Unpacking educational inequality in the NT
Professor Sven Silburn*
& Steve Guthridge**, John McKenzie*, Lilly Li** & Shu Li**
* Centre for Child Development and Education
Menzies School of Health Research, Darwin, NT
** Health Gains Planning
NT Department of Health, Darwin, NT
AIM
How can existing data be used to enable a
more integrated understanding of
educational inequality in the NT?
NAPLAN Year 3 Reading (2013)
48% of NT Indigenous students had NAPLAN scores
at or below the national minimum standard in 2013
Progress towards CtG targets:
NAPLAN Year 3 reading at or above NMS
100
% at or above NMS
90
Non-Indigenous (National)
80
70
60
On track to meet the CtG Target by 2016
Indigenous (National)
50
40
30
Indigenous (NT)
By 2018 the % of NT Indigenous children above NMS will
have doubled but this will still be far below the CTG target
20
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
1. How important is the current policy
focus on attendance?
Students’ attendance history:
Children born in the NT 1994-2004 (N=6,448)
Non-Indigenous students
% of expected attendance
Indigenous students
% of expected attendance
2. How much does “Place” matter in
shaping attendance and achievement?
Community socio-demographic differences:
% adults speaking English by % with yr 10 ed.
u
n
Relative influence of community factors
associated with remote school attendance
Mean number of people per bedroom
0.49
% Adults with year 10 education
0.14
% Adults who speak English only
0.11
Mean weekly household income
0.09
Community remoteness (ARIA)
0.08
0.05
% Population who are Indigenous
% Community SES (ICSEA)
% population aged < 15 years
0.03
0.01
3. How do early childhood development
outcomes shape subsequent school
achievement?
2012 NAPLAN Yr 3 Reading ( % < NMS)
Are AEDI outcomes associated with NAPLAN?
Indigenous
R2 linear
=0.789
2012 NAPLAN Yr 3 Reading ( % < NMS)
% of children with 2009 AEDI Total Score < 25th national %ile)
Non-Indigenous
R2 linear
=0.032
th
Relative influence of remote community factors
predictive of 2012 NAPLAN reading < NMS
Mean weekly household income
0.45
Mean number of people per bedroom
0.20
% Adults with year 10 education
0.14
Mean school attendance
% Adults who speak English only
% AEDI vulnerable (2009)
% population aged < 15 years
0.10
0.05
0.04
0.02
4. Do early-life health and socio-demographic
factors influence NAPLAN outcomes?
Individual child factors associated
with Indigenous Yr 3 reading < NMS
Multivariate logistic regression: Crude and adjusted risks for NAPLAN Yr 3 Reading below the National Minimum Standard (NMS)
Factor
Children
Crude
N=4,603
(100%) Odds Ratio
Adjusted
Odds Ratio
Primary carer’s education <year 10
2,022
(43.9%)
4.76
2.77
Age of mother at child’s birth <18yr
718
(15.6%)
1.95
1.92
Primary carers education = year 10
1,190
(25.8%)
2.16
1.78
Male gender
2,393
(51.9%)
1.31
1.40
Smoking in pregnancy
1,951
(42.3%)
1.03
1.36
581
(12.6%)
1.45
1,24
1,074
(23.3%)
1.03
1.36
609
(13.2%)
1.55
1.18
Low birth weight
First live birth
Gestation < 37 weeks
[NT Early Child Development Data-linkage Demonstration Study: Silburn, Lynch, Guthridge & McKenzie]
Relative importance of perinatal health and sociodemographic factors for Indigenous NAPLAN Yr 3 reading
Population Attributable Risk %
Population Attributable Risk is the reduction in incidence if the whole population were unexposed, comparing with actual exposure pattern.
5. How can we derive a more “holistic”
understanding of the key drivers of
educational disadvantage?
De-identified linkage of selected data
items from NT administrative datasets
Datasets already linked
Datasets to be linked
Summary
Addressing educational inequality in the NT requires
recognition that:
1. School attendance really matters
2. Levels of remoteness vary considerably
3. Community characteristics have significant influence
4. Early-life health & socio-demographic factors also matter
5. Linking child, family, community & school data will assist in
identifying key causal pathways and the best leverage
points for improving outcomes