Introduction to the SLTA Project Dr Rosa Spencer

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Introduction to the SLTA Project Dr Rosa Spencer Academic Development Officer (Awards and Recognition)

Introduction to the SLTA Project Dr Rosa Spencer Academic Development Officer (Awards and Recognition) November 2013

SLTAs and the HEA “We are committed to improving the student learning experience by

SLTAs and the HEA “We are committed to improving the student learning experience by raising the status of teaching…” • Vision: For UK higher education to be recognised and valued by students, staff and wider society for its provision of consistently excellent learning and teaching • Priority: recognise, reward, and accredit excellent teaching 2

Origins of the SLTAs • Local initiatives at Heriot-Watt University and the University of

Origins of the SLTAs • Local initiatives at Heriot-Watt University and the University of Edinburgh • 2009/10 - HEA and NUS Scotland developed and supported a small pilot-scheme • 2010/11 - Expanded to 11 Scottish HEI and Student Association partnerships • 2011 Website launched: www. student-ledteachingawards. co. uk • 2011/12 - extended the project by opening to institutions across the UK: 21 schemes supported 3

SLTA data and teaching & learning SLTAs helping to dispel myth that student-led awards

SLTA data and teaching & learning SLTAs helping to dispel myth that student-led awards are a popularity contest Data shows • Value in students identifying what good teaching practice is • Students identifying qualities they value in learning support 4

2011 -12 Pilot data o HEA Associates – Prof. Sue Thompson and Dr Elena

2011 -12 Pilot data o HEA Associates – Prof. Sue Thompson and Dr Elena Zaitseva o Pilot set of institutions o Organised into categories for analysis: • Teaching (612 720 words) • Personal Tutor (74 022 words) • Assessment and Feedback (44 313 words) • Administrative and Support Staff (28 517) Analysed: 759 572 words 5

Methodology o Utilised text mining software: ‘Leximancer’ • a specialist analytics software for unstructured

Methodology o Utilised text mining software: ‘Leximancer’ • a specialist analytics software for unstructured qualitative textual data • the software maps concepts, themes and their associated relationships from a body of text • concepts emerge from the text 6

Some general observations • Great teachers facilitate active learning • Focus on lectures •

Some general observations • Great teachers facilitate active learning • Focus on lectures • Longitudinal support • Impact on employability • Majority of nominations see excellent teaching through lens of student-centred approach • Little on assessment • Few references to student feeling they were part of a learning partnership 7

Things to consider Using the data to build an evidence base of student perceptions

Things to consider Using the data to build an evidence base of student perceptions of excellent teaching and learning • Filling gaps in the data • Demographics of staff nominated • Demographics of students nominating • What data you want to collect? • Wording of award categories may affect results • How will you use the data? • Issues of anonymity 8

Using your data Please tell us how you’ve used your data • How has

Using your data Please tell us how you’ve used your data • How has it helped facilitate dialogue on excellent T&L? • Have you worked in partnership with university staff/committees • To disseminate data? • To use data to inform policy and practice? • Have there been any unexpected outcomes? 9

The broader context: links and value o Positioning SLTA initiative in the wider HEA

The broader context: links and value o Positioning SLTA initiative in the wider HEA work • Reward and Recognition • Teaching Excellence • Change Programme 10

Questions? 11

Questions? 11