This dataset includes indicators that reflect attendance data for students in Massachusetts public schools since 2018. Because the information collected has changed over time, not all indicators are available across all years.

Attendance indicators are calculated for students enrolled in grades PK-12 with at least 20 days in membership. For more information, please view the Profiles Help - About the Data page.

Massachusetts defines chronically absent as missing at least 10 percent of days enrolled (for instance, 18 days absent if enrolled for a typical school year of 180 days), regardless of the reason for the absence. Being chronically absent can have a significant impact on a student's academic progress and their ability to access the variety of academic and non-academic supports that schools provide.

Chronic absenteeism grew during the COVID-19 pandemic and, as of the 2022-23 school year, had not returned to pre-pandemic rates. For more information about chronic absence, visit the Chronic Absence and Student Attendance page.

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset contains the same data that is also published on our DESE Profiles site: Student Attendance Report

Full 2023/24 academic year local authority, regional and national attendance since 11 September 2023, including reasons for absence. Figures are provided for state-funded primary, secondary and special schools broken down by Free School Meals eligibility. Totals for all schools are also included that include estimates for non-response.

This dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. Metrics are described as follows (also available for download at http://bit.ly/uhbzah): NDA indicates "No Data Available." SAFETY ICON: Student Perception/Safety category from 5 Essentials survey // SAFETY SCORE: Student Perception/Safety score from 5 Essentials survey // FAMILY INVOLVEMENT ICON: Involved Families category from 5 Essentials survey // FAMILY INVOLVEMENT SCORE: Involved Families score from 5 Essentials survey // ENVIRONMENT ICON: Supportive Environment category from 5 Essentials survey // ENVIRONMENT SCORE: Supportive Environment score from 5 Essentials survey // INSTRUCTION ICON: Ambitious Instruction category from 5 Essentials survey // INSTRUCTION SCORE: Ambitious Instruction score from 5 Essentials survey // LEADERS ICON: Effective Leaders category from 5 Essentials survey // LEADERS SCORE: Effective Leaders score from 5 Essentials survey // TEACHERS ICON: Collaborative Teachers category from 5 Essentials survey // TEACHERS SCORE: Collaborative Teachers score from 5 Essentials survey // PARENT ENGAGEMENT ICON: Parent Perception/Engagement category from parent survey // PARENT ENGAGEMENT SCORE: Parent Perception/Engagement score from parent survey // AVERAGE STUDENT ATTENDANCE: Average daily student attendance // RATE OF MISCONDUCTS (PER 100 STUDENTS): # of misconducts per 100 students//AVERAGE TEACHER ATTENDANCE: Average daily teacher attendance // INDIVIDUALIZED EDUCATION PROGRAM COMPLIANCE RATE: % of IEPs and 504 plans completed by due date // PK-2 LITERACY: % of students at benchmark on DIBELS or IDEL // PK-2 MATH: % of students at benchmark on mClass // GR3-5 GRADE LEVEL MATH: % of students at grade level, math, grades 3-5 // GR3-5 GRADE LEVEL READ: % of students at grade level, reading, grades 3-5 // GR3-5 KEEP PACE READ: % of students meeting growth targets, reading, grades 3-5 // GR3-5 KEEP PACE MATH: % of students meeting growth targets, math, grades 3-5 // GR6-8 GRADE LEVEL MATH: % of students at grade level, math, grades 6-8 // GR6-8 GRADE LEVEL READ: % of students at grade level, reading, grades 6-8 // GR6-8 KEEP PACE MATH: % of students meeting growth targets, math, grades 6-8 // GR6-8 KEEP PACE READ: % of students meeting growth targets, reading, grades 6-8 // GR-8 EXPLORE MATH: % of students at college readiness benchmark, math // GR-8 EXPLORE READ: % of students at college readiness benchmark, reading // ISAT EXCEEDING MATH: % of students exceeding on ISAT, math // ISAT EXCEEDING READ: % of students exceeding on ISAT, reading // ISAT VALUE ADD MATH: ISAT value-add value, math // ISAT VALUE ADD READ: ISAT value-add value, reading // ISAT VALUE ADD COLOR MATH: ISAT value-add color, math // ISAT VALUE ADD COLOR READ: ISAT value-add color, reading // STUDENTS TAKING ALGEBRA: % of students taking algebra // STUDENTS PASSING ALGEBRA: % of students passing algebra // 9TH GRADE EXPLORE (2009): Average EXPLORE score, 9th graders who tested in fall 2009 // 9TH GRADE EXPLORE (2010): Average EXPLORE score, 9th graders who tested in fall 2010 // 10TH GRADE PLAN (2009): Average PLAN score, 10th graders who tested in fall 2009 // 10TH GRADE PLAN (2010): Average PLAN score, 10th graders who tested in fall 2010 // NET CHANGE EXPLORE AND PLAN: Difference between Grade 9 Explore (2009) and Grade 10 Plan (2010) // 11TH GRADE AVERAGE ACT (2011): Average ACT score, 11th graders who tested in fall 2011 // NET CHANGE PLAN AND ACT: Difference between Grade 10 Plan (2009) and Grade 11 ACT (2011) // COLLEGE ELIGIBILITY: % of graduates eligible for a selective four-year college // GRADUATION RATE: % of students who have graduated within five years // COLLEGE/ ENROLLMENT RATE: % of students enrolled in college // COLLEGE ENROLLMENT (NUMBER OF STUDENTS): Total school enrollment // FRESHMAN ON TRACK RATE: Freshmen On-Track rate // RCDTS: Region County District Type Schools Code

Online Attendance System for Students Market Outlook

The global market size for the Online Attendance System for Students in 2023 was estimated at USD 1.2 billion. With a forecasted CAGR of 12.5%, the market is expected to grow significantly, reaching USD 3.6 billion by 2032. This growth is primarily driven by the increasing adoption of digital attendance tracking solutions in educational institutions worldwide, alongside a rising need for efficient and accurate student attendance management.

The rapid digital transformation within the education sector is one of the primary growth factors for this market. Educational institutions are increasingly leveraging technology to enhance administrative efficiency and provide a better learning experience for students. The integration of online attendance systems helps institutions to automate and streamline attendance tracking, thereby reducing manual errors and administrative workload. This shift is further supported by the rising availability of affordable and advanced digital solutions, making it feasible for even smaller institutions to adopt these systems.

Another significant factor contributing to the market growth is the increasing emphasis on data-driven decision-making in education. Online attendance systems provide real-time data and analytics that can be used by educators and administrators to monitor student attendance patterns, identify potential issues, and implement timely interventions. This data-centric approach not only helps improve student engagement and performance but also aids in ensuring compliance with regulatory requirements regarding attendance tracking.

Moreover, the ongoing COVID-19 pandemic has accelerated the adoption of online attendance systems. With many educational institutions shifting to remote and hybrid learning models, the need for robust and reliable attendance tracking solutions has become more critical than ever. Online attendance systems enable educators to track student participation in virtual classrooms accurately, thereby maintaining accountability and ensuring that learning objectives are met. This trend is expected to continue even post-pandemic, as institutions recognize the long-term benefits of digital attendance tracking.

In the realm of educational technology, the Employee Attendance Tracker has emerged as a pivotal tool for institutions looking to manage staff attendance efficiently. Just as student attendance systems streamline the tracking of student presence, employee attendance trackers provide a comprehensive solution for monitoring faculty and administrative staff attendance. These systems offer features such as real-time attendance logging, leave management, and detailed reporting, which are crucial for maintaining operational efficiency in educational institutions. By integrating such systems, schools and universities can ensure that staff attendance data is accurately captured and analyzed, leading to improved resource allocation and enhanced institutional productivity.

Regionally, North America is expected to lead the market due to the high adoption rate of advanced educational technologies and the presence of key market players. Europe and the Asia Pacific are also anticipated to witness significant growth, driven by increasing government initiatives to digitize education and the rising awareness of the benefits of online attendance systems. In contrast, Latin America, and the Middle East & Africa are expected to show moderate growth due to varying levels of technological adoption and infrastructural challenges. However, government initiatives and investments in educational technology are likely to drive future growth in these regions.

Component Analysis

The Online Attendance System for Students market can be segmented by component into software, hardware, and services. The software segment is anticipated to hold the largest market share due to the growing demand for cloud-based and on-premises attendance management solutions. These software solutions offer a range of features, including real-time tracking, analytics, and integration with other educational systems, making them a preferred choice for educational institutions. Additionally, the increasing adoption of mobile applications for attendance tracking is further propelling the growth of the software segment.

The hardware segment, though smaller in comparison, is also witnessing significant growth. Hardware components such as

Abstract

The main objective of the interventions supported by this impact evaluation is to strengthen linkages between communities and school to improve education outcomes. Rigorous evidence generated from the research will provide valuable information to Pakistani policy makers, donors and development practitioners on the ways in which school based management reforms can be strengthened in low-governance environments like Sindh, Pakistan. The findings of this research are valuable for the ongoing dialogue with the GoSindh on school based management, one of the critical reform area supported under the Second Sindh Education Sector Program (SEP-II).

The impact evaluation is a component of the World Bank's ongoing technical and advisory support to the Government of Sindh for improving the quality and performance of government primary schools as part of its medium-term, multi-pronged Sindh Education Sector Reform Program (SERP-II). An important subprogram under SERP and SERP-II has been the revitalization of school management committees (SMCs) in government schools, with the provision of annual school improvements grants and basic guidelines on SMCs rights, roles and responsibilities across Sindh province. An area of concern in these early efforts has been poor or dissipating community interest and engagement. The interventions piloted in select districts of rural Sindh were designed by the World Bank in partnership with the Reform Support Unit, which is the implementation arm of the Education and Literacy Department of GoSindh. The aim of these interventions was to explore concrete ways to elicit meaningful and sustained local community engagement in improving education outcomes.

Both the baseline survey and the interventions were implemented in three pilot districts in 2012 and 2013. The core intervention being evaluated is community engagement to revitalize SMCs under two distinct mechanisms: 1) a community-level meeting to engage the community in a dialogue for school improvement via SMCs; 2) a virtual network of community members to engage in a similar dialogue supported through text messages on mobile phones.

The first intervention arm makes use of an existing social platform, enabling community members to participate in traditional meetings to acquire information and engage the community in dialogue and discussion on school-related issues. The second arm has created an innovative virtual platform through which registered community members receive school-related information, anonymously send text messages about these issues and receive a summary of key observations or issues twice every month.

The baseline survey, documented here, was implemented in January 2012 - January 2013. There is no midline survey for this study. The endline survey will start in January 2015.

Geographic coverage

Mirpur Khas, Mitiari and Sanghar districts in Sindh province.

Analysis unit

The unit of randomization for the intervention is a village.

Administered questionnaires have the following units of analysis: individuals (teachers, students, parents), households, schools, and communities.

Universe

All primary schools and rural households in Mirpur Khas, Mitiari and Sanghar districts in Sindh province.

Kind of data

Sample survey data [ssd]

Sampling procedure

The districts chosen for the study were based on district ranks in terms of school density in the district and school participation rates from the Pakistan Social and Living Standards Measurement Survey (PSLM) and Administrative School Census (ASC) data respectively. One district each was chosen from the low, middle and top category to make an overall representative sample of rural Sindh. By this method, the final districts selected were Mirpur Khas, Mitiari and Sanghar. Using the ASC data in terms of number of schools, Mitiari was ranked the third smallest district, Mirpur Khas was ranked at number twelve (middle rank) and Sanghar at number eighteen (top rank). Using the PSLM for education indicators (proportion of adults who ever attended school and school participation rate of primary-age children), Sanghar ranked at the top followed by Mitiari (median) and lastly by Mirpur Khas.

The Administrative School Census (ASC) data is collected by the Government of Sindh every year to provide an updated list of primary schools in all districts of Sindh. The census data for 2010-2011 was used to randomly draw 300 villages within our sample districts. However, because of poor quality of administrative census data, researchers conducted a census listing of all households and also mapped all primary schools in these 300 villages to set the population frame for the study.

• School Sampling Strategy

The school sampling strategy was primarily to target all primary schools in the main settlement that were either open on the day of visit or closed for a period of less than one year. In addition, 15% of the remaining schools in these villages were also surveyed to capture spillover effects. For villages with no school in the main settlement, all schools located out of the main settlement were surveyed1. For villages that did not meet these criteria, all schools were sampled even if the school was closed for more than one year. 4 villages had to be dropped because no school was found in village-level mapping of primary schools.

• Household Sampling Strategy

The household sampling strategy for each village was to randomly select 20 households from the main settlement and 8 households from the peripheral settlements conditional on the household having at least one child of school going age (5-16 years). From this list, the first 16 households were to be surveyed and in case the head of the household was not available, the household was substituted from the list of four buffer households. For the peripheral settlement, any 4 out of the 8 households were surveyed2. In addition, household questionnaires were also administered to all SMC members from the target schools, approximately 4 households in a village.

Overall, on the school level 514 school, 454 head teacher, 409 teacher and 4,573 student questionnaires were administered. On the household level, 6,505 head of the household, 6,503 spouse, 5,281 child and 901 school management committee questionnaires were administered.

Mode of data collection

Face-to-face [f2f]

Research instrument

School Surveys

Detailed data on school-level variables such as enrollment, attendance, teacher on-task, facilities, school committees, funding and expenditure were collected through a set of four questionnaires: School Observation, Teacher Roster, Head Teacher and Teacher Questionnaire. In addition, a list of School Management Committees (SMC) members was enumerated at the school-level for household surveys.

School Observation Questionnaire

School questionnaire consisted of five sections and was based on the observation of the enumerator about school building, facilities, hygiene conditions, on-going classroom practices and teacher activities. The questionnaire also required the enumerator to record school GPS coordinates and school visit details.

Head Teacher Questionnaire

Head Teacher questionnaire compromised of two parts: information based on the head teacher’s knowledge and information based on official school records. The first part gathered data on the respondent’s personal and professional background as well as his knowledge of students, school facilities and SMC. The second part collected official school details on school improvement plan, enrollment, attendance, fee, SMC funds and expenditures.

School Teacher Questionnaire

Teacher questionnaire consisted of nine sections and was administered to all teachers present in the school . It gathered the personal and professional information of the teacher as well as his perceptions on SMC functionality, student learning and returns to education.

Teacher Roster Questionnaire

Teacher Roster collected information on teachers that are currently teaching in the school and those that left or transferred over the last two years. The survey recorded teacher information on attendance, contact number, gender, contract type, pay scale and class taught. For teachers that have left, it also covered information on reasons for leaving school. The information for the roster is to be provided by the head teacher or the senior most teacher in the school.

Household Surveys

The baseline survey also covered households to gather information on demographic and socioeconomic characteristics, parent choices about child’s school, parent engagement with school’s SMC, adult perceptions of returns to schooling and quality of learning through four set of questionnaires: Household Roster, Household Head Questionnaire, Spouse of Head Questionnaire and SMC Member Questionnaire.

Household Roster Questionnaire

The household roster questionnaire collected information about gender, age, marital status, education and job status of all members of the household. This roster information was filled by the head of the household but in case of his absence, the survey was filled by other members that were required to explain their relationship to the head.

Head of the Household Questionnaire

The head of the household questionnaire consisted of fifteen sections and collected detailed information on family members, education, consumption pattern, business details, household expenditures and incomes. It also recorded information on about the respondent’s aspirations, awareness about the SMC, trust in the education system and perceptions about returns to education and quality of learning in the respective school.

Questionnaire for Female

This dataset contains special education indicators since 2017. It is a long file that contains multiple rows for each district, with rows for different years, comparing students with disabilities, students without disabilities, and all students on a wide range of indicators. Not all indicators are available for all years. For definitions of each indicator, please visit the RADAR Special Education Dashboard.

Resource Allocation and District Action Reports (RADAR) enable district leaders to compare their staffing, class size, special education services, school performance, and per-pupil spending data with similar districts. They are intended to support districts in making effective strategic decisions as they develop district plans and budgets.

This dataset is one of five containing the same data that is also published in the RADAR Special Education Dashboard: Special Education Program Characteristics and Student Demographics Special Education Placement Trajectory Students Moving In and Out of Special Education Services Special Education Indicators Special Education Student Progression from High School through Postsecondary Education

Below is a list of indicators that are included within the dataset. Note: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion" are available for download in this companion dataset. These two indicators are separate from the main Special Education Indicators download since the data are in a different format.

List of Indicators

Context

• Stability rate (enrolled all year)
• Student Enrollment

• 4-year cohort graduation rate
• 5-year cohort graduation rate
• 9th to 10th grade promotion rate (first-time 9th graders only)
• Annual dropout rate
• Chronically absent rate (% of students absent 10% or more each year)
• Student attendance rate
• Students absent 10 or more days each year
• Students suspended in school at least once
• Students suspended out-of-school at least once

• Average student growth percentiles (SGP) in ELA (Grades 3-8)
• Average student growth percentiles (SGP) in ELA (Grade 10)
• Average student growth percentiles (SGP) in math (Grades 3-8)
• Average student growth percentiles (SGP) in math (Grade 10)
• Meeting or exceeding expectations on ELA (Grades 3-8)
• Meeting or exceeding expectations on ELA (Grade 10)
• Meeting or exceeding expectations on math (Grades 3-8)
• Meeting or exceeding expectations on math (Grade 10)
• Meeting or exceeding expectations on science (Grades 5 and 8)
• Meeting or exceeding expectations on science (Grade 10)

• Jr / Sr AP test takers scoring 3 or above
• Jr / Sr enrolled in one or more AP / IB courses
• Jr / Sr who took AP courses and participated in one or more AP tests
• SAT average score - Mathematics
• SAT average score - reading

• 12th graders passing a full year of mathematics coursework
• 12th graders passing a full year of science and technology/engineering coursework
• 9th graders completing and passing all courses
• High school graduates who completed MassCore

• College students persistently enrolled in postsecondary education for the first two years
• High school graduates enrolled in 2-year postsecondary education
• High school graduates enrolled in 4-year postsecondary education
• Students enrolled in postsecondary education in the immediate fall after high school graduation
• Student progression from high school through postsecondary degree completion
• Student progression from high school through second year of postsecondary education

• Special education director FTE
• Special education teachers per 100 SWD
• Special education paraprofessionals per 100 SWD
• Special education support staff per 100 SWD

Discipline in schools is typically disproportionate, reactive and punitive. Evidence-based strategies that have been recently developed focus on shifting schools to a more proactive and positive approach by detecting warning signs and intervening early. This project evaluates the implementation of an evidence-based intervention to improve students' mindsets and feelings of school belonging. This grant-funded project was designed to enhance school capacity to implement a Tier 2 intervention, Student Engagement and Empowerment (SEE), to improve student attendance, behavior, and achievement, while simultaneously evaluating the effects of this intervention. The intervention and research project were individualized to fit existing school operations in the school district. A grant-funded coach supported delivery of SEE at each school for the duration of the 3-year grant. SEE was delivered by trained teachers in the classroom over the course of a seven-session curriculum. The overarching project goal was to scale up and simultaneously evaluate a Tier 2 intervention that could be sustained after completion of the grant. The originally proposed research procedures consisted of an evaluation of the effects of the SEE program on the outcomes of students at elevated risk for disciplinary action and school dropout. Outcome data was collected for at-risk students in classrooms delivering the SEE program, and a comparison sample of at-risk students in classrooms not delivering the SEE program. Researchers initially hypothesized that students receiving the program would evidence a greater sense of belonging to school, endorse greater growth mindset, have better attendance and fewer suspensions/expulsions and course failure, and have better behavioral outcomes than students in the comparison group.

School and district report cards are designed to show parents and community members how a school or district is doing in different areas over multiple years. Report cards highlight a school or district's strengths as well as any challenges that need to be addressed in order to make sure the needs of all students are being met. For more information, visit the School and District Report Cards homepage.

List of Indicators

Students

• Student Enrollment
• Student Demographics

• Teacher Workforce
• Teacher Qualifications

• Access to the Arts
• Access to Digital Literacy and Computer Science Courses
• Advanced Coursework Completion
• Grade 9 Course-Passing
• MassCore Completion

• Attendance Rate
• Chronic Absenteeism Rate
• Average Number of Days Absent

• Suspensions and Expulsions
• Other Incidents

• Graduation Rates
• Annual Dropout Rate

• College-Going Rates

• Student Achievement on MCAS
• Student Progress on MCAS

Accountability Information

Attendance Tracking System Market Outlook

The global attendance tracking system market size was valued at USD 1.8 billion in 2023 and is projected to reach USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period. This significant growth is driven by factors such as the increasing need for accurate employee attendance records, enhanced operational efficiency, and the rising adoption of automated systems in various sectors.

One of the primary growth factors in the attendance tracking system market is the increasing need for real-time data and analytics. Companies across various industries are now focusing on enhancing productivity and operational efficiency, and accurate attendance tracking plays a crucial role in this. The advent of advanced technologies such as artificial intelligence (AI) and machine learning (ML) has further facilitated the development of sophisticated attendance tracking solutions, which provide real-time data and analytics to help organizations manage their workforce more effectively.

Another significant growth factor is the rising adoption of cloud-based attendance tracking systems. Cloud-based solutions offer numerous benefits, such as easy access to data from any location, scalability, and cost-effectiveness. These systems are particularly beneficial for small and medium-sized enterprises (SMEs) that require a flexible and affordable solution to manage their workforce. Moreover, the increasing trend of remote working and the need for remote attendance tracking systems have further propelled the demand for cloud-based solutions.

The growing emphasis on compliance and regulatory requirements is also driving the market for attendance tracking systems. Organizations across various sectors, including education, healthcare, and government, are required to maintain accurate attendance records to comply with labor laws and regulations. Attendance tracking systems help organizations ensure compliance by providing accurate and tamper-proof records. Additionally, these systems can generate various reports and analytics, which can be used for audit purposes, further enhancing their appeal.

In the educational sector, School Attendance Software is becoming increasingly vital for institutions aiming to streamline their administrative processes and enhance student engagement. These software solutions provide schools with the ability to track student attendance in real-time, ensuring that records are accurate and up-to-date. This not only helps in maintaining compliance with educational regulations but also aids in identifying attendance patterns that may indicate issues such as truancy or disengagement. Furthermore, the integration of School Attendance Software with other educational tools can facilitate better communication between teachers, students, and parents, fostering a more collaborative learning environment. As educational institutions continue to embrace digital transformation, the demand for sophisticated attendance solutions is expected to grow, driving further innovations in this space.

Regionally, North America holds a significant share in the attendance tracking system market, driven by the presence of numerous large enterprises and a high adoption rate of advanced technologies. The Asia Pacific region is expected to witness the highest growth during the forecast period, owing to the increasing adoption of digital solutions in emerging economies such as India and China. The growing number of SMEs and the rising trend of remote working are also contributing to the market growth in this region.

Component Analysis

The attendance tracking system market can be segmented by component into software, hardware, and services. The software segment holds the largest share of the market, driven by the increasing demand for automated and efficient attendance tracking solutions. Software solutions offer various benefits, such as real-time data access, automated reporting, and integration with other HR systems, making them highly attractive to organizations of all sizes. The proliferation of cloud-based software solutions has further fueled the growth of this segment.

The hardware segment includes biometric devices, RFID systems, and other hardware components used in attendance tracking systems. Biometric devices, in particular, are gaining popularity due to their accuracy and reliability. These devices use unique biological traits, s

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations.

This dataset is a long file that contains multiple rows for each school and district, with rows for different years, different student groups, and a wide range of indicators.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

Below is a list of indicators that are included within the dataset. Note: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion" are available for download in this companion dataset. These two indicators are separate from the main DART: Success After High School download since the data are in a different format.

List of Indicators

Context

• Stability rate (enrolled all year)
• Student Enrollment

• 4-year cohort graduation rate
• 5-year cohort graduation rate
• 9th to 10th grade promotion rate (first-time 9th graders only)
• Annual dropout rate
• Chronically absent rate (% of students absent 10% or more each year)
• Student attendance rate
• Students absent 10 or more days each year
• Students suspended out-of-school at least once

• Average student growth percentiles (SGP) in ELA
• Average student growth percentiles (SGP) in mathematics
• Grade 10 students meeting or exceeding expectations in ELA
• Grade 10 students meeting or exceeding expectations in mathematics
• Jr / Sr AP test takers scoring 3 or above
• Jr / Sr enrolled in one or more AP / IB courses
• Jr / Sr who took AP courses and participated in one or more AP tests
• SAT average score - Mathematics
• SAT average score - reading
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - All subjects
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Art
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - English
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Foreign Language
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - History/Social Science
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Mathematics
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Science

• College students persistently enrolled in postsecondary education for the first two years
• Grade 12 students who completed FAFSA
• High school graduates enrolled in 2-year postsecondary education
• High school graduates enrolled in 4-year postsecondary education
• Students enrolled in postsecondary education in the immediate fall after high school graduation
• Student progression from high school through postsecondary degree completion
• Student progression from high school through second year of postsecondary education

• 12th graders passing a full year of mathematics coursework
• 12th graders passing a full year of science and technology/engineering coursework
• 9th graders completing and passing all courses
• High school graduates who completed MassCore

Abstract

This impact evaluation was conducted by IDinsight for STIR Education in Delhi and Uttar Pradesh in India, and was funded by a World Bank Strategic Impact Evaluation Fund grant. The study seeks to evaluate the impact of STIR's purely motivational, pedagogically neutral, teacher-focused model on student learning levels. STIR works with teachers in low-cost and government schools in order to improve student learning by empowering teachers to act as change-makers and to innovate to overcome challenges in the classroom. IDinsight conducted two three-armed randomized control trials. The study looks at outcomes from 180 Affordable Private Schools (APS) in Delhi and 270 government schools in the Raebareli and Varanasi districts of Uttar Pradesh. The study began in early 2015, and lasted two academic years. In addition to measuring STIR's impact in two different contexts, the study simultaneously tests two iterations of STIR's model in these two contexts.

Geographic coverage

One district in Delhi - East Delhi, and two districts in Uttar Pradesh - Raebareli and Varanasi

Analysis unit

For student learning, the basic unit of analysis is students. For classroom practices, the basic unit of analysis is teachers. For teacher motivation, the basic unit of analysis is teachers.

Universe

• 180 Affordable Private Schools in Delhi, 540 teachers amongst these schools and 5,400 students
• 270 Government Schools in Uttar Pradesh, 810 teachers amongst these schools and 8,100 students

Kind of data

Sample survey data [ssd]

Sampling procedure

Baseline Respondent Identification and Sampling Strategy:

Delhi:

Teacher Motivation: STIR initially did a search process of several hundred Affordable Private Schools (APS) in east Delhi. From these schools, STIR passed school names onto IDinsight where the teachers might be interested in working with IDinsight. IDinsight attempted to sample all schools for the Teacher Motivation survey. In total, IDinsight interviewed 1,259 teachers for the Teacher Motivation survey.

Classroom Observation: From these 1,259 teachers, STIR did an additional round of screening to determine which teachers were the most interested and returned a list of 810 teachers to IDinsight. This list formed the basis of the classroom observation. However, due to attrition and refusals at the school level we were unable to meet our target of teachers and ended up surveying only 342 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes (of all teachers covered for the classroom observation) with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Uttar Pradesh:

Teacher Motivation: In Uttar Pradesh, IDinsight obtained a list of all clusters in Raebareli and Varanasi districts that STIR was working in. From this list, IDinsight selected all clusters with more than 16 schools. This was done to ensure that there would be enough schools in the cluster to assign some to the control group while also maintaining enough treatment schools for STIR to form a network. For the Teacher Motivation survey, IDinsight surveyed all teachers in the school, yielding 1,145 teachers.

Classroom Observation: For the classroom observation, IDinsight sampled roughly 2/3 of the teachers who completed the Teacher Motivation questionnaire, to get a final list of roughly 810 teachers. Teachers were added to this list due to teachers dropping out and the final number was 838 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Midline Respondent Identification and Sampling Strategy:

For midline, which took place at the beginning of the second academic year, we followed up with teachers and students surveyed at baseline. Teachers were added only in the case where the number of teachers still teaching in the school from our baseline lists fell below a certain number. In Delhi, teachers were added if less than two teachers from our list in a given school were available and in Uttar Pradesh, new teachers were added only if all teachers from our baseline lists in a given school dropped out.

The sampling strategy had two clear advantages: 1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 1,259 teachers surveyed at teacher motivation baseline, 453 teachers dropped out of schools during the academic year and hence were not available for surveying during midline. A further 65 teachers refused to participate and 84 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 657. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 811 teachers on the Delhi original list. For those schools where the number of teachers available from our 811 list fell below two, 148 new teachers were added based on a random selection from those teachers employed at that school as of 1 July 2015. A total of 459 teachers were surveyed as part of the classroom observation midline.

Student Testing: For testing of student learning levels, all students surveyed at baseline formed the potential sample at midline. Among the 3,367 students from baseline, 1,956 students were tracked and surveyed at midline. 1,127 students had dropped out from the schools. 40 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits. The remaining 244 students were in schools where we could not survey.

Uttar Pradesh:

Teacher Motivation: From the 1,145 teachers surveyed at baseline, 288 teachers dropped out of schools during the course of the academic year and were hence not available for data collection. An additional 61 refused to participate in the data collection and 41 were not available through the course of the data collection. The final number of teachers surveyed at midline were 755. This was the sample for analysis.

Classroom Observation: From the list of 838 teachers surveyed at baseline, we successfully observed the classrooms of 734 of these teachers at midline. Another 13 teachers were added in schools where all teachers from our 838 had dropped out. 12 of these 13 were in Raebareli and 1 was in Varanasi. In total, 747 teachers were surveyed. 82 teachers dropped out of the schools in our sample. 13 teachers refused to participate in the data collection and 14 teachers were absent throughout the survey period and were not available on either of our visits.

Student Testing: Of the 7,386 students tested at baseline, a total of 4,560 students were also tested at midline. 615 students were absent all days of visits to the schools. 149 students were in the four schools that refused data collection. 2,062 dropped out of the schools in our sample.

Endline Respondent Identification and Sampling Strategy:

For endline, which took place after the end of the second academic year, we followed up with teachers and students surveyed at midline. In Delhi, one teacher was added per school to the classroom observation sample where possible. Additional teachers were added to the teacher motivation sample by offering the survey to all the teachers in our sample schools. The sampling strategy had two clear advantages:

1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 657 teachers surveyed at teacher motivation midline, 101 teachers dropped out of schools during the academic year and hence were not available for surveying during endline. A further 25 teachers refused to participate and 50 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 481. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 459 teachers on the Delhi midline list as well as 102 teachers we surveyed at baseline and couldn't at midline but were hopeful of covering in the last survey. A new teacher was added to each school's sample where possible. A total of 376 teachers were surveyed as part of the classroom observation endline.

Student Testing: For testing of student learning levels, all students surveyed at midline formed the potential sample at endline. Among the 1,956 students from baseline, 1,843 students were tracked and surveyed at midline. 49 students had dropped out from the schools. 45 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits.

Uttar Pradesh:

Teacher Motivation: From the 967 teachers surveyed at midline, 105 teachers were transfered and 17 retired during the course of the academic year and were hence not available for data collection. An additional 36 refused to participate in the data collection and 26 were not available through

This project assesses the effectiveness of a one-day, 5-hour workshop (ESSI training, hereafter) designed for joint instruction by school staff and police to all school staff. The goal was to promote positive outcomes and reduce police involvement in interactions between staff and students exhibiting inappropriate behavior through increased staff awareness of youth behavior, the functions of the juvenile justice system, and disproportionate minority contact (DMC) in disciplinary action. 1,024 school staff participated in 51 ESSI training sessions throughought the 2015/16 academic year, which also serves as the training year in the longitudinal data. Schools which did not participate in the training served as controls for the participating school. Data were drawn from a panel of students enrolled in either a training or control school, with ten schools in each group. Data on this panel of students was collected for a five-year period, from the 2013/14 through the 2017/18 academic years. School-level data serves as the unit of analysis, as the study's main goal was to test the effects of training on school-wide outcomes. The estimated coefficient indicates small attendance reductions during the post-training phase for the training group. This indicates that most of the differences between the training and control group were statistically insignificant and that there was no pattern of statistically significant positive effects across the training schools. The second set of analyses, performed on student-level data, indicates that male and minority students are more likely to be involved in disciplinary incidents and to receive suspensions or expulsions as a consequence of their behaviors than White and female students.

Student Tracking RFID Market Outlook

According to our latest research, the global Student Tracking RFID market size reached USD 2.1 billion in 2024, with a robust year-over-year growth driven by the increasing need for student safety and operational efficiency in educational institutions. The market is projected to exhibit a CAGR of 12.3% from 2025 to 2033, reaching an estimated USD 5.9 billion by 2033. The primary growth factors fueling this expansion include heightened concerns about student security, regulatory mandates for tracking and attendance, and the growing adoption of digital transformation strategies in the education sector.

The rapid adoption of RFID technologies in educational environments is underpinned by rising safety concerns among parents and administrators. Incidents related to student safety, such as unauthorized campus access, missing students, and transportation mishaps, have prompted educational institutions to seek more reliable and real-time tracking solutions. RFID-based systems offer automated attendance, access control, and transportation management, significantly reducing manual errors and ensuring timely interventions during emergencies. Furthermore, the proliferation of mobile devices and IoT infrastructures in schools and universities has made the integration of RFID systems more seamless and cost-effective, further accelerating market growth.

Another key driver shaping the Student Tracking RFID market is the increasing regulatory emphasis on student safety and accountability. Governments and educational authorities across various regions are introducing stringent guidelines mandating the implementation of electronic tracking and attendance systems. These regulations are not only aimed at safeguarding students but also at improving operational transparency and efficiency within educational institutions. In addition, the growing trend of digital transformation and smart campus initiatives is pushing schools and universities to invest in advanced RFID solutions that can be integrated with other digital platforms, such as learning management systems and campus security networks.

The market is also witnessing significant technological advancements, such as the development of ultra-high frequency RFID solutions and cloud-based student tracking platforms. These innovations enable real-time data collection, remote monitoring, and advanced analytics, providing educational institutions with actionable insights to enhance student safety and optimize resource allocation. The integration of RFID with other emerging technologies, such as artificial intelligence and big data analytics, is further expanding the scope of applications, from attendance tracking to behavioral analysis and predictive security measures. As a result, the Student Tracking RFID market is poised for sustained growth, with vendors focusing on delivering customizable and scalable solutions to meet the diverse needs of educational institutions.

The introduction of RFID Yard Check-in Kiosk systems has revolutionized the way educational institutions manage student transportation and campus entry. These kiosks, equipped with RFID technology, allow for seamless and efficient check-ins, reducing wait times and enhancing security. By automating the check-in process, schools can ensure that students are accounted for as they arrive on campus, providing peace of mind to parents and staff alike. Additionally, the data collected through these kiosks can be integrated with existing student management systems, offering real-time insights into student attendance and movement patterns. This integration not only streamlines administrative processes but also supports data-driven decision-making, ultimately contributing to a safer and more efficient educational environment.

From a regional perspective, North America remains the largest market for Student Tracking RFID solutions, accounting for approximately 38% of the global market share in 2024. This dominance is attributed to early technology adoption, strong regulatory frameworks, and significant investments in educational infrastructure. Asia Pacific, however, is emerging as the fastest-growing region, with a projected CAGR of 15.2% through 2033, driven by increasing government initiatives for student safety, rapid urb

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

School Management Software Market Outlook

According to our latest research, the global school management software market size reached USD 4.1 billion in 2024, reflecting the strong adoption of digital solutions in educational administration worldwide. The market is set to expand at a robust CAGR of 14.2% from 2025 to 2033, driven by the increasing demand for automation, enhanced operational efficiency, and the rising trend of digital transformation in education. By the end of 2033, the school management software market is forecasted to achieve a value of USD 12.7 billion, signifying substantial growth and widespread integration of these platforms across diverse educational settings.

A primary growth factor for the school management software market is the global emphasis on digital transformation in education. Educational institutions are increasingly shifting away from traditional, paper-based administrative processes towards integrated, cloud-based school management software solutions. This transition is being fueled by the need for real-time access to student data, improved communication between stakeholders, and streamlined school operations. The COVID-19 pandemic further accelerated this shift, as remote learning and online administration became essential, pushing schools to adopt robust, scalable, and secure digital platforms. These systems not only enhance efficiency but also provide actionable insights through analytics, enabling data-driven decision-making and improved educational outcomes.

Another significant driver is the growing complexity of educational administration, which necessitates advanced management solutions. With rising student enrollments, diverse course offerings, and increased regulatory compliance requirements, schools and higher education institutions are seeking comprehensive software platforms that can handle everything from attendance tracking to finance and accounting management. Modern school management software integrates multiple modules, allowing seamless management of academic, administrative, and financial tasks. The demand for customization, scalability, and interoperability with existing systems further propels the development and adoption of flexible school management software solutions, catering to the unique needs of institutions of all sizes.

The proliferation of mobile devices and internet connectivity has also played a crucial role in the growth of the school management software market. As students, parents, and teachers increasingly rely on smartphones and tablets for communication and information access, software vendors are prioritizing mobile-friendly and cloud-based solutions. These platforms offer anytime, anywhere access to critical data and facilitate smooth collaboration among all stakeholders. Additionally, the integration of emerging technologies such as artificial intelligence, machine learning, and analytics is enhancing the functionality of school management software, enabling predictive insights, personalized learning experiences, and proactive intervention strategies.

From a regional perspective, Asia Pacific is emerging as a key growth engine for the school management software market, supported by large student populations, government initiatives for digital education, and rapid technological adoption. North America continues to lead in terms of market share, driven by early adoption, well-established educational infrastructure, and a strong focus on innovation. Europe follows closely, benefitting from increased investments in educational technology and a growing emphasis on administrative efficiency. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, as schools in these regions gradually transition to digital platforms to address administrative challenges and enhance the quality of education.

In the context of digital transformation, School Information Management Systems are becoming increasingly pivotal. These systems offer a centralized platform for managing student records, attendance, grades, and other critical data, ensuring that educational institutions can operate efficiently and effectively. By integrating various administrative functions, School Information Management Systems help reduce redundancy and streamline operations, allowing educators to focus more on teaching and less on paperw

The Metropolitan Development and Housing Agency in Nashville, Tennessee, received a National Institute of Justice grant to study the effectiveness of Nashville's Juvenile Court Truancy Reduction Program (TRP). The goals of the TRP were to increase attendance and to get children safely to and from school. While habitual truancy, also referred to as chronic absenteeism, was legally defined under the Juvenile Offender Act of the State of Tennessee as five or more aggregate, unexcused absences in the course of a school year, the TRP operationally defined students at risk of truancy as those who had three unexcused absences in a school year. The intent of TRP was to intervene before the student was adjudicated habitually truant, so once a student had a third unexcused absence, the child was placed on the TRP caseload. TRP staff would then intervene with a variety of services, including home visits, community advisory boards, a suspension school, and a summer program. The evaluation study was designed to test the following hypotheses: (1) students who participated in TRP would increase their attendance rates, and (2) students who participated in TRP and other community services that were part of the Public Housing Drug Elimination Program network would increase their attendance rates at higher rates than students who participated in TRP alone. The targeted population for this study consisted of child and youth residents from five of the six public housing communities that participated in TRP. These communities also represented the public housing communities with the highest crime rates in Nashville, and included five of the eight total family public housing developments there. All kindergarten through 8th-grade students from the targeted communities who began participating in TRP during the 1998-1999 or 1999-2000 school years were included in the study. The TRP served over 400 kindergarten through 8th-grade students during the two school years included in this study. Students who had all of the required data elements were included in the analyses. Required data elements included TRP entry date and school entry and exit dates. Students also had to have begun TRP during the study period. Variables include students' grade, gender, race, age, school enrollment date, TRP program entry date, bus eligibility, other program participation, attendance records for every school day during the two years of the study, and aggregated counts of attendance and truant behavior.

Violence against youth is a global issue; one form of youth victimization is school corporal punishment. We use baseline assessments from a cluster randomized controlled trial to examine the prevalence of school corporal punishment, by gender, and the relationship to levels of peer violence at school, parent corporal punishment, youth food security and youth academic performance and school attendance in Pakistan. Forty homogenous public schools in the urban city of Hyderabad, Pakistan were chosen for randomization into the trial evaluating a youth violence prevention intervention. 1752 6th graders, age 11–14 years, were selected as the target population. Since schools are segregated by gender in Pakistan, data are from interviews in 20 boys’ schools and 20 girls’ schools. Overall, 91.4% of boys and 60.9% of girls reported corporal punishment at school in the previous 4 weeks and 60.3% of boys had been physically punished at home in the past 4 weeks compared to 37.1% of girls. Structural equation modeling revealed one direct pathway for both boys and girls from food insecurity to corporal punishment at school while indirect pathways were mediated by depression, the number of days missed from school and school performance and for boys also by engagement in peer violence. Exposure to corporal punishment in school and from parents differs by gender, but in both boys and girls poverty in the form of food insecurity was an important risk factor, with the result that poorer children are victimized more by adults.

Abstract

The Government of Madagascar (GOM), through the Ministry of Population, Social Protection and Promotion of Women (MPSPPW) and the Social Development Fund (FID) is implementing a Human Development Cash Transfer (HDCT) program in partnership with the World Bank (WB). The program targets selected geographical areas and provides bi-monthly cash transfers to extreme poor households (bottom 30th percentile in the income distribution) with children through primary school age (0-10 years). The cash transfer is designed to provide both short-term income support and to leverage longer-term family investments in children’s human development, notably core elements of early childhood development, nutrition and formal education. A portion of the cash transfer is conditioned on regular primary school attendance while an unconditional transfer coupled with encouragement to attend child nutrition and development sessions is provided for households with younger children not yet in primary school. The cash transfer also accompanies various community leader-led informational modules on family well-being, health, nutrition, and sanitation. ideas42 is partnering with the World Bank’s Madagascar Social Protection Team to support the GOM in designing and implementing a rigorous impact evaluation that meets its policy needs by clarifying the effectiveness of the HDCT program. An important focus of the evaluation is in piloting certain community participation, behavioral science, and motivational interventions that may improve the effectiveness of the program and help guide its ultimate scalability.

Geographic coverage

51 communes across Madagascar

Analysis unit

Households

Universe

Extremely poor households (bottom 30th percentile of income) with children ages 0-10 (primary school age)

Kind of data

Sample survey data [ssd]

Sampling procedure

ideas42 is running this impact evaluation of a conditional cash transfer program in Madagascar for 51,000 households from 2016-2019. It is a three-level cluster randomization design. There are three intervention effects we want to measure:

1. The effect of the Cash Transfer (commune level)
2. The additional effect of the Mother Leader (ML) group program (village level)
3. The additional effect of the Nudges (village level)

4,485 households were sampled for the baseline survey. For the MDAT, 3,366 households were eligible (have at least one eligible child) and surveyed using MDAT. The endline will be the full 8,222 households, but the baseline enabled us do basic randomization checks and provides some information on the vulnerable population for the government before the 3-year experiment ends. Given these constraints, our approach to baseline sampling was as follows:

1. Sampled from all 309 treatment villages (surveyed 7-8 randomly selected eligible households per village).

2. For pure control, randomly sampled 65 villages total. This meant sampling 5 villages from each of the 13 control communes, with 9-10 people from each village. This is because cash is evaluated at the commune level – there are 110 control villages, and we did not necessarily need to go into all of them. Since there are 13 control communes, we needed to sample from about 5 villages from each commune, with 9-10 people from each village. That is roughly 600 people from 65 total villages. Other important notes are that four of the 13 control communes had only five eligible villages. One of the communes only had three villages, so five villages per commune was the only realistic sampling ceiling.

3. We randomly selected 3,000 eligible households and only did the MDAT (the early childhood cognitive development measure instrument) for households that had children ages 2-6. That said, once we had the household list for the baseline, we could pre-select with which of the children in a given household we could conduct the MDAT.

For absentee respondents/refusals/not found, field enumerators had a backup list of randomly selected households from which could replace the absentee household. Field followed order of the randomly generated backup list systematically to prevent the introduction of any bias into the sampling process.

For additional information on the sampling, please refer to the 'Human Development Cash Transfer Sampling Approach Brief' available for download.

Mode of data collection

Face-to-face [f2f]

Research instrument

The HDCT Baseline and Midline Surveys: The baseline and midline surveys were designed by pulling key questions used in other national-level surveys carried out in Madagascar by INSTAT (Institut National de laStatistique de Madagascar), as well as in other relevant surveys conducted in Africa (e.g., World Bank surveys). They covered topics such as household demographic information, education, food and non food-related household consumption, general household expenses, agricultural production, sources of household revenue, women’s empowerment, parenting practices and food security. The HDCT baseline survey was approved by the National Institute of Statistics (INSTAT) in charge of the technical secretariat of the CCISE (Committee of Coordination of the Statistical and Economic Information) in Madagascar before it was implemented. The baseline survey was published in English and French and is available to download. The midline survey is provided in French and is also available for download.

The Malawi Developmental Assessment Tool (MDAT): The MDAT (Gladstone et al 2010) is a child development assessment survey specifically designed for a rural African context and is publicly available for adaptation to multiple countries. We (1) adapted this tool to a Malagasy context, creating a Madagascar-specific tool that can be used by future researchers and early childhood development specialists to assess developmental status and (2) used this tool to conduct a baseline assessment of child developmental status for the HDCT program. The MDAT assesses child developmental status across four domains: gross motor, fine motor, language, and social abilities. The MDAT survey received a non-objection from the Ethical Committee of the Ministry of Public Health before implementation. This survey was also published in English and Malagasy and is available for download.

Cleaning operations

CAETIC développement, the survey implementing agency, led the data processing for this study.

Response rate

TMDH Baseline: 99.6% (4,484/4,485) MDAT: 99.9% (3,365/3,366); 2,629 surveys were completed

This single group pre and posttest study evaluated the feasibility of a new 10-week group-based motor skills enhancement intervention: “Moving Together,” and associated use of the Challenge assessment and Gait Outcomes Assessment List (GOAL). Participant attendance/completion and satisfaction with the assessments and intervention were evaluated, and a first estimate of associated motor skill-related changes obtained. Ten ambulatory children with cerebral palsy (7–14 years) and their parents participated. Ninety percent of Challenge sessions were attended and 82.5% of GOAL questionnaires completed. Program attendance was 83% overall. Satisfaction with assessments was high for the Challenge and moderate for the GOAL, and intervention satisfaction was high. Mean change scores (95% CI) post-intervention for the Challenge and GOAL were 4.2 (−11.4 to 3.1) and 3.6 (−14.4 to 4.0) points (/100) respectively. Challenge and GOAL use was feasible and appropriate for “MovingTogether” and associated with gains in motor skill performance and functional abilities.