The data includes: * academic year * region * board number * board name * board language * board type * elementary school male educators * elementary school female educators * secondary school male educators * secondary school female educators * total male educators * total female educators * total educators This dataset is reported during October submissions by schools and school boards in the Ontario School Information System (OnSIS). Educators include teachers, administrators, early childhood educators, long- term occasional (LTO) teachers and LTO early childhood educators. LTO educator data is based on data reported by school boards. The definition of an LTO teacher or LTO early childhood educator may vary across school boards based on local collective agreements in place. The data does not include personnel on leave, as well as educators at: * private schools * hospital and provincial schools * care and/or treatment, custody and correctional facilities * summer, night and adult continuing education day schools Small cells have been suppressed. Where fewer than 10 educators are in a given category, the data is shown with (< 10). Suppressed totals are shown with (SP). The report may not be used in any way that could lead to the identification of an individual. *[LTO]: long-term occasional

This dataset contains the processed survey data for the paper 'Teachers' Intention to Integrate Computational Thinking (CT) Skills in Higher Education - A Survey Study in the Netherlands'. The research adopts a survey to measure teachers' understanding of CT skills and intention to integrate CT skills in higher education. The data collected includes: demographics of the participants, participants' understandings of CT skills, and their intention to integrate CT skills in their teaching practices. These data includes both categorical and numerical answers and open-text answers for multiple choice questions, open-ended questions, likert-scale choices.

Citywide Class Size Report, including Region, District, School, Program, Grade or Service Category, Average Class Size, and Pupil / Teacher Ratio (PTR) SOURCES: 10/31/06 Official Register (K-9) and 12/15/06 Register/Schedule (9-12) Grade 9 not in high schools Indicates how special class is delivered For schools with students in any grades between Kindergarten and 9th grade (where 9th grade is the termination grade for the school), class size is reported by four program areas: general education, special education self-contained class, collaborative team teaching and gifted and talented self-contained class. Within each program area class size is reported by grade or service category, which indicates how a special education self-contained class is delivered. Class size is calculated by dividing the number of students in a program and grade by the number of official classes in that program and grade. The following data is excluded from all the reports: District 75 schools, bridge classes which span more than one grade, classes with fewer than five students (for other than special education self-contained classes) and classes with one student (for special education self-contained classes). On the summary reports programs and grades with three or fewer classes are excluded from the citywide, borough and region reports and programs and grades with one class are excluded from the district report. For schools with students in any grades between 9th and 12th grade (where 9th grade is not the termination grade for the school), class size is reported by two program areas: general education and special education. For general education students class size is reported by grade for each core subject area: English, Math, Science and Social Studies. For special education students with a self-contained program recommendation, class size is reported by service category (self-contained or mainstream) for each core subject area. Since high school classes may contain students in multiple grades and programs, class size is calculated by taking a weighted average of all the classes in a core subject area with students in a particular grade or program. For example, there are 75 ninth graders enrolled at a high school. 25 ninth graders attend a Math class with 28 students, a second group of 25 ninth graders attend a Math class with 25 students, and a third group of 25 ninth graders attend a Math class with 30 students. Average class size for ninth grade Math equals: (25x28 + 25x25 + 25x30)/75 = 27.7. The Pupil Teacher Ratio is also provided on the school level report. Pupil Teacher Ratio is another means to evaluate the instructional resources provided at a school. Pupil Teacher Ratio for All Students is calculated by dividing the number of students at a school by the number of full-time equivalent teachers, including both teachers in classes taught by two teachers, “cluster” teachers providing instruction in specialized topics like art or science, and teachers providing special education instruction. Pupil Teacher Ratio Excluding Special Education is calculated by dividing the number of non-special education students at a school by the number of full-time equivalent non-special education teachers.

The Schools and Staffing Survey, 2003-04 (SASS 03-04), is a study that is part of the Schools and Staffing Survey (SASS) program. SASS 03-04 (https://nces.ed.gov/surveys/sass) is a survey that covers a wide range of topics from teacher demand, teacher and principal characteristics, general conditions in schools, principals' and teachers' perceptions of school climate and problems in their schools, teacher compensation, district hiring and retention practices, to basic characteristics of the student population. The survey was conducted using mail, email, paper questionnaires, and telephone interviews. Teachers, librarians, principals, and school coordinators were sampled. Key statistics produced from SASS 03-04 are how many teachers and principals remained at the same school, moved to another school, or left the profession in the year following the SASS administration.

Computerized questionnaire through the Google Forms private server

This dataset presents data on the number of schools, classes, students, and teachers in public schools across Qatar, categorized by the level of education and type of school (Boys, Girls, or Mixed). It includes information on the number of teachers, students, classes, and schools for each educational level (Pre-primary, Primary, Preparatory). The data allows for an analysis of the educational landscape, showing how resources like teachers and classes are distributed among various school types and educational stages. This dataset is valuable for policymakers, researchers, and planners to understand trends in educational growth and resource allocation.

This dataset can be used for signature verification and forgery detection tasks using machine learning or computer vision algorithms.

The dataset likely includes multiple samples of each teacher's signature, both real and forged, in order to capture variations in their signing style. These variations could be due to factors such as speed, pressure, and angle of the pen, as well as the level of familiarity with the signature.

The real signatures in the dataset can be used as a reference to train a signature verification system, which would compare an input signature to the known reference signature to determine its authenticity. The forged signatures, on the other hand, can be used to train a forgery detection system, which would detect signatures that do not match the known reference signatures.

It is important to note that the use of this dataset should be limited to legal and ethical applications, such as detecting fraud or verifying the authenticity of legal documents. The privacy and security of the teachers whose signatures are included in the dataset should also be carefully considered, and steps should be taken to protect their identities and personal information.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

The 1991-92 Teacher Follow-Up Survey (TFS 91-92) is a longitudinal follow-up to the 1990-91 Schools and Staffing Survey (SASS 90-91). TFS 91-92 (https://nces.ed.gov/surveys/sass/index.asp) is used to determine how many teachers remained at the same school, moved to another school, or left the profession in the year following the Schools and Staffing Survey (SASS) administration. TFS 91-92 was administered to a sample of teachers who completed the SASS in the previous year. Key statistics found from 1991-92 TFS are the percentage of teachers who remained at the same school, the percentage of teachers who moved to another school, or the percentage of teachers who left the profession in the year following the 1990-91 Schools and Staffing Survey (SASS) administration.

Analysis of ‘2006-07 Class Size - By District’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c1249587-e6ac-4c9e-9677-44ed07ccbe04 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Citywide Class Size Report, including District, Program, Grade or Service Category.

SOURCES: 10/31/06 Official Register (K-9) and 12/15/06 Register/Schedule (9-12)

Grade 9 not in high schools

Indicates how special class is delivered

For schools with students in any grades between Kindergarten and 9th grade (where 9th grade is the termination grade for the school), class size is reported by four program areas: general education, special education self-contained class, collaborative team teaching and gifted and talented self-contained class. Within each program area class size is reported by grade or service category, which indicates how a special education self-contained class is delivered. Class size is calculated by dividing the number of students in a program and grade by the number of official classes in that program and grade.

The following data is excluded from all the reports: District 75 schools, bridge classes which span more than one grade, classes with fewer than five students (for other than special education self-contained classes) and classes with one student (for special education self-contained classes). On the summary reports programs and grades with three or fewer classes are excluded from the citywide, borough and region reports and programs and grades with one class are excluded from the district report. For schools with students in any grades between 9th and 12th grade (where 9th grade is not the termination grade for the school), class size is reported by two program areas: general education and special education. For general education students class size is reported by grade for each core subject area: English, Math, Science and Social Studies. For special education students with a self-contained program recommendation, class size is reported by service category (self-contained or mainstream) for each core subject area. Since high school classes may contain students in multiple grades and programs, class size is calculated by taking a weighted average of all the classes in a core subject area with students in a particular grade or program. For example, there are 75 ninth graders enrolled at a high school. 25 ninth graders attend a Math class with 28 students, a second group of 25 ninth graders attend a Math class with 25 students, and a third group of 25 ninth graders attend a Math class with 30 students. Average class size for ninth grade Math equals: (25x28 + 25x25 + 25x30)/75 = 27.7.

The Pupil Teacher Ratio is also provided on the school level report. Pupil Teacher Ratio is another means to evaluate the instructional resources provided at a school. Pupil Teacher Ratio for All Students is calculated by dividing the number of students at a school by the number of full-time equivalent teachers, including both teachers in classes taught by two teachers, “cluster” teachers providing instruction in specialized topics like art or science, and teachers providing special education instruction. Pupil Teacher Ratio Excluding Special Education is calculated by dividing the number of non-special education students at a school by the number of full-time equivalent non-special education teachers.

--- Original source retains full ownership of the source dataset ---

Due to the COVID-19 pandemic, testing what is required to support teachers and students while subject to forced online teaching and learning is relevant in terms of similar situations in the future. To understand the complex relationships of numerous factors with teaching during the lockdown, we used administrative data and survey data from a large Danish university. The analysis employed scores from student evaluations of teaching and the students’ final grades during the first wave of the COVID-19 lockdown in the spring of 2020 as dependent targets in a linear regression model and a random forest model. This led to the identification of linear and non-linear relationships, as well as feature importance and interactions for the two targets. In particular, we found that many factors, such as the age of teachers and their time use, were associated with the scores in student evaluations of teaching and student grades, and that other features, including peer interaction among teachers and student gender, also exerted influence, especially on grades. Finally, we found that for non-linear features, in terms of the age of teachers and students, the average values led to the highest response values for scores in student evaluations of teaching and grades.

summarizes state- and district-level data on the numbers of full-time equivalent (FTE) highly qualified teachers who were enrolled in alternative route programs for three groups of teachers—(1) all teachers, (2) special education teachers, and (3) teachers in language instruction educational programs for English learners (ELs) under Title III of the Elementary and Secondary Education Act of 1965 (ESEA)—as well as for teachers in high-poverty and rural school districts.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

This dataset tracks annual total classroom teachers amount from 1991 to 2023 for May High School

Annual survey of newly qualified teachers' perceptions of the quality of their training and how well it prepared them for their first teaching post. Each new cohort of newly qualified teachers is surveyed six months after completion of their initial teacher training. Data are published at sector and provider level (where the number of returns exceeds eleven).

This dataset captures parent-teacher interactions over a six-month period from popular communication platforms like ClassDojo and Bloomz. It includes approximately 67,800 message threads exchanged between parents and teachers. Each thread reflects real-world educational communication, enabling in-depth analysis of engagement behaviors. The data reflects natural language usage in formal and informal contexts across various school settings. It supports analysis of how timing, tone, and interaction patterns impact engagement. The dataset is especially valuable for studies involving sentiment, responsiveness, and communication effectiveness in educational ecosystems.

This dataset categorises the 345 teaching and learning proposals collected in various teacher trainings on STEM education between 2017 and 2021. The training participants are active secondary school teachers (training A-C) from different STEM domains ( Mathematics, Science and Technology) and secondary school teachers in initial training (see training D-F). All participating teachers had no prior experience in STEM education, which was the reason they enrolled in this training program. The teaching and learning sequences have not been implemented in the classroom, but are the product of the training. These data have been used to study how teachers conceptualize STEM education and which elements they prioritize. The codes that categorize each item are described in the rubric and can be consulted at: https://doi.org/10.1007/s10763-024-10457-3.

The folder contains datasets from a teacher survey of primary school grade 4. The data was collected in Vietnam in 2021.

The project “Get involved! Learning in Primary School” investigated the role of parents and teachers in shaping children’s academic achievement, motivation, and behaviour throughout primary school. The study aimed to identify both direct and indirect longitudinal mechanisms, as well as the joint effects of home and school learning environments, in promoting optimal learning outcomes in children. Particular attention was paid to instructional and emotional support, as well as potential evocative effects between children and their learning environments. Data collection took place in four waves: a) Grade 2, autumn 2021 (T1) b) Grade 2, spring 2022 (T2) c) Grade 3, spring 2023 (T3) d) Grade 4, spring 2024 (T4) The longitudinal study was conducted in Lithuania and involved children, parents/guardians and teachers. The data collection included a combination of child assessments, parent and teacher questionnaires, as well as annual grades and national test scores obtained from school administrations. The following datasets were gathered covering multiple time points (T1–T4): a) Child Assessment Data: This dataset included small group assessments of children’s vocabulary, reading fluency, reading comprehension, listening comprehension, spelling, arithmetic, interest in learning, academic self-concept, task persistence and so on. In addition, national test scores in reading and mathematics were obtained for Grade 4, while annual grades in these subjects were collected for Grades 1 through 4. The dataset also incorporated psychologist observations of the child's behavior during testing sessions, including task persistence, student behavior during tasks, student well-being during testing and so on. b) Parent/Guardian Questionnaire Data: This dataset was collected from parents or guardians. It captures information on parental beliefs and expectations regarding academic performance, involvement in homework, autonomy support during homework, emotional responses to homework, the number of books at home, reading habits, parenting styles, the quality of the parent–child relationship, and sociodemographic background and so on. c) Teacher General Questionnaire Data: Teachers completed a questionnaire about themselves and their class. This questionnaire provided data on teaching interaction styles, the frequency of instructional practices related to literacy and math skills development, teacher self-efficacy, homework practices, well-being and stress, as well as both extrinsic and intrinsic motivation related to teaching and so on. d) Teacher Individual Questionnaire Data: Teachers filled out another questionnaire about each participating student. This dataset included information on student task persistence, the teacher–child relationship, the frequency and quality of individual help and control during classwork, student emotions in learning situations, personalized support and attention from the teacher, teachers’ beliefs and expectations about each student’s learning, and their perception of each student’s current interest in learning and so on. The number of participants was as follows: a) At T1 (Grade 2, autumn 2021): 522 children, 536 parents/guardians, 34 teachers, and 9 psychologists. b) At T2 (Grade 2, spring 2022): 585 children, 610 parents/guardians, 40 teachers, and 11 psychologists. c) At T3 (Grade 3, spring 2023): 582 children, 575 parents/guardians, 39 teachers, and 11 psychologists. d) At T4 (Grade 4, spring 2024): 576 children, 565 parents/guardians, 39 teachers, and 11 psychologists. The project was funded by the Research Council of Finland and conducted in cooperation with Vilnius University and Lithuanian schools. The study provides a comprehensive longitudinal view of how interactions between home and school contexts contribute to children’s academic development during primary school. More information about participants, data collection methods and variables can be found in “Get Involved! Learning in Primary School” codebook (published separately).

TEDS-M examined how different countries prepare their teachers to teach mathematics in primary and lower-secondary schools. The study gathered information on various characteristics of teacher education institutions, programs, and curricula. It also collected information on the opportunities to learn within these contexts, and on future teachers’ knowledge and beliefs about mathematics and learning mathematics. TEDS-M Educational measurements and tests Target population: Teachers of Mathematics TEDS-M surveyed teacher education institutions, educators of future teachers, and future teachers (primary and secondary levels). STRATIFIED TWO-STAGE CLUSTER SAMPLE DESIGN