) 28- Flip-classroom: (1: not useful, 2: useful, 3: not applicable) 29- Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49) 30- Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49) 31- Course ID 32- OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)

Acknowledgements

Relevant Papers:

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.

Citation Request:

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.

Inspiration

Which students are most likely to succeed?

Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

Number of pupils by level and number of classes per school – Date of observation at the beginning of the month of October each year Source: Depp, Ministry of National Education, Youth and Sports, DEPP Field: public and private institutions under contract, metropolis and DOM

Abbreviations used:

• REP:Priority Education Network

• REP+: Priority Education Network Plus

• ULIS: Localised Units for School Inclusion Number of pupils by level and number of classes per school – Date of observation at the beginning of the month of October each year Source: Depp, Ministry of National Education, Youth and Sports, DEPP

Field:public and private institutions under contract, metropolis and DOM

Abbreviations used:

• REP: Priority Education Network * REP+: Priority Education Network Plus
• ULIS: Localised Units for School Inclusion

This dataset provides information about Massachusetts public high school graduates enrolling into institutions of higher education by student group since 2004. It includes the count and percentage of students enrolled in any college or university, as well as a breakdown of enrollment in private vs. public, two-year vs. four-year, and Massachusetts vs. out-of-state institutions. It also includes the percentage of students enrolled in a Massachusetts community college, a Massachusetts state university, or the University of Massachusetts system.

The data provided in the report are based on point-in-time matching of graduates with higher education enrollment data from the National Student Clearinghouse (NSC). For more information about working with NSC data, including coverage and FERPA implications, please visit their Working with Our Data page.

Results are not reported for higher education enrollments of fewer than 15. Prior to the 2015 high school graduating class, the data refers to students attending college within 16 months of graduating high school. From 2015 on, the data is also provided by high school graduates attending college by the March following their high school graduation year. The percentages in the report are available by college attendee or high school graduate.

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: Graduates Attending Higher Ed

Abstract

The Ghana Quality for Preschool Impact Evaluation 2015, Baseline survey (QPIE-BL 2015) was approved by the Strategic Impact Evaluation Fund (SIEF) of the World Bank on August 2015 in the Great Accra Region of Ghana. The official project name is called "Testing and scaling-up supply- and demand-side interventions to improve kindergarten educational quality in Ghana”, known as “Quality Preschool for Ghana (QP4G)”.

The project seeks to increase the quality of preschool education during the two years of universal Kindergarten (KG) in Ghana through intervening in the supply-side (i.e., teacher in-service training) and the demand side (i.e., increasing parental awareness for developmentally appropriate quality early education).

The primary goal of the impact evaluation is to test the efficacy of a potentially scalable (8-day) in-service teacher training to improve the quality of KG teacher practices and interactions with children and to improve children’s development, school readiness and learning in both private and public preschools in the Greater Accra Region of Ghana. Additional goals of this evaluation are: to test the added value of combining a scalable (low-cost) parental awareness intervention with teacher in-service training; to compare implementation challenges in public and private schools; and to examine several important sources of potential heterogeneity of impact, primarily impacts in public vs. private schools.

The current submission is for the Baseline Survey, conducted with 5 types of respondents in two phases - Baseline I and Baseline II. Baseline I consisted of interviews with school head teachers and school proprietors (for private schools) and was conducted in June 2015. Baseline II consisted of collecting the following data: (a) direct assessments of children’s school readiness at school entry, (b) surveys of teacher well-being and demographics, (c) video recordings for classroom observations of teachers (not being submitted), and (d) caregiver surveys. This data collection was conducted from Sep-Nov 2015.

Geographic coverage

Urban and Peri-Urban Districts, Greater Accra Region

Analysis unit

Units of analysis include individuals (head teachers, teachers, children, caregivers) and schools.

Universe

The survey universe is 6 poor districts in the Greater Accra Region. We sampled 240 schools, 108 public (Govt.) schools and 132 private schools. The population of interest is KG teachers and students in Kindergarten (KG) 1 and KG 2 classrooms in these schools, as well as the caregivers of sampled students. It also includes school head teachers and owners/proprietors.

Kind of data

Sample survey data [ssd]

Sampling procedure

This impact evaluation applies a cluster-randomized design. Eligible schools were randomly selected to participate in the study. The eligible population was schools with KG 1 and KG 2 classrooms (the two years of universal preprimary education) in six districts in the Greater Accra Region. In these six districts we have sampled 240 schools; 108 public schools and 132 private schools in total.

The unit of randomization for this randomized control trial (RCT) is schools, whereby eligible schools (stratified by public and private sector schools) are randomly assigned to: (1) in-service teacher-training program only; (2) in-service teacher-training program plus parental awareness program; or (3) control (current standard operating) condition.

The sampling frame for this study was based on data in the Education Management Information System (EMIS) from the Ghana Education Service. This data was verified in a 'school listing exercise' conducted in May 2015.

Sample selection was done in multiple stages as shown in Figure 1. The first stage involved purposive selection of six districts within the region based on two criteria: (a) most disadvantaged (using UNICEF's District League Table scores, out of sixteen total districts); and (b) close proximity to Accra Metropolitan for travel for the training of the KG teachers. The six selected municipals were La Nkwantanang-Madina Municipal, Ga Central Municipal, Ledzokuku-Krowor Municipal, Adentan Municipal, Ga South Municipal and Ga East Municipal.

The second stage involved the selection of public and private schools from each of the selected districts in the Accra region. We found 678 public and private schools (schools with kindergarten) in the EMIS database. Of these 361 schools were sampled randomly (stratified by district and school type) for the school listing exercise, done in May 2015. This was made up of 118 public schools and 243 private schools.

The sampling method used for the school listing exercise was based on two approaches depending on the type of school. For the public schools, the full universe of public schools (i.e., 118) were included in the school listing exercise. However, private schools were randomly sampled using probability proportional to the size of the private schools in each district. Specifically, the private schools were sampled in each district proportionate to the total number of district private schools relative to the total number of private schools. In so doing, one school from the Ga South Municipal was removed and added to Ga Central so that all districts have a number of private schools divisible by three. This approach yielded 122 private schools. Additionally, 20 private schools were randomly selected from each of the districts (i.e., based on the remaining list of private schools in each district following from the first selection) to serve as replacement lists. The replacement list was necessary given the potential refusals from the private schools. There were no replacement lists for the public schools since all public schools would automatically qualify for participation.

The third stage involved selecting the final sample for the evaluation using the sampling frame obtained through the listing exercise. A total of 240 schools were randomly selected, distributed by district and sector. Schools were randomized into treatment groups after the first round of baseline data collection was completed.

The survey respondents were sampled using different sampling techniques: a. KG teachers: The research team sampled two KG teachers from each school; one from KG1 and KG2. KG teachers were sampled using purposive sampling method. In schools where there were more than two KG classes, the KG teachers from the "A" stream were selected. For the treatment schools, all KG teachers were invited to participate in the teacher training program. b. KG child-caregiver pair: The research team sampled KG children and their respective caregivers using simple random sampling method. Fifteen KG children-caregivers pair were sampled from each school. For schools with less than 15 KG children (8 from KG1, 7 from KG2 where possible), all KG children were included in the survey. KG children were selected from the same class as the selected KG teacher. The survey team used the class register to randomly select KG children who were present on the day of the school visit. Sampling was not stratified by gender or age. The caregivers of these selected child respondents were invited to participate in the survey.

The research team sought informed consent from the school head teacher, caregivers, as well as child respondents.

Mode of data collection

Face-to-face [f2f]

Research instrument

See attached questionnaires. All instruments have been shared except for IDELA (child assessment) as Save the Children have proprietary rights over this. Please contact the project Task Team Leader Deborah Newitter Mikesell dmikesell@worldbank.org for more information.

Cleaning operations

Data consistency checks (or High Frequency Checks) and back checks (audits) were conducted for all surveys remotely. Corrections were made during and after data collection after errors were reconciled.

All checks and cleaning was done using STATA and IPA possesses all the relevant code.

Response rate

Baseline I Out of the 276 schools that were selected for the Baseline I, 269 schools were surveyed (remember that potential replacement schools were also surveyed during Baseline I). This represents a response rate of 97%. It must, however, be emphasized that there were incomplete surveys in some of the schools, especially for the private schools. Incomplete surveys mean that only one of the surveys (instead of the two) was administered.

Baseline II All the surveys/assessment [with the exception of the Caregiver Survey] reported more than 90% response rate. The response rate for the Caregiver Survey was 60.0%.