1. Learning Gain: Evaluation, Evidence and Enhancement
Dr Camille B. Kandiko
King’s College London
Learning and Teaching Conference 21 March 2018
Newcastle University #ncllt18

2. Learning gain metrics and enhancement
Overview
Policy context
Learning gain project
Learning gain metrics and enhancement

3. Policy Context I

4. Students
<Bored students in lecture theatres>

5. Graduates
<Non-graduate jobs>

6. <David Willetts, Two Brains> <Browne Review of Higher Education> <Academically Adrift>

7. ‘Academically Adrift’

8. ‘lamentable’
<Jo Johnson>
‘patchy’
‘value for money’

9. Teaching Excellence Framework
Not really about ‘teaching’ per se
‘Teaching Excellence and Student Outcomes Framework’
Focus on impact and evaluation

11. <Sam Gyimah> <moneysupermarket. com> <moneysupermarket
<Sam Gyimah> <moneysupermarket.com> <moneysupermarket.com/travel_insurance>

12. HEFCE  Office for Students
students from all backgrounds are able to enter and succeed in higher education. Students should be able to make informed choices about their studies, with access to a diverse range of provision which meets their needs.
students have access to high quality teaching and learning – whatever, wherever and however they are studying.
students’ interests are protected, and that they receive value for money.
students are well prepared for life after graduation, gaining qualifications which offer rewarding opportunities for postgraduate study or employment, aligned with the needs of employers.

13. Learning gain project II

14. Learning Gain: Challenges
Student engagement with tests and surveys
Motivating students to invest in tests that don’t contribute to assessment
Context of English higher education
Discipline bias in standardised tests
Comparability of some entry and exit measures
Reliability of student self-reports
Data protection, data sharing, research ethics
But closest current proxies for learning are satisfaction surveys and graduate salaries…

15. HEFCE Learning Gain
HEFCE funding 13 mixed method projects involving 70 institutions over three years, using:
Learner analytics/Grades
Self-reported surveys
Standardised tests
Multiple measures of a specific theme
National Mixed Methodology Learning Gain Project (NMMLGP)
Higher Education Learning Gain Analysis (HELGA)

16. What is the purpose of higher education?
What to measure
What is the purpose of higher education?
What is valued?
What are student expectations of higher education?
What should graduates know and be able to do?

17. Input and entry measures
Entry qualifications and background characteristics Differences across the sector <Lack of level playing field>

18. Measures I Affective Transition experience Self efficacy Well-being
Disposition to learning
Confidence
Resilience
Satisfaction

19. Measures II Behavioural Student engagement
Placements/ work-based learning
Employability experiences
Co-curricular activities
Skills self-assessment
VLE engagement
Learner analytics

20. Measures III Cognitive General cognitive gain
Disciplinary cognitive gain
Critical Reasoning Skills
Situational judgement
Research methods

21. Output and outcome measures
What have your students gained? Grades, progression Employability skills Affective Behavioural Cognitive Other outcome measures Back to purposes… Is it critical thinking, generic graduate skills, disciplinary mastery, developing employability or something more holistic?

22. Learning gain metrics and enhancement
III

23. Uses of learning gain metrics
Improve teaching and learning
Personalised approaches to learning
Learning analytics
Pedagogy and curriculum design
Contribute to student success
Design interventions & support
Evidence-based ‘nudge’ behaviours
Predict student outcomes
Marketing, admissions

24. Learning analytics and enhancement
Shift from teaching to learning
Culture of evidence and impact
How is data (qualitative and quantitative) used in enhancement
Gain credibility
Raise awareness
Target support
Link individual practice with teaching teams and courses
From ‘module-mania’ to larger curriculum focus
How do you access, understand, interpret and use data?
Who do your new partners in the institution need to be?

25. Learning gain and student voice
Opportunities for students to be part of:
Defining ‘value’
Data gathering
Data analysis
Data sharing
‘Actioning’
Enhancement
Feedback and follow-up

26. © 2015 King’s College London. All rights reserved
Dr Camille B. Kandiko
© 2015 King’s College London. All rights reserved