1. Building a European Classification of Higher Education Institutions Workshop ‘New challenges in higher education research and policy in Europe and in CR’, Prague 27-28 November 2008 Frans Kaiser

2.  Rationale European Classification of HEIs  The project so far  The next steps; U-Map  CEIHE I; breaking the ground  CEIHE II; the survey

3.  Profiles European higher education at a global scale Functions of European Classification  Provides basis for effective policies and investment strategies  Allows institutional development strategies  Facilities benchmarking, networking and partnerships  Is a prerequisite for rankings

4.  stakeholders approach: exploration and discussions  first phase: basic design principles & first set of dimensions and indicators The Classification Project  third phase: reduced set of dimensions; first version of on-line tool  second phase: adapted set of dimensions and indicators

5.  inclusive for all European HEIs  a posteriori information Design principles  multi-dimensional  focus on ‘objective’ data  non-hierarchical 1 st phase

6.  non-prescriptive  flexible  parsimonious regarding extra data- needs  related to European Register of Quality Assurance Agencies Design principles (2) 1 st phase

7. First Version of a European Classification Methods used: •interaction with stakeholders •analysis of existing data sources •in-depth case studies •survey, to access relevance, validity, reliability and feasibility of dimensions and indicators 2 nd phase

8. Dimensions and indicators Education Research and innovation International orientation Cultural and regional engagement Size and setting 2 nd phase

9.  Highest degree offered (degree level)  degrees/diplomas granted per level  Subject mix  Orientation of programmes  number of programmes offered for licensed professions  Involvement in LLL  number of mature (> 30 years) students as % of total enrollment Education 2 nd phase Dimensions and indicators

10.  Research intensiveness  peer reviewed publications per academic staff  scientometric ‘crown’ indicator  Innovation intensiveness  Financial volume privately funded research as % of total financial volume  Number of start-ups  Number of filed patents  Income from licensing Research and innovation 2 nd phase Dimensions and indicators

11.  Teaching and staff  international degree seeking students as % of total number of students  incoming international/European exchange students as % of total number of students  outgoing international/European exchange students as % of total number of students  joint international programmes as % of total number of programmes offered  programmes offered abroad  fte international academic staff as % of total academic staff International orientation 2 nd phase Dimensions and indicators

12. International orientation  Research  Financial turnover in EU research programmes as % of total financial research volume 2 nd phase Dimensions and indicators

13.  Size  Total number of students (per degree level)  Total number of fte’s academic staff  Total financial turn over per year  Mode of delivery  distance learning programmes as %  Part-time programmes as %  Part-time students as %  Public/private character  Income from government sources as % of total income  Legal status size and settings Dimensions and indicators

14.  Cultural engagement  Number of concerts  Number of exhibitions  Regional engagement  Graduates in the region  Turnover in EU structural funds  Extra-curricula courses for region  Importance of regional income Cultural and regional engagement 2 nd phase Dimensions and indicators

15. Phase II: the survey 2 nd phase

16.  the setup of the survey  assessment of dimensions and indicators Contents  the data

17.  The questionnaires  The sample Setup of the Survey  Sampling methods  Stratification criteria  The strata in the response 2 nd phase

18.  Based on CEIHE I  Two separate questionnaires The Questionnaires  on-line 2 nd phase

19. The Questionnaire 2 nd phase

20.  Sampling methods  Sample size and criteria The Sample  Age, size, region  Sample and response 2 nd phase

21.  Age of institution The Sample 2 nd phase

22.  Size (enrolment) The Sample 2 nd phase

23.  Region (UN definitions) The Sample 2 nd phase

24. Assessment of the dimensions ‘This dimension is essential for the profile of our institution’ 2 nd phase

25. ‘This dimension is essential for the profile of our institution’ 2 nd phase

26.  Validity  Reliability Assessment of the Indicators  Feasibility 2 nd phase

27. Validity; not problematic 2 nd phase

28. Validity; some doubts 2 nd phase

29. Validity; potentially challenging 2 nd phase

30. Reliability; not problematic 2 nd phase

31. Reliability; some doubts 2 nd phase

32.  Time on collecting information  ‘Easy to collect’ Feasibility  Existing sources  Number of valid cases  Overall score 2 nd phase

33. Feasibility

34.  Calculate indicators  Assess feasibility  Identify and stress conceptual and definition issues  Rethinking the role of indicators in institutional strategies What are the data used for? 2 nd phase

35. Next steps  reduce number of dimensions/classes 3 rd phase

36. Classification is about grouping entities as a way to reduce complexity  A limited number of dimensions  A limited number of classes per dimension and per indicator 3 rd phase Next steps

37. Reduce the number of dimensions  Using conceptual considerations  Using empirical considerations 3 rd phase Next steps

38. 3 rd phase Reduce the number of dimensions Reduce the number of classes per dimension Next steps

39. % international academic staff 3 rd phase

40. % tuition fee income 3 rd phase

41. Size; enrolment 3 rd phase

42. Licensing income (as % of total income) 3 rd phase

43. Size; academic staff 3 rd phase

44. Next steps  develop on-line tool  reduce number of dimensions/classes 3 rd phase

45. Future use of the Classification Examples  providing information to stakeholders and clients about characteristics of a higher education institution 3 rd phase

46. Future use of the Classification Examples 3 rd phase

47. Future use of the Classification Examples  Providing assistance to institutional strategies and inter-institutional partnerships, benchmarking, and networking 3 rd phase

48. Future use of the Classification Examples 3 rd phase

49. Next steps  develop on-line tool  reduce number of dimensions/classes  communication process with stakeholders and preview  institutionalisation and ownership  ‘communities’ for special dimensions; www.u-map.eu 3 rd phase  rethink indicators

50. A European Classification of Higher Education Institutions Thank you for your attention! Thank you for your attention! This project has been funded with support from the European Commission. This presentation content reflects the views only of the author. The Commission cannot be held responsible for any use which may be made of the information contained therein. www.u-map.eu www.u-map.eu