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.

Kris and I, surveyed multiple student through QR codes at Grand Canyon University located in Phoenix,Arizona. These Questions where based around student AI knowledge, student general AI use case, student use of AI in school, interest of pursing a career in AI and finally major. With over 250 datapoints from 3/31/23 to 1/4/24.

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 dataset focuses on the dynamic assignment of college students to innovative training projects. It captures comprehensive information about students, project opportunities, and the outcomes of past matches, enabling research and analysis in educational optimization, project allocation, and student engagement.

The dataset combines student profiles, project characteristics, and feedback from past assignments to support data-driven decision-making in academic or institutional project planning environments.

💡 Key Features Detailed student information, including academic records, skills, interests, and availability

Diverse project metadata, covering domains, skill requirements, difficulty levels, and innovation ratings

Real-world inspired match outcomes, such as engagement levels, satisfaction, and project success

A reward-based target column representing match effectiveness for evaluation or prediction tasks

🧾 Data Fields Student attributes: year of study, CGPA, skills, interest areas, learning styles, availability

Project attributes: domain, type, required skills, weekly commitment, innovation index

Match outcomes: engagement score, satisfaction rating, project success score, outcome label

Reward value: a numeric indicator of how successful or effective the match was

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

Context: The BS Data Science Student Marks Sheet Dataset is a comprehensive collection of academic records from students enrolled in the Data Science program at a university. This dataset is valuable for analyzing student performance across different assessment components, understanding grading trends, and exploring factors influencing academic achievement in data science courses.

Content: The dataset includes the following information across three pages:

**Grades: **This page provides details of the grades obtained by students in various assessments throughout the semester. It includes columns such as:

Student ID: A unique identifier for each student. **Assignment Marks: **Marks obtained by students in assignments or projects. Quiz Marks: Marks obtained by students in quizzes or in-class assessments. Midterm Exam Marks: Marks obtained by students in the midterm examination. Final Marks: Marks obtained in any other assessments as per course requirements. **Total Marks: **Total marks obtained by students in all assessments. Grade: The final grade assigned to the student based on their total marks. **Sessional: **This page provides a detailed breakdown of sessional assessments, including attendance, participation, and performance in sessions or labs. It includes columns such as:

Student ID: A unique identifier for each student. Sessional Tests: Marks obtained in tests or quizzes conducted during sessions or labs. Sessional Assignment Marks: Marks obtained in assignments or practical exercises conducted during sessions or labs. Final: This page summarizes the final assessment outcomes, including overall course grades and any additional feedback or remarks. It includes columns such as:

Student ID: A unique identifier for each student. Course ID: A unique identifier for each data science course. Final Exam Marks: Marks obtained by students in the final examination. Final Assignment Marks: Marks obtained in final assignments or projects. Overall Course Grade: The final grade assigned to the student for the entire course. Remarks: Any additional comments or feedback provided by instructors or examiners. Acknowledgements: We would like to extend our gratitude to the university faculty members, administrative staff, and students who contributed to the compilation and organization of this dataset. Their cooperation and support have been instrumental in creating this valuable resource for academic analysis and research in the field of data science education.

**Inspiration: **The creation of this dataset is motivated by the recognition of the importance of comprehensive academic records in facilitating research and analysis in data science education. By making this dataset available, we aim to support educators, researchers, and students in their efforts to understand student performance, identify areas for improvement, and enhance teaching and learning practices in data science programs.

Through the utilization of the BS Data Science Student Marks Sheet Dataset, we aspire to contribute to the advancement of data-driven approaches in education and to foster a culture of excellence and continuous improvement within data science higher education programs.

University Student Enrollment Data

Demographics and Academic Details of University Student Enrollments

By Gove [source]

About this dataset

This dataset represents a comprehensive collection of valuable and relevant information regarding student registration across a multitude of universities. It provides an in-depth insight into various aspects of this subject matter, making it an indispensable tool for any research related to university student registrations.

The information contained within this particular dataset offers extensive details about each individual student. This rich, individual data includes demographic particulars such as their age, gender and nationality - details which could yield interesting points of analysis when correlated against other factors within the data.

Additionally, this dataset maintains academic records for each registered student, providing detailed descriptions like course of study and year of enrollment. This formative data aids in understanding students' registration patterns over the years or tracking their academic progression throughout their tenure at university.

Moreover, the dataset is also expected to contain vital statistics tied to individual universities where these students are enrolled. Such expected details include each institution's location which can provide geo-political or socio-economic insights pertaining to university selection trends amongst students.

Further enriching the body of knowledge available within this repository is potential data related to specific course offerings by these universities – a feature useful for assessing popular disciplines or identifying shifts in educational trends based on subject popularity.

Another significant set of information which might be found inside this repository pertains to faculty specifics including number and qualifications alongside overall ranking standings – these can serve as additional metrics in gauging perceived quality or reputation among the registered student bodies with respect to selecting universities for further studies.

In sum, whether you’re interested in mapping out educational trends over time; analyzing demographic profiles against choice courses; studying correlations between nationality and select colleges; or looking into institutional rankings’ sway over enrollments – this amalgamation holds invaluable keys that unlock numerous possibilities through exploration via different combinations making it versatile enough for diverse investigatory needs while offering deep analytical potentials for those willing explore its depths

How to use the dataset

• Student Demographic Analysis: You can use this dataset to understand the demographic distribution of students across universities. This involves analyzing information related to age, nationality, and gender among others. For example, you might want to find out which university has the highest number of international students or what is the gender ratio in a specific course of study.

• Analysis on Courses & Faculties: Data from this dataset can be used for insightful exploration into various courses and faculties offered by different universities. You may want to investigate questions like What is the most popular course?or Which university has a larger faculty for science stream?.

• University Comparison: The data allows for comparison between different universities based on their student population, diversity, departments/faculties and courses being offered etc.. In doing so, you could discern trends or patterns linked with university ranking and location that may play role in student enrollment decisions.

• Tracking Enrollment Trends: By examining factors such as year of enrollment and course selections over time, it becomes possible to track trends within each school's student body population or wider academic field at large scale over multiple years; potentially even predicting future movements.

• The dataset also provides excellent resources for machine learning applications such as predictive models for student academic performance or building recommender systems capable off suggesting best suited unversities or courses based on individual characterstics.

• This data set can also aid administrative decision making processes around things like budget allocation (based on number of students per faculty), policy changes related with improving diversity within campus etc., providing valuable quantitative backing towards making such important decisions.

Remember that while using this dataset correctly respecting privacy norms is paramount given sensitive nature involved with personal details included here; always adhere...

This dataset contains data on the number of students in schools and universities categorized by level of education (Pre-primary, Primary, etc.), type of education (Government, Private), and gender (Male, Female). The data provides insight into the enrollment trends across different education levels and types of schools in the region. This dataset is essential for analyzing gender and educational distribution within both government and private institutions.

The College Scorecard is designed to increase transparency, putting the power in the hands of the public — from those choosing colleges to those improving college quality — to see how well different schools are serving their students.

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. ○ ○

This dataset provides average cost data for colleges and universities in Iowa by academic year and sector. Costs include tuition and room & board for both on and off campus. Data begins with academic year 2007-08 (year ending 6/30/2008). Sectors include regent universities, private for-profit colleges and universities, private not for-profit colleges and universities and community colleges.

About this dataset

Context

The dataset tabulates the population of College Springs by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of College Springs across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 56.68% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the College Springs is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of College Springs total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Springs Population by Race & Ethnicity. You can refer the same here

Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300,000+ graduates from higher education institutions every year. Higher education opportunities range from two year, public and private institutions, four year, public and private institutions and regional education centers. Collectively, Maryland's higher education facilities offer every kind of educational experience, whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland is proud that nearly one-third of its residents 25 and older have a bachelor's degree or higher, ranking in the top 5 amongst all states. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live, learn, work and raise a family.

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

This project examined different aspects of campus crime -- specifically, the prevalence of crimes among college students, whether the crime rate was increasing or decreasing on college campuses, and the factors related to campus crime. Researchers made the assumption that crimes committed by and against college students were likely to be related to drug and alcohol use. Specific questions designed to be answered by the data include: (1) Do students who commit crimes differ in their use of drugs and alcohol from students who do not commit crimes? (2) Do students who are victims of crimes differ in their use of drugs and alcohol from students who are not victims? (3) How do multiple offenders differ from single offenders in their use of drugs and alcohol? (4) How do victims of violent crimes differ from victims of nonviolent crimes in their use of drugs and alcohol? (5) What types of student crimes are more strongly related to drug or alcohol use than others? (6) Other than drug and alcohol use, in what ways can victims and perpetrators of crimes be differentiated from students who have had no direct experiences with crime? Variables include basic demographic information, academic information, drug use information, and experiences with crime since becoming a student.

This dataset contains the total annual FTE and unduplicated headcount enrollment for undergraduate and graduate students in credit-bearing courses at public community colleges and state universities in Massachusetts since 2014. Data are disaggregated by fiscal year, segment, institution, and student attributes such as enrollment level, residency, age, race/ethnicity, and gender.

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

1) Public Postsecondary Annual Enrollment: Detail 2) Public Postsecondary Annual Enrollment: Summary

Related datasets: 1) Public Postsecondary Fall Enrollment 2) Public Postsecondary Fall Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Annual enrollment refers to a 12 month enrollment period over one fiscal year (July 1 through June 30). - Figures published by DHE may differ slightly from figures published by other institutions and organizations due to differences in timing of publication, data definitions, and calculation logic. - Data for the University of Massachusetts are not included due to unique reporting requirements. See Fall Enrollment for HEIRS data on UMass enrollment. -The most common measure of enrollment is headcount of enrolled students. Annual headcount enrollment is unduplicated, meaning any individual student is only counted once per institution and fiscal year, even if they are enrolled in multiple terms. - Enrollment can also be measured as full-time equivalent (FTE) students, a calculation based on the sum of credits carried by all enrolled students. In a fiscal year, 30 undergraduate credits = 1 undergraduate FTE, and 24 graduate credits = 1 graduate FTE at a state university. - For precise calculations and aggregations, use the FTE_RAW column. The FTE column is for display only.

CollegeScorecard US College Graduation and Opportunity Data

Exploring Student Success and Outcomes

By Noah Rippner [source]

About this dataset

This dataset provides an in-depth look at the data elements for the US College CollegeScorecard Graduation and Opportunity Project Use Case. It contains information on the variables used to create a comprehensive report, including Year, dev-category, developer-friendly name, VARIABLE NAME, API data type, label, VALUE, LABEL , SCORECARD? Y/N , SOURCE and NOTES. The data is provided by the U.S Department of Education and allows parents, students and policymakers to take meaningful action to improve outcomes. This dataset contains more than enough information to allow people like Maria - a 25 year old recent US Army veteran who wants a degree in Management Systems and Information Technology -to distinguish between her school options; access services; find affordable housing near high-quality schools which are located in safe neighborhoods that have access to transport links as well as employment opportunities nearby. This highly useful dataset provides detailed analysis of all this criteria so that users can make an informed decision about which school is best for them!

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How to use the dataset

This dataset contains data related to college students, including their college graduation rates, access to opportunity indicators such as geographic mobility and career readiness, and other important indicators of the overall learning experience in the United States. This guide will show you how to use this dataset to make meaningful conclusions about high education in America.

First, you will need to be familiar with the different fields included in this CollegeScorecard’s US College Graduation and Opportunity Data set. Each record is comprised of several data elements which are defined by concise labels on the left side of each observation row. These include labels such as Name of Data Element, Year, dev-category (i.e., developmental category), Variable Name, API data type (i.e., type information for programmatic interface), Label (i.e., descriptive content labeling for visual reporting), Value , Label (i.e., descriptive value labeling for visual reporting). SCORECARD? Y/N indicates whether or not a field pertains to U.S Department of Education’s College Scorecard program and SOURCE indicates where the source of the variable can be found among other minor details about that variable are found within Notes column attributed beneath each row entry for further analysis or comparison between elements captured across observations

Now that you understand the components associated within each element or label related within Observation Rows identified beside each header label let’s go over some key steps you can take when working with this particular dataset:

• Utilize year specific filters on specified fields if needed — e.g.; Year = 2020 & API Data Type = Character

• Look up any ‘NCalPlaceHolder” values if applicable — these are placeholders often stating values have been absolved fromScorecards display versioning due conflicting formatting requirements across standard conditions being met or may state these details have still yet been updated recently so upon assessment wait patiently until returns minor changes via API interface incorporate latest returned results statements inventory configuration options relevant against budgetary cycle limits established positions

• Pivot data points into more custom tabular structured outputs tapering down complex unstructured RAW sources into more digestible Medium Level datasets consumed often via PowerBI / Tableau compatible Snapshots expanding upon Delimited text exports baseline formats provided formerly

• Explore correlations between education metrics our third parties documents generated frequently such values indicative educational adherence effects ROI growth potential looking beyond Campus Panoramic recognition metrics often supported outside Social Medial Primary

Research Ideas

• Creating an interactive dashboard to compare school performance in terms of safety, entrepreneurship and other criteria.
• Using the data to create a heat map visualization that shows which cities are most conducive to a successful educational experience for students like Maria.
• Gathering information about average course costs at different universities and mapping them relative to US unemployment rates indicates which states might offer the best value for money when it comes to higher education expenses

Ack...

The primary research objective of this study was to examine the prevalence, nature, and reporting of various types of sexual assault experienced by university students in an effort to inform the development of targeted intervention strategies. In addition, the study had two service-oriented objectives: (1) to educate students about various types of sexual assault, how they can maximize their safety, and what they can do if they or someone they know has been victimized and (2) to provide students with information about the campus and community resources that are available should they need assistance or have any concerns or questions. The study involved a Web-based survey of random samples of undergraduate students at two large public universities, one located in the South (University 1) and one located in the Midwest (University 2). Researchers drew random samples of students aged 18-25 and enrolled at least three-quarters' time at each university to participate in the study. The survey was administered in the winter of 2005-2006, and a total of 5,446 undergraduate women and 1,375 undergraduate men participated for a grand total of 6,821 respondents. Sampled students were sent an initial recruitment e-mail that described the study, provided a unique study ID number, and included a hyperlink to the study Web site. During each of the following weeks, students who had not completed the survey were sent follow-up e-mails and a hard-copy letter encouraging them to participate. The survey was administered anonymously and was designed to be completed in an average of 15 minutes. Respondents were provided with a survey completion code that, when entered with their study ID number at a separate Web site, enabled them to obtain a $10 Amazon.com gift certificate. The survey was divided into six modules. The Background Information module included survey items on demographics, school classification (year of study, year of enrollment, transfer status), residential characteristics, academic performance, and school involvement. An Alcohol and Other Drug Use module generated a number of measures of alcohol and drug use, and related substance use behaviors. A Dating module included items on sexual orientation, dating, consensual sexual activity, and dating violence. The Experiences module was developed after extensive reviews of past surveys of sexual assault and generated information on physically forced sexual assault and incapacitated sexual assault. For both physically forced and incapacitated sexual assault, information was collected on completed and attempted assaults experienced before entering college and since entering college. For male respondents, a Behaviors module asking about the perpetration of the same types of sexual assault covered in the Experiences module was included. The final module of the survey covered attitudes about sexual assault and attitudes about the survey. The data file contains 747 variables.

The purpose of this project is to determine how college students distribute their activities in time (with a particular focus on academic and athletic activities) and to examine the factors that influence such distributions.Each R reported once about each of the seven days of the week and an additional time about either Saturday or Sunday. Rs were told the week before they were to report which day was assigned and were given a report form to complete during that day. They entered the i nformation from that form when they returned the next week.The activity codes included were: 0: Sleeping. 1: Attending classes. 2: Studying or preparing classroom assignments. 3: Working at a jog (including CAPS). 4: Cooking, home chores, laundry, grocery shopping. 5: Errands, non-grocery shopping, gardening, animal care. 6: Eating. 7: Bathing, getting dressed, etc. 8: Sports, exercising, other physical activities. 9: Playing competitive games (cards, darts, videogames, frisbee, chess, Tr ivial Pursuit, etc.). 10: Participating in UNC-sponsored organizations (student government, band, sorority, etc.). 11: Listening to the radio. 12: Watching TV. 13: Reading for pleasure (not studying or reading for class). 14: Going to a movie. 15: Attending a cultural event (such as a play, concert, or museum). 16: Attending a sports event as a spectator. 17: Partying. 18: Religious activities. 19: Conversation. 20: Travel. 21: Resting. 22: Doing other things DIARY1-8: These datasets contain a matrix of activities by times for a particular day. Included is time period, activity code (see above), # of friends present, # of others present. (Rs were allowed to report doing two activities at once. In these cases they were also asked to report the % of time during the time period affected which was allocated to the first of the two activities listed.)THE DIARY DATASETS ARE STORED IN RAW FORM. SUMMARY FILES, CALLED TIMEREP, CONTAIN MOST SUMMA RY INFORMATION WHICH MIGHT BE USED IN ANALYSES. THE DIARY DATASETS CAN BE LISTED TO ALLOW UNIQUE CODING OF THE ORIGINAL DATA. Each R reported once about each of the seven days of the week and an additional time about either Saturday or Sunday.TIMEREP: The TIMEREP dataset is a summary file which gives the amount of time spent on each activity during each of the eight reporting periods and also includes more detailed information about many of the activities from follow-up questions which were asked if the respondent reported having engaged in certain activities. Data from additional questions asked of every respondent after each diary entry are also included: contact with family members, number of alcoholic drinks consumed during the 24 hour period reported on, number of friends and others present while drinking, number of cigarettes smoked on day reported about, and number of classes skipped on day reported about. Follow-up questions include detail about kind of physical activity or sports participation, kind of university organization, kind of radio program listened to and place of listening, kind of TV program watched and place of watching, kind of reading material read and topic, alcohol consumed while partying and place of partying, conversation topics, kind of travel, activities included in 'other' category.Special processing is required to put the dataset into SAS format. See spec for details.

This dataset provides a comprehensive view of student performance and learning behavior, integrating academic, demographic, behavioral, and psychological factors.

It was created by merging two publicly available Kaggle datasets, resulting in a unified dataset of 14,003 student records with 16 attributes. All entries are anonymized, with no personally identifiable information.

Key Features

• Study behaviors & engagement → StudyHours, Attendance, Extracurricular, AssignmentCompletion, OnlineCourses, Discussions
• Resources & environment → Resources, Internet, EduTech
• Motivation & psychology → Motivation, StressLevel
• Demographics → Gender, Age (18–30 years)
• Learning preference → LearningStyle
• Performance indicators → ExamScore, FinalGrade

Objectives & Use Cases

The dataset can be used for:

• Predictive modeling → Regression/classification of student performance (ExamScore, FinalGrade)
• Clustering analysis → Identifying learning behavior groups with K-Means or other unsupervised methods
• Educational analytics → Exploring how study habits, stress, and motivation affect outcomes
• Adaptive learning research → Linking behavioral patterns to personalized learning pathways

Analysis Pipeline (from original study)

The dataset was analyzed in Python using:

• Preprocessing → Encoding, normalization (z-score, Min–Max), deduplication
• Clustering → K-Means, Elbow Method, Silhouette Score, Davies–Bouldin Index
• Dimensionality Reduction → PCA (2D/3D visualizations)
• Statistical Analysis → ANOVA, regression for group differences
• Interpretation → Mapping clusters to LearningStyle categories & extracting insights for adaptive learning

File

• merged_dataset.csv → 14,003 rows × 16 columns Includes student demographics, behaviors, engagement, learning styles, and performance indicators.

Provenance

• Source: Zenodo – Student Performance and Learning Behavior Dataset
• Creator: Kamal Najem (2024)
• License: CC BY 4.0 (per Zenodo terms)

This dataset is an excellent playground for educational data mining — from clustering and behavioral analytics to predictive modeling and personalized learning applications.