NSW public schools have defined local enrolment areas, meaning that every child is entitled to enrol in a particular school based on his or her residential address. Every public school reserves enough places within their school for students in their local enrolment area. Disclaimer: Due to the evolving nature of school information and local enrolment areas, no responsibility can be taken by the NSW Department of Education, or any of its associated departments, if information is relied upon. For example, but not limited to, real estate purchases or rentals where the school intake zone data is used as a reference source. School catchments areas can change for multiple reasons. These include schools opening and closing, and changes in population demographics, for example. For more information visit https://data.cese.nsw.gov.au/data/dataset/ffd4298f-c4b2-48de-a939-e8ba8cc1b70a.

The Department of Education proposes to fundamentally reform school infrastructure planning and delivery to meet the future demand for student places, and to overcome chronic under-investment for much of the last decade. To do this, it will need to spend much more than it has been receiving to date. These are the main findings of a report released today by Acting NSW Auditor-General, Ian Goodwin.

BackgroundSchool readiness is a measure of a child’s cognitive, social, and emotional readiness to begin formal schooling. Children with low school readiness need additional support from schools for learning, developing required social and academic skills, and catching-up with their school-ready peers. This study aims to identify the most significant risk factors associated with low school readiness using linked routine data for children in Wales.MethodThis was a longitudinal cohort study using linked data. The cohort comprises of children who completed the Foundation Phase assessment between 2012 and 2018. Individuals were identified by linking Welsh Demographic Service and Pre16 Education Attainment datasets. School readiness was assessed via the binary outcome of the Foundation Phase assessment (achieved/not achieved). This study used multivariable logistic regression model and a decision tree to identify and weight the most important risk factors associated with low school readiness.ResultsIn order of importance, logistic regression identified maternal learning difficulties (adjusted odds ratio 5.35(95% confidence interval 3.97–7.22)), childhood epilepsy (2.95(2.39–3.66)), very low birth weight (2.24(1.86–2.70), being a boy (2.11(2.04–2.19)), being on free school meals (1.85(1.78–1.93)), living in the most deprived areas (1.67(1.57–1.77)), maternal death (1.47(1.09–1.98)), and maternal diabetes (1.46(1.23–1.78)) as factors associated with low school readiness. Using a decision tree, eligibility for free school meals, being a boy, absence/low attendance at school, being born late in the academic year, being a low birthweight child, and not being breastfed were factors which were associated with low school readiness.ConclusionThis work suggests that public health interventions focusing on children who are: boys, living in deprived areas, have poor early years attendance, have parents with learning difficulties, have parents with an illness or have illnesses themselves, would make the most difference to school readiness in the population.

Study information

The sample included in this dataset represents five children who participated in a number line intervention study. Originally six children were included in the study, but one of them fulfilled the criterion for exclusion after missing several consecutive sessions. Thus, their data is not included in the dataset.

All participants were currently attending Year 1 of primary school at an independent school in New South Wales, Australia. For children to be able to eligible to participate they had to present with low mathematics achievement by performing at or below the 25th percentile in the Maths Problem Solving and/or Numerical Operations subtests from the Wechsler Individual Achievement Test III (WIAT III A & NZ, Wechsler, 2016). Participants were excluded from participating if, as reported by their parents, they have any other diagnosed disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, intellectual disability, developmental language disorder, cerebral palsy or uncorrected sensory disorders.

The study followed a multiple baseline case series design, with a baseline phase, a treatment phase, and a post-treatment phase. The baseline phase varied between two and three measurement points, the treatment phase varied between four and seven measurement points, and all participants had 1 post-treatment measurement point.

The number of measurement points were distributed across participants as follows:

Participant 1 – 3 baseline, 6 treatment, 1 post-treatment

Participant 3 – 2 baseline, 7 treatment, 1 post-treatment

Participant 5 – 2 baseline, 5 treatment, 1 post-treatment

Participant 6 – 3 baseline, 4 treatment, 1 post-treatment

Participant 7 – 2 baseline, 5 treatment, 1 post-treatment

In each session across all three phases children were assessed in their performance on a number line estimation task, a single-digit computation task, a multi-digit computation task, a dot comparison task and a number comparison task. Furthermore, during the treatment phase, all children completed the intervention task after these assessments. The order of the assessment tasks varied randomly between sessions.

Measures

Number Line Estimation. Children completed a computerised bounded number line task (0-100). The number line is presented in the middle of the screen, and the target number is presented above the start point of the number line to avoid signalling the midpoint (Dackermann et al., 2018). Target numbers included two non-overlapping sets (trained and untrained) of 30 items each. Untrained items were assessed on all phases of the study. Trained items were assessed independent of the intervention during baseline and post-treatment phases, and performance on the intervention is used to index performance on the trained set during the treatment phase. Within each set, numbers were equally distributed throughout the number range, with three items within each ten (0-10, 11-20, 21-30, etc.). Target numbers were presented in random order. Participants did not receive performance-based feedback. Accuracy is indexed by percent absolute error (PAE) [(number estimated - target number)/ scale of number line] x100.

Single-Digit Computation. The task included ten additions with single-digit addends (1-9) and single-digit results (2-9). The order was counterbalanced so that half of the additions present the lowest addend first (e.g., 3 + 5) and half of the additions present the highest addend first (e.g., 6 + 3). This task also included ten subtractions with single-digit minuends (3-9), subtrahends (1-6) and differences (1-6). The items were presented horizontally on the screen accompanied by a sound and participants were required to give a verbal response. Participants did not receive performance-based feedback. Performance on this task was indexed by item-based accuracy.

Multi-digit computational estimation. The task included eight additions and eight subtractions presented with double-digit numbers and three response options. None of the response options represent the correct result. Participants were asked to select the option that was closest to the correct result. In half of the items the calculation involved two double-digit numbers, and in the other half one double and one single digit number. The distance between the correct response option and the exact result of the calculation was two for half of the trials and three for the other half. The calculation was presented vertically on the screen with the three options shown below. The calculations remained on the screen until participants responded by clicking on one of the options on the screen. Participants did not receive performance-based feedback. Performance on this task is measured by item-based accuracy.

Dot Comparison and Number Comparison. Both tasks included the same 20 items, which were presented twice, counterbalancing left and right presentation. Magnitudes to be compared were between 5 and 99, with four items for each of the following ratios: .91, .83, .77, .71, .67. Both quantities were presented horizontally side by side, and participants were instructed to press one of two keys (F or J), as quickly as possible, to indicate the largest one. Items were presented in random order and participants did not receive performance-based feedback. In the non-symbolic comparison task (dot comparison) the two sets of dots remained on the screen for a maximum of two seconds (to prevent counting). Overall area and convex hull for both sets of dots is kept constant following Guillaume et al. (2020). In the symbolic comparison task (Arabic numbers), the numbers remained on the screen until a response was given. Performance on both tasks was indexed by accuracy.

The Number Line Intervention

During the intervention sessions, participants estimated the position of 30 Arabic numbers in a 0-100 bounded number line. As a form of feedback, within each item, the participants’ estimate remained visible, and the correct position of the target number appeared on the number line. When the estimate’s PAE was lower than 2.5, a message appeared on the screen that read “Excellent job”, when PAE was between 2.5 and 5 the message read “Well done, so close! and when PAE was higher than 5 the message read “Good try!” Numbers were presented in random order.

Variables in the dataset

Age = age in ‘years, months’ at the start of the study

Sex = female/male/non-binary or third gender/prefer not to say (as reported by parents)

Math_Problem_Solving_raw = Raw score on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Num_Ops_Raw = Raw score on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

The remaining variables refer to participants’ performance on the study tasks. Each variable name is composed by three sections. The first one refers to the phase and session. For example, Base1 refers to the first measurement point of the baseline phase, Treat1 to the first measurement point on the treatment phase, and post1 to the first measurement point on the post-treatment phase.

The second part of the variable name refers to the task, as follows:

DC = dot comparison

SDC = single-digit computation

NLE_UT = number line estimation (untrained set)

NLE_T= number line estimation (trained set)

CE = multidigit computational estimation

NC = number comparison

The final part of the variable name refers to the type of measure being used (i.e., acc = total correct responses and pae = percent absolute error).

Thus, variable Base2_NC_acc corresponds to accuracy on the number comparison task during the second measurement point of the baseline phase and Treat3_NLE_UT_pae refers to the percent absolute error on the untrained set of the number line task during the third session of the Treatment phase.

This dataset tracks annual distribution of students across grade levels in North Wales Elementary School

This dataset tracks annual total students amount from 1987 to 2023 for North Wales Elementary School

Historical Dataset of North Wales Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1992-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (2003-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2003-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022)

This dataset tracks annual total classroom teachers amount from 1987 to 2023 for North Wales Elementary School

This dataset tracks annual asian student percentage from 1991 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual student-teacher ratio from 1987 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual black student percentage from 1991 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual white student percentage from 1991 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual hispanic student percentage from 1992 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual overall school rank from 2011 to 2022 for North Wales Elementary School

This dataset tracks annual diversity score from 1991 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual reading and language arts proficiency from 2011 to 2022 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual two or more races student percentage from 2013 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District

This dataset tracks annual reduced-price lunch eligibility from 2003 to 2023 for North Wales Elementary School vs. Pennsylvania and North Penn School District