The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. 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 feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

At a time when OECD and partner countries are trying to figure out how to reduce burgeoning debt and make the most of shrinking public budgets, spending on education is an obvious target for scrutiny. Education officials, teachers, policy makers, parents and students struggle to determine the merits of shorter or longer school days or school years, how much time should be allotted to various subjects, and the usefulness of after-school lessons and independent study. This report focuses on how students use learning time, both in and out of school. What are the ideal conditions to ensure that students use their learning time efficiently? What can schools do to maximise the learning that occurs during the limited amount of time students spend in class? In what kinds of lessons does learning time reap the most benefits? And how can this be determined? The report draws on data from the 2006 cycle of the Programme of International Student Assessment (PISA) to describe differences across and within countries in how much time students spend studying different subjects, how much time they spend in different types of learning activities, how they allocate their learning time and how they perform academically.

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.

The National Survey of College Graduates is a repeated cross-sectional biennial survey that provides data on the nation's college graduates, with a focus on those in the science and engineering workforce. This survey is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

One of the sectors that felt the impact of the Corona Virus Disease 2019 (COVID-19) pandemic was the educational sector. The outbreak led to the immediate closure of schools at all levels thereby sending billions of students away from their various institutions of learning. However, the shut down of academic institutions was not a total one as some institutions that were solely running online programmes were not affected. Those who were running face to face and online modes quickly switched over to the online mode. Unfortunately, institutions that have not fully embraced online mode of study were greatly affected. 85% of academic institutions in Nigeria are operating face to face mode of study, therefore, majority of Nigerian students at all levels were affected by the COVID-19 lockdown. Social media platforms and emerging technologies were the major backbones of institutions that are running online mode of study, therefore, this survey uses the unified theory of acceptance and use of technology (UTAUT) model to capture selected Face to face Nigerian University students accessibility, usage, intention and willingness to use these social media platforms and emerging technologies for learning. The challenges that could mar the usage of these technologies were also revealed. Eight hundred and fifty undergraduate students participated in the survey.

The dataset includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file and the descriptive statistics for all the variables captured. This second version contains the reliability statistics of the UTAUT variables using Cronbach's alpha. This measured the reliability as well as the internal consistency of the UTAUT variables. This was measured in terms of the reliability statistics, inter-item correlation matrix and item-total statistics. Authors believed that the dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies.

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system.

Provides aggregated key enrolment data for college students, such as:

• Fall term headcount enrolment by campus, credential pursued and level of study
• Fall term headcount enrolment by program and Classification of Instructional Program
• Fall term headcount enrolment by student status in Canada and country of citizenship by institution
• Fall term headcount enrolment by student demographics (e.g., gender, age, first language)

To protect privacy, numbers are suppressed in categories with less than 10 students.

Related

• College enrolments - 1996 to 2011
• University enrolment
• Enrolment by grade in secondary schools
• School enrolment by gender
• Second language course enrolment
• Course enrolment in secondary schools
• Enrolment by grade in elementary schools

Student Enrollment reports the number of enrolled students, per grade.

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

For those interested in data on student drug addiction in 2024, several sources offer valuable datasets and statistics.

1. Kaggle Dataset: Kaggle hosts a specific dataset on student drug addiction. This dataset includes various attributes related to student demographics, substance use patterns, and associated behavioral factors. It's a useful resource for data analysis and machine learning projects focused on understanding drug addiction among students【5†source】.

2. National Survey on Drug Use and Health (NSDUH): This comprehensive survey provides detailed annual data on substance use and mental health across the United States, including among students. It covers a wide range of substances and demographic details, helping to track trends and the need for treatment services【6†source】【8†source】.

3. Monitoring the Future (MTF) Survey: Conducted by the National Institute on Drug Abuse (NIDA), this survey tracks drug and alcohol use and attitudes among American adolescents. It provides annual updates and is an excellent source for understanding trends in substance use among high school and college students【7†source】.

4. Australian Institute of Health and Welfare (AIHW): For those interested in a more global perspective, the AIHW offers data from the National Drug Strategy Household Survey, which includes information on youth and young adult drug use in Australia. This can be useful for comparative studies【10†source】.

For detailed datasets and further analysis, you can explore these resources directly:

• Kaggle Student Drugs Addiction Dataset
• NSDUH Data
• Monitoring the Future
• AIHW Drug Survey

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week. Data from August

The dataset contains Academic-year-, Programme- and Course-wise historically compiled data on the Number of International Students who have enrolled in the United States of America (U.S.A) for pursuing various types of Graduate, Under-graduate and OPT courses, along with percentage number of students pursing on each specific type of education

1. About the Dataset:

Description: Dive into the world of college placements with this dataset designed to unravel the factors influencing student placement outcomes. The dataset comprises crucial parameters such as IQ scores, CGPA (Cumulative Grade Point Average), and placement status. Aspiring data scientists, researchers, and enthusiasts can leverage this dataset to uncover patterns and insights that contribute to a deeper understanding of successful college placements.

2. Projects Ideas:

Project Idea 1: Predictive Modeling for College Placements Utilize machine learning algorithms to build a predictive model that forecasts a student's likelihood of placement based on their IQ scores and CGPA. Evaluate and compare the effectiveness of different algorithms to enhance prediction accuracy.

Project Idea 2: Feature Importance Analysis Conduct a feature importance analysis to identify the key factors that significantly influence placement outcomes. Gain insights into whether IQ, CGPA, or a combination of both plays a more dominant role in determining success.

Project Idea 3: Clustering Analysis of Placement Trends Apply clustering techniques to group students based on their placement outcomes. Explore whether distinct clusters emerge, shedding light on common characteristics or trends among students who secure placements.

Project Idea 4: Correlation Analysis with External Factors Investigate the correlation between the provided data (IQ, CGPA, placement) and external factors such as internship experience, extracurricular activities, or industry demand. Assess how these external factors may complement or influence placement success.

Project Idea 5: Visualization of Placement Dynamics Over Time Create dynamic visualizations to illustrate how placement trends evolve over time. Analyze trends, patterns, and fluctuations in placement rates to identify potential cyclical or seasonal influences on student placements.

3. Columns Explanation:

• IQ:

  • Definition: Intelligence Quotient, a measure of a person's intellectual abilities.
  • Data Type: Numeric
  • Range: Typically, IQ scores range from 70 to 130, with 100 being the average.

• CGPA:

  • Definition: Cumulative Grade Point Average, a measure of a student's overall academic performance.
  • Data Type: Numeric
  • Range: Typically, CGPA is on a scale of 0 to 4, with 4 being the highest possible score.

• Placement:

  • Definition: Binary variable indicating whether a student secured a placement (1) or not (0).
  • Data Type: Categorical (Binary)
  • Values: 1 (Placement secured) or 0 (No placement).

These columns collectively provide a comprehensive snapshot of a student's intellectual abilities, academic performance, and their success in securing a placement. Analyzing this dataset can offer valuable insights into the dynamics of college placements and inform strategies for optimizing student outcomes.

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year. Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

The Bangladeshi Universities Dataset provides information on the geographical location, administrative division, field of specialization, type and Ph.D. granting status of various universities in Bangladesh.

The "Location" column in the dataset provides the geographic location of each university in Bangladesh. This information can be used to identify universities located in different parts of the country and to analyze the distribution of higher education institutions across the regions of Bangladesh.

The "Division" column categorizes each university according to its administrative division within Bangladesh. This information can be useful for studying the distribution of higher education institutions across the administrative regions of the country and identifying any regional disparities in access to higher education.

The "Specialization" column in the dataset identifies the fields of study that each university is known for. This information can be useful for students seeking admission to universities that offer programs in their areas of interest and for researchers studying the strengths and weaknesses of higher education institutions in Bangladesh.

The "Type" column categorizes each university as either public or private. This information can be used to analyze the distribution of public and private universities in Bangladesh and to compare the quality and accessibility of education offered by each type of institution.

Finally, the "Ph.D. granting" column indicates whether each university offers doctoral programs or not. This information can be useful for students seeking advanced degrees and for researchers studying the availability and quality of doctoral education in Bangladesh.

The questionnaires were distributed among 342 full-time students who studied at Universitas Negeri Jakarta in the 2022-2023 academic year. This data set contains data on undergraduate students’ attitudes toward e-learning during the COVID-19 pandemic. E-learning attitudes consist of 2 subscale constructs including avoidance of e-learning (10 items) and tendency to e-learning (10 items).

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

This dataset contains the total Fall enrollment headcount and FTE for undergraduate and graduate students at all public Massachusetts institutions of higher education since 2014.

This dataset is 1 of 2 datasets that is also published in the interactive Fall Enrollment dashboard on the Department of Higher Education Data Center:

Public Postsecondary Fall Enrollment Public Postsecondary Fall Enrollment by Race and Gender

Related datasets: Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Figures published here may vary from those of individual institutions due to differences in calculation methodologies.