Historical Dataset of Native American Magnet is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (2013-2023),Asian Student Percentage Comparison Over Years (1990-2023),Hispanic Student Percentage Comparison Over Years (1993-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1994-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2013),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023)

Historical Dataset of Native American Community Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2007-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),American Indian Student Percentage Comparison Over Years (2007-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2007-2023),White Student Percentage Comparison Over Years (2008-2022),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2007-2018),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2011-2023),Overall School Rank Trends Over Years (2011-2023),Graduation Rate Comparison Over Years (2012-2021)

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

Enrollment is a head count of all students receiving their primary PK-12 educational services through Wisconsin public schools. This map is in a series of maps that show enrollments by district for a particular student group (demographic) for the 2023-2024 school year. Additional enrollment data are available for the public to view on the WISEdash Public Portal. Enrollment data is sourced from the WISEdata system. 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. DPI collects data to meet all required school, district, state, and federal reporting mandates, e.g., Every Student Succeeds Act (ESSA), Individuals with Disabilities Education Act (IDEA), and Title II Higher Education Act. These data inform education research and data analysis. Multiple teams from IT and content areas work together at DPI to build tools for data collection, to support districts in data collection, and to report on and facilitate the use of data based on federal and state reporting mandates. Through the DPI dashboard and reporting tools, DPI staff, teachers, administrators, parents, and researchers are better able to understand and improve educational outcomes for Wisconsin students.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.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

Abstract (en): Since 1982, the World Health Organization (WHO) Regional Office for Europe has sponsored a cross-national, school-based study of health-related attitudes and behaviors of young people. These studies, generally known as Health Behavior in School-Aged Children (HBSC), are based on independent national surveys of school-aged children in as many as 30 participating countries. The HBSC studies were conducted every four years since the 1985-1986 school year. The data available here are from the results of the United States survey conducted during the 2001-2002 school year. The study results can be used as stand-alone data, or to compare with the other countries involved in the international HBSC. The HBSC study has two main objectives. The first objective is to monitor health-risk behaviors and attitudes in youth over time to provide background data and to identify targets for health promotion initiatives. The second objective is to provide researchers with relevant information in order to understand and explain the development of health attitudes and behaviors through early adolescence. The study contains variables dealing with many types of drugs such as tobacco, alcohol, marijuana, inhalants, and any other substances. The study examines the first time these substances were used and the frequency of their use. Other topics include questions about the person's health and other health behaviors. Some of these topics include eating habits, body image, health problems, family make-up, personal injuries, bullying, fighting, and bringing weapons to school. A school administrator and the lead health education teacher also completed individual surveys concerning school programs and policies that affect students' health and the content of various health courses. The data file contains weights. Each valid respondent record was weighted by the inverse of the probability of having selected the respondent's school and classroom, and adjusted for school nonresponse and student nonresponse within classrooms. The weights were then trimmed and adjusted to national totals by ethnicity and grade level. A hotdeck approach was used to impute missing values (for weighting purposes only) for race and ethnicity, classifying the students into five categories: White, African-American, Hispanic, Asian and Native American. Totals were obtained for each race and grade level from the National Center for Educational Statistics' Web site. The weights were then adjusted so that totals for each race/grade category corresponded to national totals. The name of the weight variable in the dataset is STU_WT. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: Of the 548 schools selected, 204 schools did not respond. Extra schools were selected into the original sampling frame yielding a balance of 465 schools. From these 465 schools, 340 agreed to participate, yielding a participation rate of 73.2 percent. Within these schools, 18,620 students were eligible and 15,245 participated, yielding a student response rate of 81.9 percent. Of the 3,375 students who did not participate, 600 students did not return a consent form, 518 parents declined to allow their child to participate, and 1,620 students declined to participate. From the 340 schools that participated, a school administrator and lead health education teachers were asked to complete a survey. There were 329 questionnaires returned by a school administrator and 320 questionnaires returned by the lead health education teacher. A total of 317 schools returned both the administrator and teacher surveys. The universe consisted of public, Catholic, and other private school students in grades 6, 7, 8, 9, and 10 or their equivalent in the 50 states and the District of Columbia. Very small schools, those with an enrollment of less than 14, were excluded from the universe. These schools comprise about ...

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

AI Slide Deck Creators for Teachers Market Outlook

According to our latest research, the Global AI Slide Deck Creators for Teachers market size was valued at $420 million in 2024 and is projected to reach $2.35 billion by 2033, expanding at a robust CAGR of 20.8% during 2024–2033. The primary factor driving this remarkable growth is the accelerating integration of artificial intelligence in educational content creation, enabling teachers to automate the design of engaging, personalized, and visually appealing presentations. This technological advancement not only saves educators significant time but also enhances the learning experience by facilitating the creation of dynamic and interactive teaching materials tailored to diverse student needs.

Regional Outlook

North America currently commands the largest share of the AI Slide Deck Creators for Teachers market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature educational technology landscape, widespread adoption of digital teaching tools, and strong governmental support for EdTech initiatives. The presence of leading AI software developers and a high concentration of technology-forward educational institutions further reinforce North America’s market leadership. Additionally, robust investments in teacher training and infrastructure, combined with favorable policies supporting digital transformation in schools and universities, have fueled early adoption and consistent market expansion across the United States and Canada.

The Asia Pacific region is poised to be the fastest-growing market, projected to exhibit a CAGR exceeding 25% from 2025 to 2033. This growth trajectory is underpinned by surging investments in educational modernization, particularly in countries like China, India, South Korea, and Japan. Government initiatives to digitize classrooms, bridge educational gaps, and upskill teachers are accelerating the deployment of AI-driven teaching tools. Furthermore, the region’s burgeoning population of digitally native students and the rapid proliferation of affordable cloud-based solutions are catalyzing market uptake. The increasing participation of local EdTech startups and global technology providers is also fostering innovation and competitive pricing, making AI slide deck solutions more accessible to a broader range of institutions.

Emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual, yet promising, rise in the adoption of AI Slide Deck Creators for Teachers. However, these regions face unique challenges such as limited digital infrastructure, budgetary constraints, and a shortage of skilled IT professionals in the education sector. Despite these hurdles, localized demand is growing, particularly in urban centers where educational reform and digital inclusion policies are being prioritized. International aid programs and public-private partnerships are playing a pivotal role in overcoming adoption barriers, with a focus on enhancing teacher capabilities and expanding access to AI-powered teaching tools in underserved communities.

Report Scope

Historical Dataset of American Samoa is provided by PublicSchoolReview and contain statistics on metrics:Trends in the Average Number of Students Per Public School,Trends in the Average Number of Teachers Per Public School,Student-Teacher Ratio Trends (1988-1998),Asian Student Percentage Trends,Native Hawaiian or Pacific Islander Student Percentage Trends,Diversity Score Trends,Free Lunch Eligibility Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends

Historical Dataset of American Lakes is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1996-2023),Total Classroom Teachers Trends Over Years (1995-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1995-2023),American Indian Student Percentage Comparison Over Years (1996-2023),Asian Student Percentage Comparison Over Years (1996-2023),Hispanic Student Percentage Comparison Over Years (1996-2023),Black Student Percentage Comparison Over Years (1996-2023),White Student Percentage Comparison Over Years (1996-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2009-2015),Two or More Races Student Percentage Comparison Over Years (2009-2023),Diversity Score Comparison Over Years (1996-2023),Free Lunch Eligibility Comparison Over Years (1995-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Overall School Rank Trends Over Years (2012-2023)

Historical Dataset of Onamia High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1990-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1991-2023),American Indian Student Percentage Comparison Over Years (1992-2023),Asian Student Percentage Comparison Over Years (1990-2023),Hispanic Student Percentage Comparison Over Years (1997-2023),Black Student Percentage Comparison Over Years (1988-2023),White Student Percentage Comparison Over Years (1992-2023),Two or More Races Student Percentage Comparison Over Years (2015-2023),Diversity Score Comparison Over Years (1992-2023),Free Lunch Eligibility Comparison Over Years (1992-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

Historical Dataset of Hamilton Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1989-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2009-2014),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1992-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2000-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Overall School Rank Trends Over Years (2012-2023)

Historical Dataset of Pacific American Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2013-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2013-2023),American Indian Student Percentage Comparison Over Years (2012-2013),Asian Student Percentage Comparison Over Years (2013-2023),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2013-2023),White Student Percentage Comparison Over Years (2013-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2011-2015),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2013-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2013),Math Proficiency Comparison Over Years (2011-2013),Overall School Rank Trends Over Years (2011-2013)

Historical Dataset of American High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1988-2000),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2009-2014),Two or More Races Student Percentage Comparison Over Years (2010-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1992-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

Historical Dataset of Colstrip High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),American Indian Student Percentage Comparison Over Years (1993-2023),Asian Student Percentage Comparison Over Years (2009-2023),Hispanic Student Percentage Comparison Over Years (1999-2023),Black Student Percentage Comparison Over Years (1993-2004),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (1999-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2010-2023),Graduation Rate Comparison Over Years (2013-2023)

Historical Dataset of American Fork High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1991-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1988-2008),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1992-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2021),Math Proficiency Comparison Over Years (2011-2023),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2012-2023)

Historical Dataset of Earl Wooster High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1991-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1991-2023),American Indian Student Percentage Comparison Over Years (1992-2023),Asian Student Percentage Comparison Over Years (1992-2023),Hispanic Student Percentage Comparison Over Years (1992-2023),Black Student Percentage Comparison Over Years (1992-2023),White Student Percentage Comparison Over Years (1992-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1992-2023),Free Lunch Eligibility Comparison Over Years (1990-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2000-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2011-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

Historical Dataset of American Fork Jr High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1991-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),American Indian Student Percentage Comparison Over Years (1988-2021),Asian Student Percentage Comparison Over Years (1989-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (2004-2023),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1991-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2001-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2021),Math Proficiency Comparison Over Years (2011-2023),Overall School Rank Trends Over Years (2012-2023)