Mapping schools' NAPLAN results: a spatial inequality of school outcomes in Australia

Abstract

This article identifies spatial dimensions of educational outcomes using maps of the 2016 Grade 5 reading results for Australia's National Assessment Program–Literacy and Numeracy for all Australian schools. A geographical information system (GIS) was used to overlay schools' results onto suburbs' advantage or disadvantage to visualise spatial patterns. We then examined the extent to which school results “cluster” in socio‐economically advantaged and disadvantaged suburbs and considered the consistency of spatial patterns for results across major cities. That work illustrates both how GIS can foreground educational inequality and how “the spatial” is more than corollary for student socio‐economic status. Results show substantial differences between urban and remote areas and towns of different size. Maps of cities visualise spatial “clustering” patterns of school results, with most schools in advantaged suburbs having high results and almost no schools in disadvantaged suburbs having high results. Educational outcomes strongly align to local socio‐demographic characteristics, and parallel host communities’ levels of advantage or disadvantage. Differences between public and private schools are less significant than within‐sector differences for schools in advantaged or disadvantaged locales. Patterns in all cities are consistent—schools in advantaged suburbs predominantly have high results, whereas non‐government schools generally perform better than government schools in disadvantaged suburbs. Most concerning is the persistent and increasing trajectory of results in advantaged, and more so in disadvantaged suburbs, of all cities since the first National Assessment Program–Literacy and Numeracy in 2008. Ameliorating spatial inequality between primary schools is one of the greatest challenges for Australians.

The Academic Development Study of Australian Twins (ADSAT): Research Aims and Design

“…Extant educational research in Australia suggests that environmental influences shared equally by twins in a pair (shared environment in the terminology of behavior genetics) do contribute to the academic performance of students. These shared environment factors are similar to those that have been found in international research and include socioeconomic status (SES) and related aspects of family life, preschool attendance, school effects and location, for example, (Buckingham et al, 2014;Marks 2015aMarks , 2015bPerry & McConney, 2013;Smith et al, 2019;Warren & Haisken-Denew, 2013). Nonetheless, behavior genetic research designs that investigate the extent to which shared environment contributes to variation in achievement across multiple longitudinal time points, and separate from genetic factors and nonshared environment, had not been widely applied in the Australian system before ADSAT was established.…”

“A Passion and Enthusiasm to Bring out the Best in All”: Regional Candidate Teacher Motivations

“…A consideration of the research underpinning this study is the focus on how to attract, support and retain teacher diversity in regional schools (Handal, Watson, Petocz, & Maher, 2013;Jenkins & Cornish, 2015). The need for teacher quality in regional and remote locations has been well documented in the literature (Arnold et al, 2005;Kelly & Fogarty, 2015;Plunkett & Dyson, 2011;Smith et al, 2019). However, providing a career path for new teachers in regional areas is complex and challenging with issues such as: geographic isolation, difficulty in securing permanent jobs in favoured locations, difficulties in staffing in hard to staff schools, cultural differences faced by the urban trained teacher, high unemployment and low socioeconomic conditions.…”

“…The impact is that an increasing number of regional young people choose not to work in regional and remote locations (Davies, 2008;Jenkins & Cornish, 2015). This is a concern for regional areas where there is often a greater need for quality teachers, considered crucial in addressing inequalities in student outcomes (ACARA, 2016;Smith, Parr, & Muhidin, 2019).…”

Who’s striking, and who’s not? Avoiding and acknowledging bias in youth climate activism research

“…Not only does this put excess strain on non-government school administrators, but repeatedly engaging with school students from non-government schools faces the same risk as online or eventsampling: that an unrepresentative group of students is presented as normative. Factors such as socio-economic status will influence the school life of a student, and individual schools cannot be expected to simply ameliorate this influence on their own (Smith, Parr, & Muhidin, 2019).…”