1. 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

2. AIM How can existing data be used to enable a more integrated understanding of educational inequality in the NT?

3. NAPLAN Year 3 Reading (2013) 48% of NT Indigenous students had NAPLAN scores at or below the national minimum standard in 2013

4. Progress towards CtG targets: NAPLAN Year 3 reading at or above NMS % at or above NMS On track to meet the CtG Target by 2016 Non-Indigenous (National) Indigenous (National) Indigenous (NT) By 2018 the % of NT Indigenous children above NMS will have doubled but this will still be far below the CTG target

5. 1. How important is the current policy focus on attendance?

6. Students’ attendance history: Children born in the NT 1994-2004 (N=6,448) % of expected attendance % of expected attendance Non-Indigenous students Indigenous students

7. 2. How much does “Place” matter in shaping attendance and achievement?

8. Community socio-demographic differences: % adults speaking English by % with yr 10 ed. u n

9. Relative influence of community factors associated with remote school attendance Mean weekly household income % Adults with year 10 education % population aged < 15 years Mean number of people per bedroom % Adults who speak English only Community remoteness (ARIA) % Population who are Indigenous % Community SES (ICSEA) 0.49 0.14 0.11 0.09 0.08 0.05 0.03 0.01

10. 3. How do early childhood development outcomes shape subsequent school achievement?

11. Are AEDI outcomes associated with NAPLAN? 2012 NAPLAN Yr 3 Reading ( % < NMS) Indigenous % of children with 2009 AEDI Total Score < 25 th national %ile) Non-Indigenous R 2 linear =0.789 R 2 linear =0.032 % of children with 2009 AEDI Total Score < 25 th national %ile)

12. Relative influence of remote community factors predictive of 2012 NAPLAN reading < NMS Mean weekly household income Mean number of people per bedroom % Adults with year 10 education Mean school attendance % Adults who speak English only % AEDI vulnerable (2009) % population aged < 15 years 0.45 0.20 0.14 0.10 0.05 0.04 0.02

13. 4. Do early-life health and socio-demographic factors influence NAPLAN outcomes?

14. Individual child factors associated with Indigenous Yr 3 reading < NMS FactorChildren N=4,603 (100%) Crude Odds Ratio Adjusted Odds Ratio Primary carer’s education <year 102,022 (43.9%)4.762.77 Age of mother at child’s birth <18yr718 (15.6%)1.951.92 Primary carers education = year 101,190 (25.8%)2.161.78 Male gender2,393 (51.9%)1.311.40 Smoking in pregnancy1,951 (42.3%)1.031.36 Low birth weight581 (12.6%)1.451,24 First live birth1,074 (23.3%)1.031.36 Gestation < 37 weeks609 (13.2%)1.551.18 Multivariate logistic regression: Crude and adjusted risks for NAPLAN Yr 3 Reading below the National Minimum Standard (NMS) [NT Early Child Development Data-linkage Demonstration Study: Silburn, Lynch, Guthridge & McKenzie]

15. Relative importance of perinatal health and socio- demographic 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.

16. 5. How can we derive a more “holistic” understanding of the key drivers of educational disadvantage?

17. De-identified linkage of selected data items from NT administrative datasets Datasets already linked Datasets to be linked

18. 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