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Explore the impact of poverty on student attainment in Scotland and the importance of using data effectively. Discover how data can help identify disparities, foster inquiry, and improve teaching practices. Learn about the dangers of high-stakes data and the characteristics of useful data. Find out how timely and thoughtful data discussions can empower teachers and enhance student learning in a self-improving education system.
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Breaking the link between attainment and deprivationEdinburgh, June 5th 2015 Sue Ellis, Strathclyde University
Average school-leaver attainment by area deprivation in Scotland
Poverty: Scotland’s Landscape • We need to take account of: • The geography of poverty in Scotland • Almost two thirds of pupils in poverty attend schools serving relatively affluent neighbourhoods • The attainment gap exists within every school - must be addressed by every school • The policy climate/process in Scotland • Devolved, premised on a single outcome, on difference and on teacher professional judgement
Useful Data, Well Used … • Data can: • show which groups are served well/not well by the system, identify disproportionate impacts & possible issues • help professionals identify and explore aspects of pupil performance that matter for progress • prompt conversations about possible ways forward for individuals/groups & systems; help determine goals and monitor progress
Useful data, well used • It can also: • foster a culture of inquiry • ground creative teaching/ potential solutions in clear evidence & a theory of change • aid communication within an organisation • identify local examples of highly effective practice, so that all can learn from them.
What to avoid: the dangers of high-stakes data • Enforces, centralised, unresponsive policy implementations • Skews delivery priorities towards narrow, skills based curricula targets and groups • De-professionalizes teachers & alienates the best –loss of agency • Puts children under pressure • Diverts resources to admin/compliance systems • Polarises the system - less equity (‘sink/star’ schools) • Quickly becomes unreliable - Norfolk, Philadelphia, Atlanta….
What Sort of Data is Useful? • Reliable • Cost efficient • Experiences & beliefs within the system • Attainment on key aspects that matter for progress, not general levels • Quick and easy to administer/analyse • Locally marked and owned • Criterion referenced –tends to skew attention to atomistic criteria – although proxy-measures • Standardised – allows comparisons across groups and over time
What matters are the conversations that spring from data… Timing, implementation & knowledge - creating narratives of good data use • Mobilize teacher knowledge & know-how • linked to the data • timely arrival, alongside the data • fostering a policy of recognition - categories that may need help / what may work –prof. narratives • Local Control - school/ teacher decisions from a central bank • WHAT data • WHEN useful • HOW used to enhance teaching and learning
A self-improving system • robust professional knowledge • Grounded conversations that embrace complexity • political / media restraint & (re)education • careful implementation • A system that works • in Scotland • for Scotland