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Annex I: Exploratory analysis of existing data sources 39 In this annex we present the results of the exploration of existing data sources. The information is presented by dimension and by indicator. Dimension 1: Types of degrees offered Indicator 1a: Highest degree offered Information may be found in the Ploteus database, although extracting all information may be rather tedious. http://ec.europa.eu/ploteus/portal/searchcustom.jsp Indicator 1b: Number of qualifications granted The number of graduates (is not the same as number of qualifi cations – issue of double degrees etc.-) needs to be broken down by level of programme. It is therefore unlikely that international databases will include this information (although through international networks like NARIC this information may be retrieved). National data sources will however in most countries have this information. Dimension 2: Range of subjects offered Indicator 2a: number of subjects offered This indicator relates to the scope of the educational profi le of the institution. In the World of Learning (Europa Publications 2006) the departments are listed, which gives an indication of the scope, but the comparability of that data is problematic, due to the existence of broad, multi-disciplinary departments and single subject departments. Counting the number of subjects is also problematic. In many higher education systems there has been a strong process of differentiation, which has lead to a huge number of subjects offered. Standardising these subjects (for comparative reasons) is diffi cult at the national level (and even more at the international level). There is an international classifi cation of disciplines (in the ISCED framework). Although the ISCED classifi cation is used by a growing number of countries, it is still not a common classifi cation, leaving us with the problem of how to compare different disciplines across countries. Dimension 3: Orientation of degrees In the draft classifi cation no indicator was specifi ed for this dimension. The rationale for this dimension is similar to the rationale behind the binary divide in many higher education systems: to differentiate between the vocationally oriented sector on the one hand and the academically oriented sector on the other hand. However, this dichotomy is heavily debated and the line between the sectors is not always clear. One way to look at this dimension is to use the formal structural divide between university and non-university wherever that applies. Information on this issue is available in national databases, listing the institutions by sector. Another way is to ask the institutions what part of the subjects offered are vocationally oriented (or to what extent the programmes are vocationally oriented). This requires a clear idea of what a vocational orientation is. But even if that condition is met, the reliability of the data collected this way is questionable. MAPPING DIVERSITY
Indicator 3a: number of programmes leading to certified/ regulated professions as a % of the 40 total number of programmes In the questionnaire used in the survey references are listed to websites at which (national) lists of regulated professions can be found. Indicator 3b: the number of programmes offered that answer to a particular demand from the labour market or professions (as % of the total number of programmes) This indicator asks for a subjective assessment of the proportion and therefore the question on existing data sources is not applicable. Dimension 4: Involvement in life long learning Indicator 4a: number of adult learners as a % of total number of students by type of degree International databases do not breakdown enrolment data by institution. Even national agencies very often present enrolment data by age only at the system or sector level. Dimension 5: Research intensiveness Indicator 5a: number of peer reviewed publications per fte academic staff This indicator is self-reported, therefore the question on existing data sources is not applicable. Indicator 5b: the ISI based citation indicator, also known as the ‘crown indicator’ For this indicator, the ISI databases are the obvious international data sources to use. There are some methodological issues to resolve and there is the issue of the licence to use data on the institutional level. Co-operation with CWTS, Leiden University is envisaged . Dimension 6: Innovation intensiveness The EU administers the Community Innovation Survey: A bi-annual survey among companies and (in some countries higher education institutions). Based on interviews with the Dutch statistical offi ce is seems highly unlikely that this source will yield data on higher education institutions (at the institutional level) for a reasonable range of countries. MAPPING DIVERSITY Indicator 6a: Number of start up firms There is no encompassing European database on start ups. There are a number of initiatives/ research projects that have addressed the issue. One of the disturbing results of the analysis of such projects is that there is no clear unambiguous defi nition of start ups. That means that the counting of start ups may be biased by ’deviant’ national, institutional or even departmental practices. OECD DSTI The Industry Science Relationships Outlook report (2002). In this biannual report ten ISRs are identifi ed, of which spin-offs are one and licensing is another. OECD is working towards a generic model, allowing international comparison. This information base may be used as context information at the national level, but there are no data in it at the institution level.
Page 1 and 2: Mapping Diversity Developing a Euro
Page 4 and 5: Mapping Diversity Contents Developi
Page 6 and 7: The research project team consisted
Page 8 and 9: 2. Building a European Classificati
Page 10 and 11: with university rankings, rankings
Page 12 and 13: Classifi cations use the principles
Page 14 and 15: Table 1: Draft and classifi cation
Page 16 and 17: • Fontys Hogescholen, the Netherl
Page 18 and 19: 3.1.2 Response to the survey 67 Res
Page 20 and 21: The question ‘this dimension is e
Page 22 and 23: 4. Conclusions 21 In this chapter w
Page 24 and 25: of information on non-degree offeri
Page 26 and 27: 4.3 Reduction of the number of dime
Page 28 and 29: Dimension Indicator new and suggest
Page 30 and 31: 5. Operational implementation Part
Page 32 and 33: etter world’ from 19 th to 20 th
Page 34 and 35: Government model In this model gove
Page 36 and 37: also underline the need for coopera
Page 38 and 39: Bailey, K. D. (1994) Typologies and
Page 42 and 43: EU The Rebaspinoff projects (part o
Page 44 and 45: World of Learning. If a further bre
Page 46 and 47: Annex II: The case studies 45 Case
Page 48 and 49: Indicator 1.3.1.: number of program
Page 50 and 51: Indicator 3.1.2a.: number of Europe
Page 52 and 53: Case Study: University of Strathcly
Page 54 and 55: Indicator 1.3.2.: number of program
Page 56 and 57: Indicator 4.2.2.: number of part ti
Page 58 and 59: The dimension ‘cultural engagemen
Page 60 and 61: Figure 4: Institutional profi les b
Page 62 and 63: and life long learning. Feasibility
Page 64 and 65: Conclusions The pilot survey showed
Page 66 and 67: Indicator 10b: The percentage of pr
Page 68 and 69: Figure 32: Higher education institu
Page 70 and 71: Introduction 69 Rationale for the s
Page 72 and 73: Table 2: Age strata IAU based strat
Page 74 and 75: What is more problematic (or more d
Page 76 and 77: Table 5: Overview of indicators and
Page 78 and 79: Figure 3: Opinions regarding the st
Page 80 and 81: Figure 4: Opinions on the statement
Page 82 and 83: enrolment and 12a: legal status) or
Page 84 and 85: Data from existing sources The ques
Page 86 and 87: Valid cases The fourth indication o
Page 88 and 89: ate (3r d doctor cycle) or (1st bac
Page 90 and 91:
If 40% is used as a cutoff point, t
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doct er mast Figure 16: Higher educ
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As a result of the discussion on th
Page 96 and 97:
Indicator 6d: Financial volume of p
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doctorate er mast or bachel or +mas
Page 100 and 101:
Indicator 9a: Enrolment The number
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er mast or bachel er mast or bachel
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Figure 37: Higher education institu
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Indicator 13b: Exhibitions 105 The
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Figure 44: Higher education institu
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The clustering is based on the corr
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as well as dimension 4 (involvement
Page 114 and 115:
Appendix 1: Comments 113 Dimension
Page 116 and 117:
7e number of program offered abroad
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