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Harmonising data semantics of rankings
Results from Italy & Flanders
Hanne PoelmansSadia Vancauwenbergh ECOOM-Hasselt, Hasselt University, Belgium
Luciana SacchettiStefano PiazzaAMS Università di Bologna, Italy
Introduction university rankings
Visibility
Bench-marking
Policy formation
universityrankings
Introduction data semantics
“Mouse”
“Apple”
Introduction data semantics
Example “academic staff”: ranking organisations
Times Higher Education U-Multirank QSAcademic staff Academic staff Faculty staff
= staff employed in an academic post (e.g., lecturer, reader, professor)
EXCLUDEnon-teaching “fellows”,researchers (only doing research), postdoctoral researchers
= personnel whose primary assignment is instruction, research or public service
= staff who are responsible for planning, directing and undertaking academic teaching only, research only or both academic teaching and research within HEI.
teaching teaching + research teaching + research≠
≠
=
=
The Flemish ranking initiative
Interuniversity working group consisting of 4 HEIs in Flanders (Belgium)concerned with semantic harmonisation of university ranking indicators.
Participants
Goals- Increase data collection efficiency and quality- Create correctly comparable ranking data in Flanders- Improve branding and visibility of the Flemish HEI
The Italian ranking project72 Italian HEIs working group on Global rankings, a CRUI initiative.Coordinators
2018 BENCHMARK ACTIVITIESCRUI Universities QS
2019THE
2019GREENMETRIC
2018Ranked 28 39 25
Not ranked 45 33 47
Provide datafor benchmark
28/72 35/72 15/72
Results Times Higher Education rankingThe harmonisation process
EMILIA R. STUDENT RESEARCH STAFF ACADEMIC STAFFTo include Regular
(undergraduate,graduate, PhD,Professional Mastersprograms,Specialization Schools)
regular = student enrolledfor less than or equal tothe legal length of thedegree program
Research fellow Full professor,Associate professor,Senior AssistantProfessor (tenured),Junior assistant-professor (fixed-term), Seniorassistant professor(fixed-term)
To exclude Non regular student,summer school
Academic staff Research fellow,Adjunct professor(fix-term)
Results Times Higher Education rankingThe harmonisation process
FLEMISH INITIATIVE STUDENT RESEARCH STAFF ACADEMIC STAFF
To include Regular (undergraduate, graduate, PhD,Professional Masters programs,Specialization Schools)
regular = student enrolled to an entire degree program (not singlecourses)
Research fellow (post-doc)
Full professor, Associate professor, Senior Assistant Professor (tenured), Junior assistant-professor (fixed-term), Senior assistant professor (fixed-term)
To exclude Non regular student, summer school, exchange students, postgraduate courses
Academic staff Research fellow,Adjunct professor (fix-term)
0
500
1000
1500
2000
2500
3000
univ1 univ2 univ3 univ4 Mean
Academic staff Emilia R.
2018 not harmonised 2019 harmonised
Results Times Higher Education rankingEffect of harmonisation
0
200
400
600
800
1000
1200
univ1 univ2 univ3 univ4 Mean
Academic staff Flanders
2018 not harmonised 2019 harmonised
-0,1% -2,6% +5,1% -64,6% -17,3%-30,7% +11,8% -38,9% +1,8% -9,1%
0
5000
10000
15000
20000
25000
30000
35000
40000
univ1 univ2 univ3 univ4 Mean
Students Flanders
2018 not harmonised 2019 harmonised
-6%
0
10000
20000
30000
40000
50000
60000
70000
80000
univ1 univ2 univ3 univ4 Mean
Students Emilia R.
2018 not harmonised 2019 harmonised
-13,2% +4,2% -0,2% -3,6% +7,5% -22,2% +0,2% +4,0% +0,6%
Results Times Higher Education rankingEffect of harmonisation
0
10
20
30
40
50
60
70
80
90
100
Teaching Research Citations Industryincome
Internat.outlook
Overall
Univ Mean Flanders
2018 not harmonised 2019 harmonised
Results Times Higher Education rankingPillar indicators (scores)
0
10
20
30
40
50
60
70
80
90
100
Teaching Research Citations Industryincome
Internat.outlook
Overall
Univ Mean Emilia R.
2018 not harmonised 2019 harmonised
+3,1 +0,4 +0,3 +2,0 +4,1 +1,5 +4,1 +3,4 +2,8 +3,3 +0,6 +3,2
THE WUR 2019Flanders
THE WUR 2019Emilia R.
Conclusion
COMPETITON & RANKINGS
COOPERATION & BENCHMARK
COMPETITION = threshold of competitive advantage of the leadinguniversities must be respected in any case
COOPERATION = a mutual vision as Sistema Italia implies the willingness toexchange data and information
Conclusion
Data semantics
Data quality
Efficiency
Transparancy
Benchmarking
Branding
