In an era when education is supposed to be a means for good jobs and linked to extrinsic values such as fame and money, students are losing interest in school education. The goal of this research is to see if there is any correlation between the students' views of school worthiness and schoolwork, and demographic variables. Our hypothesis is that several key demographics involving diversity, income, and education will affect how students view school and its importance. Results show that there is no correlation between the demographic variables we analyzed and the student perception of school worthiness.

This dataset contains yearly certified enrollment for all public school districts (with physical boundaries) in Wisconsin for the 2023-2024 school year. This data is also available in the WISEdash Public Portal. This dataset is derived from publicly available files on the WISEdash Download Page. Enrollment Count is the number of students enrolled on specific dates as determined by school enrollment/exit dates that cover those dates. Percent Enrollment by Student Group is a percent of the enrollment count for all student groups combined. Reporting Disability is indicated in the pupil’s individualized education program (IEP) or individualized service plan (ISP). A person's race or ethnicity is the racial and/or ethnic group to which the person belongs or with which he or she most identifies. Ethnicity is self-reported as either Hispanic/Not Hispanic. Race is self-reported as any of the following 5 categories: Asian, American Indian or Alaskan Native, Black or African American, Native Hawaiian or other Pacific Islander, or White. The data displayed reflects the race/ethnicity that is reported by school districts to DPI.An economically disadvantaged student is one who is identified by Direct Certification (only if participating in the National School Lunch Program) OR a member of a household that meets the income eligibility guidelines for free or reduced-price meals (less than or equal to 185 percent of Federal Poverty Guidelines) under the National School Lunch Program (NSLP) OR identified by an alternate mechanism, such as the alternate household income form.English Learner status is any student whose first language, or whose parents' or guardians' first language, is not English and whose level of English proficiency requires specially designed instruction, either in English or in the first language or both, in order for the student to fully benefit from classroom instruction and to be successful in attaining the state's high academic standards expected of all students at their grade level.A child is eligible for the Migrant Education Program (MEP) (and thereby eligible to receive MEP services) if the child: meets the definition of “migratory child” in section 1309(3) of the ESEA,[1] and is an “eligible child” as the term is used in section 1115(c)(1)(A) of the ESEA and 34 C.F.R. § 200.103; and has the basis for the State’s determination that the child is a “migratory child” properly recorded on the national Certificate of Eligibility (COE). Eligibility determination is made by a Wisconsin state migrant recruiter during a face-to-face family interview.

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

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How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](https://creativecommons.org/public...

This dataset contains the total annual unduplicated enrollment headcount and percentages by race and gender for undergraduate and graduate students at public community colleges and state universities in Massachusetts since 2014.

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:

Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

Related datasets: Public Postsecondary Fall Enrollment 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.

PURPOSE The purpose of this study was to investigate the variables that best predicted Transfronterizx college students’ sense of on-campus belonging in higher education at the San Diego-Tijuana borderlands. To identify the variables that predicted students’ on-campus sense of belonging, this study also examined their demographic characteristics, student characteristics, transborder interactions, and beliefs about their campus climate. A total of 100 Transfronterizx students (58% female and 42% male) from a four-year higher education institution in the Southwestern region of the United States participated in this study. PARTICIPANTS The data for this study was collected during the fall 2015 academic year. A total of 100 students (58% female, 42% male) from a four-year higher education institution located in a border town in the southwestern region of the United States participated in this study. Of these, 81 (81%) were undergraduates, and 19 (19%) were master's students. The mean age of participants was 23.66 years (SD = 5.19), with a range of 18 to 50 years. Overall, 9,000 undergraduate and 2,000 graduate Latinx students were contacted via email to participate in the study. A total of 130 participants completed the questionnaire. However, responses from students who had already graduated or were not living a transborder life during the study period were excluded from the analysis and findings. As a result, only 100 students were included in the final sample. SITE The recruitment site for this study was a Hispanic-serving, four-year public higher education institution located in a border town in the southwestern region of the United States. The campus is situated in close proximity to the U.S.-Mexico international border, and campus leaders have engaged in various binational collaboration efforts with non-profit organizations and higher education institutions in Mexico. This institution was selected as the recruitment site due to its proximity to the U.S.-Mexico international border. At the time of this study, the university served approximately 32,000 students. However, there is no record of the number of students who attend the campus and live a transborder life in the U.S.-Mexico region. Researchers explain that, due to the nature of the Transborder population—where many members have dual citizenships and dual domiciles—it is difficult to track the exact number of students who are part of the U.S.-Mexico Transfronterizx community (Chavez Montaño, 2006). PROCEDURES AND RECRUITMENT The methodological procedure for this study was descriptive in nature. The recommended sample size for a descriptive study is approximately 100 participants for each major subgroup (Mertens, 2015). In line with this recommendation, 100 students from the higher education institution mentioned above participated in the study. Upon receiving Institutional Review Board (IRB) approval, the consent form and data collection instruments were uploaded to the Qualtrics system for participants to access. A Qualtrics link to the consent form and data collection instruments was included in a recruitment email sent to students via social media outlets, student clubs, and campus organizations. A total of 130 students from the higher education institution completed the questionnaire; however, 30 responses were excluded because the students did not meet the participant requirements. As a result, only 100 students were included in this study. INSTRUMENT The researcher collected quantitative responses about the transborder experiences of college students who live a transborder life through a 30-item questionnaire to address the first and second sub-research questions of this study: (1) What are the demographic and student characteristics of Transfronterizx college students from the U.S.-Mexico Southwest Border Region? and (2) What are Transfronterizx college students’ transborder characteristics and belief levels about living a transborder life?To capture students’ demographic characteristics, the questionnaire included nominal questions about students’ age, race, ethnicity, cultural identification, and academic experiences. It also included nominal questions about students’ transborder interactions, the circumstances that led them to live a transborder life, and their current reasons for continuing to do so. Additionally, two ordinal items measured students’ beliefs about living a transborder life using a Likert scale ranging from 1 = strongly disagree to 4 = strongly agree. The researcher administered the following subscales from the National Survey of Hispanic Students (NSHS) (Hurtado & Carter, 1997) to address the third and fourth sub-research questions of this study: Sense of Belonging to Campus (SBC), Experienced Discrimination-Exclusion (EDE), and Perceptions of Campus Racial-Ethnic Tension (PCRET). These sub-research questions were: (3) What are Transfronterizx college students’ belief levels about their sense of belonging, experiences...

Historical Dataset of Sadler Means Ywla is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2016-2023),Total Classroom Teachers Trends Over Years (2016-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2016-2023),Asian Student Percentage Comparison Over Years (2016-2023),Hispanic Student Percentage Comparison Over Years (2016-2023),Black Student Percentage Comparison Over Years (2016-2023),White Student Percentage Comparison Over Years (2016-2023),Two or More Races Student Percentage Comparison Over Years (2016-2023),Diversity Score Comparison Over Years (2016-2023),Free Lunch Eligibility Comparison Over Years (2016-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2016-2023),Reading and Language Arts Proficiency Comparison Over Years (2015-2022),Math Proficiency Comparison Over Years (2015-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2015-2022)

Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an

Description. This project contains the dataset relative to the Galatanet survey, conducted in 2009 and 2010 at the Galatasaray University in Istanbul (Turkey). The goal of this survey was to retrieve information regarding the social relationships between students, their feeling regarding the university in general, and their purchase behavior. The survey was conducted during two phases: the first one in 2009 and the second in 2010.

The dataset includes two kinds of data. First, the answers to most of the questions are contained in a large table, available under both CSV and MS Excel formats. An description file allows understanding the meaning of each field appearing in the table. Note thesurvey form is also contained in the archive, for reference (it is in French and Turkish only, though). Second, the social network of students is available under both Pajek and Graphml formats. Having both individual (nodal attributes) and relational (links) information in the same dataset is, to our knowledge, rare and difficult to find in public sources, and this makes (to our opinion) this dataset interesting and valuable.

All data are completely anonymous: students' names have been replaced by random numbers. Note that the survey is not exactly the same between the two phases: some small adjustments were applied thanks to the feedback from the first phase (but the datasets have been normalized since then). Also, the electronic form was very much improved for the second phase, which explains why the answers are much more complete than in the first phase.

The data were used in our following publications:

Labatut, V. & Balasque, J.-M. (2010). Business-oriented Analysis of a Social Network of University Students. In: International Conference on Advances in Social Network Analysis and Mining, 25-32. Odense, DK : IEEE. ⟨hal-00633643⟩ - DOI: 10.1109/ASONAM.2010.15

An extended version of the original article: Labatut, V. & Balasque, J.-M. (2013). Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection. Özyer, T.; Rokne, J.; Wagner, G. & Reuser, A. H. (Eds.), The Influence of Technology on Social Network Analysis and Mining, Springer, 2013, chap.6, 303-330. ⟨hal-00633650⟩ - DOI: 10.1007/978-3-7091-1346-2_13

A more didactic article using some of these data just for illustration purposes: Labatut, V. & Balasque, J.-M. (2012). Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application. Abraham, A. & Hassanien, A.-E. (Eds.), Computational Social Networks: Tools, Perspectives and Applications, Springer, chap.4, 81-113. ⟨hal-00633653⟩ - DOI: 10.1007/978-1-4471-4048-1_4

Citation. If you use this data, please cite article [1] above:

@InProceedings{Labatut2010, author = {Labatut, Vincent and Balasque, Jean-Michel}, title = {Business-oriented Analysis of a Social Network of University Students}, booktitle = {International Conference on Advances in Social Networks Analysis and Mining}, year = {2010}, pages = {25-32}, address = {Odense, DK}, publisher = {IEEE Publishing}, doi = {10.1109/ASONAM.2010.15},}

Contact. 2009-2010 by Jean-Michel Balasque (jmbalasque@gsu.edu.tr) & Vincent Labatut (vlabatut@gsu.edu.tr)

License. This dataset is open data: you can redistribute it and/or use it under the terms of the Creative Commons Zero license (see license.txt).

In 2024, 92 percent of the students enrolled in Macau University of Science and Technology in Macao were international students. Contrastingly, 30 percent of the students enrolled in The Hong Kong University of Science and Technology were international students in 2024. International students are pivotal to a university in terms of economic contribution and reputation. As a result, many universities across the Asia-Pacific region offer English as a universal language, meaning international students have opportunities to excel in Asian universities.

BNIA-JFI tracks twenty seven indicators for elementary, middle, and high school students in an effort to measure the educational performance and youth engagement in Baltimore City. These indicators are grouped into the following seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. The source of data for the Education and Youth section is provided by the Baltimore City Public School System, the American Community Survey and the Baltimore City Board of Elections. The data provided by the Baltimore City Public Schools includes student address, which allows BNIA-JFI to present data on educational performance by the neighborhood in which the student lives, not by the school attended. This allows BNIA-JFI to examine the relationship between community conditions and educational performance. Education and Youth indicators may not be directly comparable to data provided by either the Baltimore City Public School System or the Maryland Department of Education due to several reasons including differences in methodology used to create indicators, excluding students who cannot be matched between data files provided by BCPSS, and excluding students whose home address cannot be geocoded. In the 2009-2010 school year, 3.4% of the student addresses could not be matched or geocoded. This means that these students were not included in our analysis and not included in calculations for the City as a whole and therefore direct comparisons to data and results available through the Baltimore City Public Schools and the Maryland Report Card cannot be made.

Includes mean values based on the daily observations of the 14 PORTAAL practices included in SEM analyses for each unit of analysis. Binary gender coded as 1 = male, 0 = female. EOP and URM coded as 1 = EOP/URM, 0 = non-EOP/non-URM. (XLSX)

Abstract

The PIRLS 2006 aimed to generate a database of student achievement data in addition to information on student, parent, teacher, and school background data for the 47 areas that participated in PIRLS 2006.

Geographic coverage

Nationally representative

Analysis unit

Units of analysis in the study are schools, students, parents and teachers.

Universe

PIRLS is a study of student achievement in reading comprehension in primary school, and is targeted at the grade level in which students are at the transition from learning to read to reading to learn, which is the fourth grade in most countries. The formal definition of the PIRLS target population makes use of UNESCO's International Standard Classification of Education (ISCED) in identifying the appropriate target grade:

"…all students enrolled in the grade that represents four years of schooling, counting from the first year of ISCED Level 1, providing the mean age at the time of testing is at least 9.5 years. For most countries, the target grade should be the fourth grade, or its national equivalent."

ISCED Level 1 corresponds to primary education or the first stage of basic education, and should mark the beginning of "systematic apprenticeship of reading, writing, and mathematics" (UNESCO, 1999). By the fourth year of Level 1, students have had 4 years of formal instruction in reading, and are in the process of becoming independent readers. In IEA studies, the above definition corresponds to what is known as the international desired target population. Each participating country was expected to define its national desired population to correspond as closely as possible to this definition (i.e., its fourth grade of primary school). In order to measure trends, it was critical that countries that participated in PIRLS 2001, the previous cycle of PIRLS, choose the same target grade for PIRLS 2006 that was used in PIRLS 2001. Information about the target grade in each country is provided in Chapter 9 of the PIRLS 2006 Technical Report.

Although countries were expected to include all students in the target grade in their definition of the population, sometimes it was not possible to include all students who fell under the definition of the international desired target population. Consequently, occasionally a country's national desired target population excluded some section of the population, based on geographic or linguistic constraints. For example, Lithuania's national desired target population included only students in Lithuanian-speaking schools, representing approximately 93 percent of the international desired population of students in the country. PIRLS participants were expected to ensure that the national defined population included at least 95 percent of the national desired population of students. Exclusions (which had to be kept to a minimum) could occur at the school level, within the sampled schools, or both. Although countries were expected to do everything possible to maximize coverage of the national desired population, school-level exclusions sometimes were necessary. Keeping within the 95 percent limit, school-level exclusions could include schools that:

• were geographically remote,
• had very few students,
• had a curriculum or structure different from the mainstream education system, or
• were specifically for students with special needs.

The difference between these school-level exclusions and those at the previous level is that these schools were included as part of the sampling frame (i.e., the list of schools to be sampled). Th ey then were eliminated on an individual basis if it was not feasible to include them in the testing.

In many education systems, students with special educational needs are included in ordinary classes. Due to this fact, another level of exclusions is necessary to reach an eff ective target population-the population of students who ultimately will be tested. These are called within-school exclusions and pertain to students who are unable to be tested for a particular reason but are part of a regular classroom. There are three types of within-school exclusions.

• Intellectually disabled students
• Functionally disabled students
• Non-native language speakers

Students eligible for within-school exclusion were identified by staff at the schools and could still be administered the test if the school did not want the student to feel out of place during the assessment (though the data from these students were not included in any analyses). Again, it was important to ensure that this population was as close to the national desired target population as possible. If combined, school-level and within-school exclusions exceeded 5 percent of the national desired target population, results were annotated in the PIRLS 2006 International Report (Mullis, Martin, Kennedy, & Foy, 2007). Target population coverage and exclusion rates are displayed for each country in Chapter 9 of the PIRLS 2006 Technical Report. Descriptions of the countries' school-level and within-school exclusions can be found in Appendix B of the PIRLS 2006 Technical Report.

Kind of data

Sample survey data [ssd]

Sampling procedure

The basic sample design used in PIRLS 2006 is known as a two-stage stratified cluster design, with the first stage consisting of a sample of schools, and the second stage consisting of a sample of intact classrooms from the target grade in the sampled schools. While all participants adopted this basic two-stage design, four countries, with approval from the PIRLS sampling consultants, added an extra sampling stage. The Russian Federation and the United States introduced a preliminary sampling stage, (first sampling regions in the case of the Russian Federation and primary sampling units consisting of metropolitan areas and counties in the case of the United States). Morocco and Singapore also added a third sampling stage; in these cases, sub-sampling students within classrooms rather than selecting intact classes.

For countries participating in PIRLS 2006, school stratification was used to enhance the precision of the survey results. Many participants employed explicit stratification, where the complete school sampling frame was divided into smaller sampling frames according to some criterion, such as region, to ensurea predetermined number of schools sampled for each stratum. For example, Austria divided its sampling frame into nine regions to ensure proportional representation by region (see Appendix B for stratification information for each country). Stratification also could be done implicitly, a procedure by which schools in a sampling frame were sorted according to a set of stratification variables prior to sampling. For example, Austria employed implicit stratification by district and school size within each regional stratum. Regardless of the other stratification variables used, all countries used implicit stratification by a measure of size (MOS) of the school.

All countries used a systematic (random start, fixed interval) probability proportional-to-size (PPS) sampling approach to sample schools. Note that when this method is combined with an implicit stratification procedure, the allocation of schools in the sample is proportional to the size of the implicit strata. Within the sampled schools, classes were sampled using a systematic random method in all countries except Morocco and Singapore, where classes were sampled with probability proportional to size, and students within classes sampled with equal probability. The PIRLS 2006 sample designs were implemented in an acceptable manner by all participants.

Sampling deviation

8 National Research Coordinators (NRCs) encountered organizational constraints in their systems that necessitated deviations from the sample design. In each case, the Statistics Canada sampling expert was consulted to ensure that the altered design remained compatible with the PIRLS standards.

These country specific deviations from sample design are detailed in Appendix B of the PIRLS 2006 Technical Report (page 231) attached as Related Material.

Mode of data collection

Face-to-face [f2f]

Research instrument

• PIRLS Background Questionnaires By gathering information about children’s experiences together with reading achievement on the PIRLS test, it is possible to identify the factors or combinations of factors that relate to high reading literacy. An important part of the PIRLS design is a set of questionnaires targeting factors related to reading literacy. PIRLS administered four questionnaires: to the tested students, to their parents, to their reading teachers, and to their school principals.

• Student Questionnaire Each student taking the PIRLS reading assessment completes the student questionnaire. The questionnaire asks about aspects of students’ home and school experiences - including instructional experiences and reading for homework, self-perceptions and attitudes towards reading, out-of-school reading habits, computer use, home literacy resources, and basic demographic information.

• Learning to Read (Home) Survey The learning to read survey is completed by the parents or primary caregivers of each student taking the PIRLS reading assessment. It addresses child-parent literacy interactions, home literacy resources, parents’ reading habits and attitudes, homeschool connections, and basic demographic and socioeconomic indicators.

• Teacher Questionnaire The reading teacher of each fourth-grade class sampled for PIRLS completes a questionnaire designed to gather information about classroom contexts for developing reading literacy. This questionnaire

This dataset contains the total annual FTE and unduplicated headcount enrollment for undergraduate and graduate students at public community colleges and state universities in Massachusetts since 2014.

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:

Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

Related datasets: Public Postsecondary Fall Enrollment 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.

The median age of a given population is the age separating the group into two halves of equal size. In the case of this indicator it means that half of the student population, i.e. persons enrolled in tertiary education (ISCED levels 5 and 6), is younger than the median age and the other half is older.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.
The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor’s average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.

Data Description

Private A factor with levels No and Yes indicating private or public university * Apps Number of applications received * Accept Number of applications accepted * Enroll Number of new students enrolled * Top10perc Pct. new students from top 10% of H.S. class * Top25perc Pct. new students from top 25% of H.S. class * F.Undergrad Number of fulltime undergraduates * P.Undergrad Number of parttime undergraduates * Outstate Out-of-state tuition * Room.Board Room and board costs * Books Estimated book costs * Personal Estimated personal spending * PhD Pct. of faculty with Ph.D.’s * Terminal Pct. of faculty with terminal degree * S.F.Ratio Student/faculty ratio * perc.alumni Pct. alumni who donate * Expend Instructional expenditure per student * Grad.Rate Graduation rate

You can Use it for clustering projects

Description: This dataset supports a research study evaluating the challenges faced by undergraduate nursing students in Iraq regarding online nursing education. The study was conducted from September 26, 2020, to April 10, 2021, across eight nursing colleges in Iraq. It investigates various challenges related to learning, technology, instructor competency, communication, course design, and psychosocial factors. Data Collection: The dataset was collected through a self-reported questionnaire distributed to 320 undergraduate nursing students from eight universities. Data was gathered through Google Forms and physical questionnaires between January 8, 2021, and February 27, 2021. The study instrument consists of demographic data and six domains assessing online education challenges using a three-point Likert scale. Data Contents: Demographic Information: Includes students' age, gender, educational status, family income, residency, and internet access. Online Nursing Education Challenges: Covers six domains: Learning, understanding, and comprehension Software and e-learning tools Instructors' skills and experience Class discussions and student-teacher communication Course design and content Psycho-social circumstances Analysis Methods: The dataset was analyzed using descriptive statistics (frequency, percentage, mean scores) and inferential statistical methods, including factor analysis, ANOVA, and multiple linear regression, to assess relationships between challenges and student demographics. Ethical Considerations: Approval for the study was obtained from the Scientific Research Ethical Committee at the University of Baghdad. All participants provided informed consent before participation. Limitations: The findings are limited to the study sample and may not be generalizable. Additionally, there is a lack of comparable national and international research on this topic.

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

BackgroundNurses and researchers emphasize the importance of adding educational content of palliative care to nursing curricula in Iran as a means to improve the quality of care at the end of life and self-efficacy is considered as an important determinant in palliative care nursing. However, undergraduate nursing students are not sufficiently trained to achieve the qualifications required in palliative care. The aim of this study was to determine the effect of combined training (theoretical-practical) of palliative care on the perceived self-efficacy of nursing students.MethodsThis is a semi-experimental study with a pretest-posttest design. Sampling was nonrandomized with convenience method and included 23 seventh-semester students. The intervention consisted of palliative care training for ten theoretical sessions and three practical sessions. Data were collected using demographic and the perceived self-efficacy questionnaires completed before and after the intervention. Data were then analyzed in the statistical SPSS 23 software using descriptive and analytical statistics.ResultsThe mean age of the samples was 22.78 (SD1.17). Most of the participants were male (56.5%) and single(91.3%). The findings showed that, perceived self-efficacy, psycho-social support and symptom management improved significantly after the intervention (p

According to a study conducted in 2025, the greatest factor influencing students' college choice in the United States was the number of majors offered by the school. Campus safety was also a significant factor, receiving a student-attractor score of ***, meaning that the most safe schools will attract *** percent more students than those with the least.