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.

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).

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

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.

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.

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.

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. ○ ○

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The QS Rankings, renowned for its esteemed university evaluations, annually releases the QS World University Rankings. The 2024 edition comprises a dataset encompassing the top 100 universities globally, with each entry defined by 12 features.

The 'rank' feature denotes the university's position in the QS rankings, offering a quantitative representation of its standing. The 'university' column identifies the institution by name. The 'overall score' is a floating-point value derived from various contributing factors, reflecting the comprehensive evaluation undertaken by QS.

Academic reputation, an integral aspect, is quantified in the 'academic reputation' feature, while 'employer reputation' gauges the institution's standing in the professional realm. The 'faculty student ratio' is calculated by dividing the faculty count by the number of students, a metric often indicative of the learning environment's quality.

'Citations per faculty' delves into the scholarly impact, measuring the total citations received by an institution's papers over five years, normalized by faculty size. The 'international faculty ratio' and 'international students ratio' shed light on the global diversity of the academic community, capturing the proportion of foreign faculty and students.

The 'international research network' employs a formula to quantify the institution's global partnerships and collaborations. 'Employment outcomes' are assessed through a formula involving alumni impact and graduate employment indices, providing insights into the professional success of graduates.

Finally, the 'sustainability' feature evaluates an institution's commitment to environmental sciences, considering alumni outcomes and academic reputation within the field. It also examines the inclusion of climate science and sustainability in the curriculum, reflecting the growing emphasis on environmental consciousness in higher education.

In essence, this dataset encapsulates a multifaceted evaluation of universities worldwide, encompassing academic, professional, and sustainability dimensions, making it a valuable resource for individuals and institutions navigating the dynamic landscape of global higher education. VALUE FOUNDS IS HIPOTICALY data 2021

Argumentative skills are crucial for any individual at the personal and professional levels. In recent decades, there has been an increasing concern about the weak undergraduates' skills and considerable difficulty in reworking and expressing one's own reflection on a topic. In turn, this has implications for being a critical thinker, able to express an original point of view. Tailored interventions in Higher Education could constitute a powerful approach to promote argumentative skills and extend these skills to professional and personal life. In this regard, argument maps (AM) could prove to be a valuable support to the visualization process of arguments. They don’t just create associations between concepts, but trace the logical relationships between different statements, allowing you to track the reasoning chain and understand it better. We conducted an experimental study to investigate how a path with AM could support students in increasing the level of text comprehension (CoT) competence, in terms of identifying the elements of an argumentative text, and critical thinking (CT), in terms of reconstructing meaning and building their own reflection.

Our preliminary descriptive analysis suggested the positivity of the role of AM in increasing students’ CoT and CT proficiency levels

This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) composed of the following datasets and script:

1. Comprehension of Text and AMs Results - ExpAM.xlsx

2. Critical Thinking Results - CriThink.xlsx

3. Argumentative skills in Forum - ExpForum.xlsx

4. Selfassessment Results - Dataset_Quest.xlsx

5. Data for Correlation and Regression - Dataset_CorRegr.xlsx

6. Descriptive Statistics - Preliminary Analysis.R

7. Inferential Statistics - Correlation and Regression.R

Any comments or improvements are welcome!

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

This dataset contains college enrollment information for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university. This dataset includes enrollment in two-year and four-year institutions of higher education.

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.

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.

The dataset contains anonymized academic performance data of undergraduate engineering students from 2011 to 2020 at the nine campuses of the Universidad Nacional de Colombia. Key variables include socioeconomic status, faculty, gender, academic program, age, and cumulative weighted academic average (CWAA). This dataset provides insights into academic outcomes across various demographics, enabling analysis of patterns that may inform educational strategies and improve equity in engineering education.

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.

While enrolment in tertiary education has increased dramatically over the past decades, many university-aged students do not enrol, nor do they expect to earn a university degree. While it is important to promote high expectations for further education, it is equally important to ensure that students’ expectations are well-aligned with their actual abilities. Grade Expectations: How Marks and Education Policies Shape Students' Ambitions reveals some of the factors that influence students’ thinking about further education. The report also suggests what teachers and education policy makers can do to ensure that more students have the skills, as well as the motivation, to succeed in higher education. In 2009, students in 21 PISA-participating countries and economies were asked about their expected educational attainment. An analysis of PISA data finds that students who expect to earn a university degree show significantly better performance in math and reading when compared to students who do not expect to earn such a university degree. However, performance is only one of the factors that determine expectations. On average across most countries and economies, girls and socio-economically advantaged students tend to hold more ambitious expectations than boys and disadvantaged students who perform just as well; and students with higher school marks are more likely to expect to earn a university degree – regardless of what those marks really measure.

The dataset contains Academic-year-wise historically compiled data on the total number of Indian students who enrolled for Undergraduate, Graduate, Non-Degree and Optional Practical Training (OPT) courses in the United States of America (USA).

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).

Download the data that appears on the College Scorecard, as well as supporting data on student completion, debt and repayment, earnings, and more. Last updated on 4-19-2023.

Data from the Ministry of Training, Colleges and Universities' College NoName Program Enrolment Reporting system.

Provides aggregated key enrolment data for college students, like enrolment headcounts by program type, institution and campus.

Historical data ranges from 1996 to 2011-12, and is based on fall enrolment counts. To protect privacy, numbers are suppressed in categories with less than 10 students.

In 2012 a new data collection system was introduced, which collects data at the student level and includes many additional data elements which were not collected in the historical file (e.g., data was not collected on international students prior to 2013). For this reason it is not possible to report historical data in the same format as the 2012-13 and beyond data.

Related

• College enrolment
• 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