Student Classroom Activity

## Overview

Student Classroom Activity is a dataset for object detection tasks - it contains Student annotations for 779 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Abstract

In Brazil, policy makers in the state of Ceara are looking at how providing information to schools about best teaching practices, as well as offering peer learning opportunities, can help boost the performance of less effective teachers. The state government wants to stimulate more interaction among teachers at the school level that will lead to faster and cheaper diffusion of good practices within schools. The Secretariat requested World Bank assistance with the design and implementation of the random assignment experiment during the 2015 school year to measure cost-effectiveness of this approach.

The endline data collection was carried out in October - November 2015. Researchers used "Stallings Classroom Snapshot" instrument to gather information about teachers' use of time, materials and interactive pedagogical practices. The observations were made in 3,121 classrooms of 10th, 11th and 12th grade in randomly selected 300 schools.

The schools were randomly assigned to a treatment group (156 schools) or a control group (136 schools). The treatment was launched in March 2015. The treatment group received detailed feedback on the school's results from the classroom observations, information on teacher performance, self-help materials that included a book, videos and exercises about effective teaching strategies, and a log book and classroom observation templates to record teachers' observations of one other. The control schools received neither feedback nor information. After the treatment, researchers assessed the results of student test scores to determine whether the campaign helped improve classroom learning.

The baseline survey was conducted in November 2014.

Geographic coverage

The state of Ceará

Analysis unit

Classrooms; Schools

Kind of data

Observation data/ratings [obs]

Sampling procedure

Ceará has 573 secondary schools that offer the complete three-year cycle. Of these, a sample of 400 schools was stratified by size, geographic area and quartile of learning results. Researchers randomly assigned the 400 schools into 4 groups, with the first 175 assigned to the treatment group, a second group of 25 assigned to a no-observation group, the next 175 schools assigned to the control group, and the last 25 schools also assigned to the no-observation group.

A late start to the baseline round of classroom observations and a limited budget led to a reduction in the sample to 350 schools (175 treatments and 175 controls), which were selected through simple randomization to keep the sample balance. The team did not observe any classroom in the group of 50 schools that were randomly assigned to a no-observation group of the study, but was able to analyze the students' assessments results afterwards.

Sampling deviation

Out of the 350 schools of the randomization, with 175 each planned for treatment and controls, 292 schools were observed in November 2014 and in November 2015. The full initial sample could not be observed due to disruptions in the school calendar in November 2014 (standardized tests and holidays) and a shortage of observers in the Fortaleza district. The 292-school final sample included 156 schools in the treatment group and 136 in the control group. This difference in the attrition of treatment and control schools was due to the data collection firm focusing their efforts on making up for the schools of the treatment group that would benefit from the classroom observation and the intervention. As a result, because the loss of schools from the treatment and control groups was uneven, the research team conducted a series of balance checks to test the randomization.

In the treatment sample, the 19 schools that were not observed could not receive the information treatment (benchmarked classroom observation feedback for the teachers in their school). But these schools were given access to the other three components of the program – self-help materials, face-to-face training and coaching, and were observed again at endline. The same schools were observed to obtain the endline data. Matched repeat observations were made in 2,399 classrooms, 75% of those observed at baseline. Variations in the school calendar and logistical issues resulted in 25% of the 2015 observations being conducted in grades and subjects in the school that had not been observed at baseline.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) The Stallings Classroom Snapshot Coding Sheet: The classroom snapshot records the participants, their activities, and the materials being used in the classroom, at ten separate instances throughout a class period.

2) School Principals Questionnaire: The questionnaire gathers information about teachers' engagement in the school, level of teacher training activities, and Principals' and supervisors' assessments of the value and impact of the treatment.

3) Classroom Demographic Sheet: The instrument is used for identification of the school and classroom, and the basic information related to the observed classroom, such as the number of student and the start time of the class.

Classroom Management Software Market size was valued at USD 100 Million in 2024 and is projected to reach USD 304.2 Million by 2031, growing at a CAGR of 15% during the forecast period 2024-2031.

Global Classroom Management Software Market Drivers

Increasing Adoption of E-Learning: The growing trend towards e-learning and digital education platforms significantly boosts the demand for classroom management software to enhance online learning experiences and classroom interactions.

Technological Advancements: Innovations in educational technology, including AI, machine learning, and cloud computing, provide advanced features and functionalities in classroom management software, attracting educational institutions.

Remote and Hybrid Learning Models: The rise of remote and hybrid learning models, especially post-pandemic, necessitates efficient classroom management tools to facilitate seamless communication, collaboration, and administration of virtual classrooms.

Demand for Real-Time Monitoring and Assessment: The need for real-time monitoring and assessment of student performance and engagement drives the adoption of classroom management software that offers analytics and reporting capabilities.

Enhanced Student Engagement: Features such as interactive content, gamification, and personalized learning paths in classroom management software help in increasing student engagement and motivation.

Streamlined Administrative Tasks: The software helps educators streamline administrative tasks like attendance tracking, grading, and scheduling, allowing teachers to focus more on instruction and student support.

Government Initiatives and Funding: Government initiatives and funding aimed at digitizing education and enhancing the quality of education through technology adoption promote the use of classroom management software in schools and universities.

Improved Communication Tools: The integration of communication tools within classroom management software facilitates better interaction between teachers, students, and parents, contributing to a more collaborative learning environment.

Security and Compliance: Enhanced security features and compliance with educational standards and regulations ensure the safe and responsible use of digital tools in the classroom, encouraging adoption.

Scalability and Flexibility: The scalable and flexible nature of classroom management software allows educational institutions to customize and expand their use of the software according to their specific needs and size, from small classrooms to large universities.

This includes all the raw data that supports the study on the Classroom learning experience, environment, and engagement of high school students in the conduct of their pure online class during the pandemic.

Many undergraduate students encounter struggle as they navigate academic, financial, and social contexts of higher education. The transition to emergency online instruction during the Spring of 2020 due to the COVID-19 pandemic exacerbated these struggles. To assess college students’ struggles during the transition to online learning in undergraduate biology courses, we surveyed a diverse collection of students (n = 238) at an R2 research institution in the Southeastern United States. Students were asked if they encountered struggles and whether they were able to overcome them. Based on how students responded, they were asked to elaborate on (1) how they persevered without struggle, (2) how they were able to overcome their struggles, or (3) what barriers they encountered that did not allow them to overcome their struggles. Each open-ended response was thematically coded to address salient patterns in students’ ability to either persevere or overcome their struggle. We found that during the transition to remote learning, 67% of students experienced struggle. The most reported struggles included: shifts in class format, effective study habits, time management, and increased external commitments. Approximately, 83% of those struggling students were able to overcome their struggle, most often citing their instructor’s support and resources offered during the transition as reasons for their success. Students also cited changes in study habits, and increased confidence or belief that they could excel within the course as ways in which they overcame their struggles. Overall, we found no link between struggles in the classroom and any demographic variables we measured, which included race/ethnicity, gender expression, first-generation college students, transfer student status, and commuter student status. Our results highlight the critical role that instructors play in supporting student learning during these uncertain times by promoting student self-efficacy and positive-growth mindset, providing students with the resources they need to succeed, and creating a supportive and transparent learning environment.

Student Behaviour In Classroom

## Overview

Student Behaviour In Classroom is a dataset for object detection tasks - it contains Objects annotations for 876 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is about books. It has 2 rows and is filtered where the book is Assessment in the classroom : constructing and interpreting texts. It features 7 columns including author, publication date, language, and book publisher.

Materials, data, findings, and implications of experimental classroom research

The Ministry of Educations' - Basic Education Statistical Booklet captures national statistics for the Education Sector in totality. This Dataset shows Primary classrooms both private and public across the 47 Counties. It also highlights the average size of the classroom, temporary or permanent classroom Source: Table 61: Primary Classrooms

This dataset tracks annual total classroom teachers amount from 2019 to 2023 for Wildflower Open Classroom

Abstract (en): The National Center for Early Development and Learning (NCEDL) combined the data of two major studies in order to understand variations among state-funded pre-kindergarten (pre-k) programs and in turn, how these variations relate to child outcomes at the end of pre-k and in kindergarten. The Multi-State Study of Pre-Kindergarten and the State-Wide Early Education Programs (SWEEP) Study provide detailed information on pre-kindergarten teachers, children, and classrooms in 11 states. By combining data from both studies, information is available from 721 classrooms and 2,982 pre-kindergarten children in these 11 states. Pre-kindergarten data collection for the Multi-State Study of Pre-Kindergarten took place during the 2001-2002 school year in six states: California, Georgia, Illinois, Kentucky, New York, and Ohio. These states were selected from among states that had committed significant resources to pre-k initiatives. States were selected to maximize diversity with regard to geography, program settings (public school or community setting), program intensity (full-day vs. part-day), and educational requirements for teachers. In each state, a stratified random sample of 40 centers/schools was selected from the list of all the school/centers or programs (both contractors and subcontractors) provided to the researchers by each state's department of education. In total, 238 sites participated in the fall and two additional sites joined the study in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2002, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Pre-kindergarten data collection for the SWEEP Study took place during the 2003-2004 school year in five states: Massachusetts, New Jersey, Texas, Washington, and Wisconsin. These states were selected to complement the states already in the Multi-State Study of Pre-K by including programs with significantly different funding models or modes of service delivery. In each of the five states, 100 randomly selected state-funded pre-kindergarten sites were recruited for participation in the study from a list of all sites provided by the state. In total, 465 sites participated in the fall. Two sites declined to continue participation in the spring, resulting in 463 sites participating in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2004, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Demographic information collected across both studies includes race, teacher gender, child gender, family income, mother's education level, and teacher education level. The researchers also created a variable for both the child-level data and the class-level data which allows secondary users to subset cases according to either the Multi-State or SWEEP study. 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 recodes and/or calculated derived variables.. Response Rates: Multi-State: Of the 40 sites per state, 78 percent of eligible sites agreed to participate (fall of pre-k, n = 238). For fall of pre-k (n = 238), 94 percent of the one classroom per site selected agreed to participate. For fall (n = 940) and spring (n = 960) of pre-k, 61 percent of the parents of eligible children consented.; SWEEP: Of the 10...

Abstract

This study aims to assess the impacts of two types of interventions to be implemented by the Guinea Ministry of Pre-University and Civic Education (MEPU-EC) using performance-based rewards for teacher as motivation-enhancing strategies and providing simple guidance on effective classroom management strategies to encourage their use. In the context of the incentive scheme, teacher performance will be measured using an objective and comparable performance indicator.

The evaluation is designed to answer the following research questions: - What is the effect of performance-based incentives for teachers on teaching practices and behaviors (absenteeism, time on task) and on student learning outcomes? - To what extent do recognition rewards trigger different (better or worse) modifications in teachers’ behavior and practices compared to those triggered by in-king rewards? - What is the effect of providing guidance on effective classroom management on teaching practices and student learning outcomes? - What is the effect of performance-based incentives when teachers are also provided with guidance on effective classroom management practices? Moreover, cost effectiveness of the different treatment arms will be investigated.

The proposed evaluation strategy is a randomized control trial that spans two academic years (2012-2013 and 2013-2014) and targets all grade 3 and 4 teachers of a nationally representative sample of 420 schools. The first year of the impact evaluation focused on assessing the impact of performance-based teacher incentives only and comparing the two types of incentives: in-kind and recognition. The second year includes the additional intervention of delivering guidance on classroom management. Data on schools, teachers, and students is collected through (unannounced) attendance checks, time on task and general classroom observations (carried out in person and using a video), official inspection visits, administration of student’s curriculum-based standardized Math and French tests, teacher surveys and content-knowledge tests, as well student and principal questionnaires. Costing data will be collected through the financial reports of the IDA project and of the government budget (for the second year incentives and guidance intervention) since all expenditures related to the interventions evaluated are paid through these channels.

Baseline data is documented here.

Geographic coverage

National

Analysis unit

• Teachers

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to be representative, at the national level, of the target grades' teacher population in public French-speaking schools.

The sampling process at the schools took place as followed: - The population of public Francophone schools was extracted from the in 2011-12 Education Management Information System database and used as the sampling frame; - Schools were split into the 15 strata defined to capture the school location (8 regions and 2 zones, namely rural-urban). - Assuming that the number of teachers and students in grade 2 and 3 in in 2011-12 indicates the numbers of teachers to be expected in grades 3 and 4 in 2012-13, the number of grade 2 and 3 teachers/classes for each school was calculated. - The number of schools to be selected per strata was established using the Markwardt protocol (this represent the average between selecting a proportional and equal number of schools per strata). The selection probability of each individual school was established using the number of targeted teachers in the school and the number of schools in the strata. - Using a random starting point and the selection probability, 450 individual schools and 75 replacement schools were selected.

This sample was completed by adding the 16 pre-identified schools where the instruments were piloted in the 2011-12 academic year. Therefore, before launching the baseline fieldwork, a sample of 466 schools was targeted. Within each school, all grade 3 and 4 teachers and all of their students were targeted.

Randomization is at the school level but target beneficiaries are the teachers. 420 schools, all grade 3 and 4 teachers, all grade 3 and 4 students.

Sampling deviation

While in the field at baseline, the teams were unable to locate some of the schools and some of the located schools turned out not to have the targeted grades and thus had to be taken out of the sample. The final sample contains 420 schools. No replacement schools were used. The final sample therefore differs from the targeted sample and national representativeness is uncertain. It is important to note that this does not reduce the internal validity of the IE design since the random assignment of schools to the different experiment arms was carried-out once the realized sample of schools was stabilized.

Sample of teachers and students: once in the school in May 2012, the aim was to administer a standardized test to all grade 2 and 3 students (who will be grade 3 and 4 students in 2012-13, the first year of the intervention). Furthermore, in October 2012, the aim was to survey/inspect all grade 3 and 4 teachers for the 2012-13 academic year. At baseline, a total of 416 principal and 1177 teachers were surveyed; 1214 inspected, and 23183 students participated in the test.

Within each of the targeted teachers’ classes, the objective was that all students should take part in the test. However, once in the field, the teams were faced with larger schools (in terms of the number of students) than expected and thus did not have enough printed copies of the tests to administer it to all students. In about one third of the visited schools, instead of randomly selected students within each of the classroom, only subset of the classrooms (all students) were selected to participate in the test. Furthermore, when class size was too big, a random selection of students within the selected classes was carried out. There is no reason to believe that the selection of students within a class was not random but there is also no certainty that it was. Finally, because of teacher absenteeism and logistics difficulties, the tests were only administered in 353 out of the 420 targeted schools.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used: (i) a questionnaire administered to the school’s principal, (ii) a teacher questionnaire administered to targeted teachers (grade 3 and 4), (iii) a Math test (a few parallel booklets), a French test (a few parallel booklets) administered to students in the targeted teachers’ classrooms, and (iv) an inspection bulletin administered to targeted teachers in the context of two lessons, one in French and one in Math.

Below is presented a more detailed description of the various instruments.

Principal questionnaire (May and October 2012) A. School and principal identification B. Demographics C. Education and professional training D. Work experience and training needs E. Pedagogical practices and languages F. School basic characteristics G. School environment H. Interaction with colleagues (subordinate, supervisors, etc.) I. Support and monitoring of teachers J. Motivation

Teacher questionnaire(May and October 2012) A. Class and teacher identification B. Demographics C. Class characteristics D. Education and professional training E. Work experience and training needs F. Pedagogical practices and languages G. Interaction with colleagues H. Motivation I. Absenteeism and events disturbing teaching J. Remuneration K. Perception of key factors influencing student learning L. Performance recognition or punishment

Student tests (May 2012) 1.A Identification of school, class, and teachers 2.B. School-related student characteristics 1.C. Student environmental and familial backgrounds 2. French test questions 3. Math test questions

Inspection bulletin (October 2012) I. Class and inspector identification II. Teacher identification III. Summary of scores VI. General material and spatial classroom arrangement V.1 Lesson 1 – Identification of the lesson V.2 Lesson 1 – Teaching and learning material preparation V.3 Lesson 1 – Lesson planning (according to the Competency-based approach) V.4 Lesson 1 – Delivery of the lesson V.5 Lesson 1 – Analysis of the own’s performance VI.1 Lesson 2 – Identification of the lesson VI.2 Lesson 2 – Teaching and learning material preparation VI.3 Lesson 2 – Lesson planning (according to the Competency-based approach) VI.4 Lesson 2 – Delivery of the lesson VI.5 Lesson 2 – Analysis of the own’s performance

Response rate

Response rates varies from high in the case of inspection and questionnaires to a little lower in the case of test. Balance analysis indicates that these response rates were orthogonal to treatment.

Classroom management is an essential task for every teacher, yet it is often experienced as a major challenge by preservice and beginning teachers. A complexity of classroom management is that within information-dense classroom environments teachers should notice salient classroom management situations and take immediate action. Little is known yet about teachers’ noticing as basis for their own classroom management and how this unfolds with years of teaching experience. The overall goal of the current research project is to explore preservice, beginning and experienced teachers’ noticing as basis for their classroom management while teaching their own secondary classrooms. Mobile eye-tracking data provide information about teachers' gaze behavior during teaching. Signaling data in- and on-action allow for capturing the frequency and timing of classroom situations teachers identified as salient for their classroom management during teaching. Verbal data provide information about the type of situations teachers identified during teaching and their accompanying cognitions.

Student Behaviour In Classroom 2

## Overview

Student Behaviour In Classroom 2 is a dataset for object detection tasks - it contains Students annotations for 1,470 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Classroom Sound System Market Outlook

The global classroom sound system market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 2.6 billion by 2032, growing at a CAGR of 8.1% from 2024 to 2032. The growth in this market is driven by the increasing emphasis on interactive and immersive learning environments, the proliferation of smart classrooms, and the integration of advanced audio technologies to enhance the quality of education.

The rising demand for enhanced communication in educational settings is a significant growth factor for the classroom sound system market. Clear audio is crucial for effective teaching and learning, particularly in large classrooms and auditoriums. High-quality sound systems help ensure that all students, regardless of where they are sitting, can hear the instructor clearly, which improves comprehension and engagement. The growing adoption of blended learning models that combine traditional face-to-face instruction with digital tools is also contributing to the demand for advanced sound systems in classrooms. These systems support multimedia presentations and interactive lessons, making education more engaging and effective.

Technological advancements are also playing a crucial role in the growth of the classroom sound system market. Innovations such as wireless microphones, Bluetooth-enabled speakers, and digital signal processors are enhancing the functionality and ease of use of classroom sound systems. These advancements allow for seamless integration with other educational technologies such as interactive whiteboards, projectors, and computers. Additionally, the development of compact and energy-efficient sound systems is making it easier for schools and other educational institutions to adopt these technologies without significant infrastructure changes.

Furthermore, the increasing investment in the education sector by governments and private entities worldwide is boosting the market for classroom sound systems. Many governments are implementing policies and funding programs aimed at modernizing educational infrastructure, including upgrading audio-visual equipment in classrooms. Private investments from educational technology companies and philanthropic organizations are also contributing to the development and deployment of advanced sound systems in schools and universities. These investments are particularly significant in emerging economies where there is a strong push towards improving educational outcomes through the adoption of modern technologies.

The importance of a robust Sound System in educational settings cannot be overstated. As classrooms evolve into dynamic learning environments, the role of sound systems becomes increasingly critical. These systems are not just about amplifying the teacher's voice; they are integral to creating an immersive learning experience. A well-designed sound system can facilitate better communication, enhance student engagement, and support various teaching methodologies. With the integration of multimedia tools and digital content, sound systems help in delivering clear and consistent audio, which is essential for maintaining student focus and participation. As educational institutions continue to adopt more interactive and technology-driven teaching methods, the demand for sophisticated sound systems is expected to rise, making them a cornerstone of modern education infrastructure.

From a regional perspective, North America and Europe currently dominate the classroom sound system market, driven by the high adoption rate of advanced educational technologies and significant funding for educational infrastructure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by increasing government initiatives to improve education quality and rising investments in smart classroom technologies. The growing awareness about the benefits of high-quality sound systems in enhancing learning outcomes is also contributing to the market growth in this region.

Product Type Analysis

The product type segment of the classroom sound system market is categorized into speakers, microphones, amplifiers, mixers, and others. Each of these product types plays a distinct role in creating a comprehensive audio environment in educational settings. Speakers are perhaps the most critical component, as they are responsible for delivering clear and intelligible audio across the classroom. Modern speakers come with advanced features such

Hypothetically, a student could attend a class, listen to lectures, and pass the class without knowing or interacting with other students. What happens to the network when the classroom expectations change? For example, there is a coursework expectation that students exchange contact information, or the instructor uses collaborative learning practices. Or what if the principal investigator (PI) of a scientific team goes on a sabbatical? This study uses the framework of classrooms because of their relatability across science. We asked how do different instructor coursework expectations change network structures within a classroom or other learning environments? A social network survey was administered at the start and end of the semester (pre- and post-test) in six university sociology classrooms to explore how expectations impacted the communication and learning networks. We found practical changes in course expectations impact the communication and learning networks, suggesting that instructors, facilitators, and others could be the archintorTM (architect+instructor+facilitator) of the network. Understanding that expectations can impact a network’s structure marks a paradigm shift in educational assessment approaches. If the archintorTM has identified the “optimal” network structure, then their task is to design expectations that result in specific interactions that ultimately improve student achievement and success. This work provides recommendations for classroom archintorsTM to create the most impactful classroom networks. Future research should extend beyond education and classroom networks and identify the best or desired networks in other areas like public policy, urban planning, and more. If these “optimal” networks were identified, an archintorTM could design a social network to solve wicked problems, manage a crisis, and create social change.

Session on using real data in the classroom with Serenity, a platform-independent browser-based interface to data science tools for the classroom.

In a 2019 survey, 23 percent of grade 6-12 students reported that their teacher spent all of the class using digital learning technologies to teach in science, math, and history/social studies. All of these were significantly below computer science/information technology though, with 60 percent of surveyed students stating that they were taught using digital learning tools for the entire class.

Note the time teachers spend teaching a subject with digital tools was differentiated in the survey from the time students spent learning with digital learning tools. For almost all subjects, the time spent learning with digital tools was lower than the time spent teaching.

The programs replicate tables and figures from "Evolution vs. Creationism in the Classroom: The Lasting Effects of Science Education in the Classroom", by Benjamin Arold. Please see the README file for additional details.

Classroom Detected

## Overview

Classroom Detected is a dataset for object detection tasks - it contains Neu Classroom Detected Behavior Of Student In Classroom annotations for 4,790 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).