General presentation of EUA
Yann-Maêl Bideau DG EAC
Alliances for innovation in the HE&R sector
each alliance is different and test different models of european models
4 flagships: broader the uropean univerity initiative to 60 alliances by mid-2024
budget 1 billon euros from Horizon 2020-2021 + HOrizon EUrope
- going beyond existing partnerships
- the last erasmums+ call closed in Marsh 22
- 52 proposals from accross europe
flagship : possible legal status , joint degrees
need: monitoring tools to advise +build strategies
HE&R monitoring tools : Euro grauates, euro rank and ETER
data focused HE&R sector observatory : https://www.eoslhe.eu/ ?
streamlining synergies between kowledge anlaysis tools
- monitor the implementation od the stategy (inclusion, emplybailities, progess in digital an green skills)
- support data needs of HE&R institutions + upgrading thier intellgence, mess burdensome data collection
- promote competitiveness of HE1R in EUorpe + strengthen performance
A mapping study will be launched to determine data needs linked to the EU univeristy is needed
what indicators are missing?
the study will run unti the end of the year
ETER Dataset
Andrea B. (univ pizza, founder of the european tertiary education register (ETER))
micro-data on HE&R : at the level of individual institutions
EUropean University initiative(EUI) : data already avaialble and the missing indicators needed to follow the long terme evolution of the evolution of HE1R in EUrope.
Profile of member EUI institutions: data on university size, subject, research orientation (number of phds), internationalization
– the most complete data in ETER is given by students
– the percentage of extended STEM is roughly the same in member as in non-member universities –> the alliance has been able to attract students regardless of background
– no data on publications, but could be integrated
– number of PhD students is considered a measure of research intensity –> the toal number of PhD students enrolled in universities in alliances exceeds the students enrolled in non-member universities –> member students are engaged in long-term transformation of society through research and innovation
– the presence of foreign students is larger in universities members of alliances
what could be done? Large opportunity to combine data
geographical level anlysis
intra-alliance analysis
integration with other data (publications)
linked ETER data with EUI data
The Policy brief is available here?https://www.risis2.eu/2022/05/26/risis-eter-dataset-data-and-indicator-needs-for-the-european-university-initiative/
Roundtable
How do you interpret the evidence on differences and similarities between universities?
What kind of indicators would you need?
Michael Murphy (EUA president)
the importance of bringing data into one place for efficiency
autonomy score card : provides info about the conditions under which universities work and promote collaborations between EU universities –> currently being updated
countries have been doing several things:
allowed the use of more English
updating accreditation process
:warning: governance is a throwing up challenges currently that need to be adressed
project NewLead on university leadership capacity in Europe
It comes as no surprise that:
- universities in the alliance are larger (3 times larger), because of the ressources it takes to particiapte.
- that research intensive institutions collaborate more (because of better insentives historically, like Erasmus)
- about discipline mixity : the university sector operates in an integrated space between research, innovation, but administratively this has been hard to take into account
- it’s a big strategic decision to engage in an alliance, so when there is already a history of international partnerships it’s easier
Ben Jongbloed
the integration is being explored between 2 databases using an institutional identifier to link the data to combine multi-rank data with eter data
the eter data is mostly focused on input data (descriptive) but if you combine it, you get a richer picture of the institutions in and out of the alliances
diversity is more than taking a look at the averages of both groups : other measures of dispersions should be looked at to see other differences between them
the data we saw was only from PhD granting institutions : this is strange because there is more to EU HE1R institutions –> that would be another way of showing diversity
we could also look at the differenecs within the member group :
evaluating the alliances has t obe done according to their different goals, which depend on the alliances
they could then be compared to non-member institutions to see if the EU funding had an impact on the alliance institution
what have the institutions achieved using the EU funds? is that achievement based on the fact that they are working together in an alliance? when do we have to start measuring that performance?
integrating qualitative data could be an opportunity for the future when we’re asked to evaluate these HE&R alliances.
Sebastian Stride
data tends to benefit the wealthy –> for smaller alliances they had a harder time because they had less resources
this is a serious point because data such as publication networks can lead to favoring leading institutions
ex : eastern european institutions
one size fits all doesn’t work for data or alliances
the rectors of these universities publically support these alliances but they look to the shangai ranking not eter (which is a political value)
however, with consistent data over 5-10 years alliances can better track their progress
comparing data requires shared semantics –> a huge problem in EU
a general propsal : a two-track university alliance approach
one that allowed universties to be members of many alliances
one than is more resticted to a more transformation al alliance that aims to replace existing process by an integrated eu-level one
in teh first case, classical datasets can be used to evaluated and comapre performance
in the second, theese are of litte relevance because the structures would be truely inrtegrated and obtaining funding in non-competitive mecanisms
data is only useful if it can be compared (alligning semantics)
comaprison is counter productive is it used to stnadardise practices between eu univeristies
Q&A
Emily Palmer
Ludovic Thilly, Coimbra Group
groups like EU WAY have high-level interactions have created a new type of alliances that are complementary
M. Murphy
th EU is pursuing the concept of “distributed excellence”, committed to ensuring that all parts of Eu are successful as opposed to the US. We must measure and demonstrate that it is working. ideally we would have a randomized control trial, but we can’t.