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Simon Baker
- 1. Global rankings: measuring local impact Simon Baker Data Editor Times Higher Education
- 2. What do we measure? Portal Reputation survey Scopus Teaching reputation Staff to student ratio Doctorates to Bachelor awarded ratio Doctorates awarded to academic staff ratio Institution income to academic staff Research reputation Research income to academic staff ratio Paper to academic staff ratio Citation impact International student percentage International staff percentage International co-authorship Industry research income to academic staff Teaching Research Citations Industry income International outlook World University Ranking
- 3. Teaching Portal Reputation survey Teaching reputation Staff to student ratio Doctorates to Bachelor awarded ratio Doctorates awarded to academic staff ratio Institution income to academic staff Teaching World University Ranking
- 4. Research Portal Reputation survey Scopus Research reputation Research income to academic staff ratio Paper to academic staff ratio Research World University Ranking
- 6. Industry Income Portal Industry research income to academic staff Industry income World University Ranking
- 7. International Outlook Portal Scopus International student percentage International staff percentage International co-authorship International outlook World University Ranking
- 9. 0 10 20 30 40 50 60 70 80 90 Asean Australia China IndiaSouth Korea United Kingdom United States Average scores on five ranking "pillars" Teaching Research Citations Industry International
- 10. 0 5 10 15 20 25 30 35 40 45 50 Asean China India Japan South Korea Taiwan Average scores on international metrics Int students Int staff Int co-authorship
- 11. 0 10 20 30 40 50 60 70 80 90 Malaysia Indonesia Thailand China Japan South Korea United Kingdom Average scores by country in three areas International outlook Industry income Students to academic staff
- 12. Some THE metrics may link to ‘local’ impacts But rankings generally restricted by: • Metrics that cross borders (tend to be more research orientated) • Impacts that are measurable (qualitative analysis sometimes useful but makes comparison difficult) • Limited numbers (many of the institutions that do local ‘heavy lifting’ are not in the rankings)
- 13. What are ‘local impacts’ we might want to measure? • Universities’ contribution to regional and national economy • How universities improve skills in workforce • Employment rate and salary level of graduates • Contribution to social cohesion • Cultural impact of universities and their research • Transfer of knowledge from universities to industry
- 14. WSJ/THE US College Rankings (plus Japan and Europe) an example of ‘going local’
- 17. But can we go global with local data? • Current metrics in world rankings perhaps show it is possible (staff-student ratio) • But does subjectivity come much more into play? • For example, is it possible to compare inclusion in US with an emerging HE nation? • How about employment rates in places with vastly different economies? • Perhaps some kind of benchmarking is the answer, as used in UK HE data? Now over to you…..
Editor's Notes
- 1
- What the WUR measures and the sources. Total of 13 metrics weighted into five pillars. Only universities teaching undergraduates and passing research publication threshold (1,000 articles between 2012 and 2016).
- Universities in the World University Rankings mapped
- These averages are the medians so Singapore skewing effect is minimised
- These averages are the medians so Singapore skewing effect is minimised
- Again these are medians. We can use the rankings - despite the limitations - to identify impacts that may arguably be “more local”.
- Just a few examples, some easily measured, others not so.
- THE is moving towards trying to measure things that have more of a local impact through teaching-centred rankings.
- This can lead to some very interesting ”local” insights. The following slides show distribution of scores in two metrics in the US College Ranking by different university groups. On salary value-added the THE data team uses statistical modelling to create an expected graduate salary for each college based on a wide range of factors, such as the make-up of its student body and the characteristics of the institution. The ranking looks at how far the college either exceeds expectations in getting students higher average salaries than one would predict based on the student body and its characteristics, or falls below what is expected.
- Inclusion is based on proportion of students who are first-generation students (the first person in their family to go to college) as reported in the College Scorecard. We also look at the proportion of students who receive Pell Grants (paid to those in need of financial support), as reported in IPEDS.