Summary of Eduworks project

EDUWORKS – MULTI-PARTNER ITN

EDUWORKS
Crossing borders in the comprehensive investigation of labour market
matching processes: An EU-wide, trans-disciplinary, multilevel and
science-practice-bridging training network

Part B - Page 1 of 21


B.1

LIST OF PARTICIPANTS
Private
Sector

Partnership
University
Amsterdam
(coordinator)

of

CNTR
NL


Corvinno
Technology
Transfer
Center
(Corvinno)
The
Provost,
Fellows, Foundation
Scholars, and the
other members of
Board,
of
the
College of the Holy
and
Undivided
Trinity of Queen
Elizabeth
near
Dublin
(Trinity
College Dublin TCD)
University
of
Salamanca (USAL)
Central
European
University (CEU)
University of Siegen
(U-Siegen)

Associated
Partners
Aristotle University
of Thessaloniki
Central
European 
Labour
Studies
Institute (CELSI)
Corvinus University
of Budapest (CUB)

HU

Department /
Role
of
Legal Entity
Division /
Scientist-in-Charge Associated
Name
Laboratory
Partner
University
Amsterdam
Prof Dr. Kea Tijdens
Business School
(UvA-ABS),
Amsterdam
Institute
for
Advanced
labour Studies
(UvA-AIAS)
Non-Profit
Dr. András Gábor
SME
Company

IE

University

School
Computer
Science
Statistics

ES

University

HU

University

DE

University

Department of
Sociology
Department of
Public Policy
Institute of
Knowledge
Based Systems
and Knowledge
Management

GR

University

SK

Company,
SME

HU

University

Informatics
Institute

Labour & Social
Policy Department
Ericsson
Academy

Ecorys



NL

Company

Ericsson



IE

Company


Department
Informatics

of Prof. Dr. Inmaculada
Arnedillo-Sanchez
&

Prof Dr. Rafael
Muñoz de Bustillo
Dr. Martin Kahanec
Prof
Dr.
Madjid Fathi

–Ing.

of Dr. Lefteris Angelis
Dr.
Kahancová

TRA, SEC
NET, DIS
Marta TRA, SEC
NET, DIS

Dr. Zoltán Szabó

TRA (together
with
Corvinno),
NET, DIS
Peter Donker van TRA, SEC
Heel
DAT, NET,
DIS
Sean Delaney
TRA, SEC,
DAT, NET,
DIS

European Distance 
and
ELearning
Network (EDEN)
European Foundation for the Improvement of Living
and Working Conditions (EuroFound)

GITP

Dr. András Szűcs

UK

Association

IE

Foundation

Employment
Donald Storrie
and
Competitiveness team

NL

Company

Research

DIS,
DIS

NET,

TRA, SEC,
DAT, NET,
DIS

Dr. Alec W. Serlie

TRA, SEC,
DAT, NET,
DIS

Labour Asociados
ES
Company
Ricardo Rodriguez
TRA, NET,
DIS

Netpositive
HU
Company,
Mátyás Török
TRA, SEC,
SME
NET, DIS

Randstad
NL
Company
Labour Market
Marjolein
ten TRA, SEC,
Hoonte;
DAT, NET,
DIS
University
of
ES
University
Office
for prof Dr. Amparo TRA, NET,
Alicante (UAL)
Research,
Navarro Faure
DIS
Development
and Innovation

WageIndicator
NL
Foundation
Paulien Osse
TRA, DAT,
Foundation
NET, DIS
Note: TRA (specialised training), SEC (hosting secondments), DAT (data provision), NET (networking opportunities),
DIS (dissemination and communication)

Data for SME participant(s):

SME name

CELSI

Location of
research
premises
(city/country)
Bratislava/
Slovakia

Corvinno
Technology
Transfer Center

Budapest/
Hungary

Netpositive

Budapest/
Hungary


Type of R&D
activities
Labour
economics
Semantic
technologies,
knowledge
management
Software
development

No. of fulltime
employees

No. of fulltime
employees
in R&D

2

2

100 000

10

6

1 000 000

11

5

250 000

Annual turnover
(approx, in Euro)


B.2 S&T QUALITY
B.2.1.

S&T OBJECTIVES

The objective of EDUWORKS is to train talented early-stage researchers in the socio-economic and psychological
dynamics of labour supply and demand matching processes at aggregated and disaggregated levels. EDUWORKS
brings together researchers from several academic disciplines. Supply and demand matches at the aggregated national
or European labour force levels are widely studied in Labour Economics. Processes of supply and demand matching
at the meso-level are studied in Sociology, and deal particularly with the dynamics of occupational boundaries and
occupational licensing, educational institutions monitoring the skill demands in local labour markets, and adult
individuals considering the future skills needed to ensure their continued employability. At the disaggregated level
the person’s demands - ability fit refers to a wide body of knowledge in HRM. Increasing segments of the demand
side and the supply side of the labour market are digitized, ranging from job sites and cv’s at Facebook and LinkedIn
to extensive databases with job descriptions and related skills demands. These developments have led to Knowledge
Management and educational challenges in (digitized) matching processes. Specifically, EDUWORKS will focus on
matching processes at three levels and on one overarching topic:
 Individual (Micro) level fit between job demands - persons’ abilities
 Meso-level employer demands for occupational skills versus occupational dynamics
 European and national (Macro) level labour supply and demand matches and mismatches
 Knowledge Management for supply and demand matches

Labour Economics

Labour Market
Matching
Processes
Individual
skills

Job
Requirements

Knowledge Management

Educational
Outcomes

Macro – level
Focus

Sociology of
Occupations

Human Resource
Management

Lifelong Learning

Meso – level
Focus
Micro – level
Focus

Figure B 2.1. ‘EDUWORKS’ Objectives

By bringing these disciplines together in a comprehensive analytics framework and training researchers in its
exploitation, we expect to bring about much needed expertise and insight. Scientists and professionals in psychology,
economics, and sociology have started to recognize the interdependencies between their fields, with a growing
number of publications focussing on interaction and collaboration opportunities. This has led to many exciting new
questions and a search for matching models and theories, which are firmly based in each of these disciplines and can
thus be expected to create a strong foundation for learning and collaboration.
EDUWORKS will establish an interdisciplinary Training Network, covering the four social science domains of
HRM, Labour Economics, Sociology of Occupations and Lifelong Learning, that in turn are envisaged to be
scaffolded by a fifth domain, Knowledge Management. As is visible in Figure B 2.1, the EDUWORKS Network will
be established around the interactions of, and inter-relations between Educational Outcomes, Individual Skills and
Job Requirements. Each domain will be managed by a renowned and research active institution. Research and training
activities will be organised around these domains. Associate partners provide further resources (applied research,
data, training, and industry involvement) and therewith contributing to quality and ensuring the applied relevance of
the EDUWORKS activities.

The detailed objectives
The detailed aims of the training activities in the ITN are
1. To provide talented early-stage and experienced researchers a comprehensive research training programme aimed
at the acquisition of state-of-the-art knowledge of the components of the skills spectrum needed to analyse
matching processes at the individual, meso- and national/European levels

2. To prepare talented early-stage and experienced researchers for leading roles in European research and
consultancy such that they will be able to oversee and in a goal-oriented manner direct the multi-sectorial
matching processes at the individual, meso and national/European levels
3. To improve the employability of its early stage and experienced researchers in the higher ranks of academia and
industry by both enhancing their current skill set and increasing the scope of their transferable skills e.g. in
writing, communication, data analysis, intellectual property management, ethics, valorisation and
entrepreneurship, and by enhancing their skills in drafting research designs and submitting research proposals
4. To focus on result-oriented training in research by teaching the writing of scientific papers, resulting in
submissions to international refereed academic journals
The specific research training aims of the research in the ITN are to develop expertise in:
1. Investigating demands - abilities fit, that is the extent to which individual skills and abilities match the demands
(tasks) and requirements of organizations, and the ways in which organisations allocate tasks to jobs, following
an evidence based approach by leveraging scientific research into the practice of target areas and vice versa
2. Investigating the mechanism concerning the division of work reflected in task sets of occupations and the shaping
of occupational boundaries, the skill sets related to these occupations and the ways in which organisations define
their skills need
3. Investigating the wide range of mechanisms causing skills mismatches in national and European labour markets,
including the impact of the 2008 crisis on skills-occupation mismatch in Europe, and workers’ responsiveness to
labour market shortages concerning gender, age, and ethnicity
4. The establishment of a common language on the basis of which future investigations on the topics may draw to
further facilitate training and knowledge exchange. We expect this endeavour will benefit greatly from our
interdisciplinary approach by developing a transparent information exchange model between organisations,
educational institutions, individuals, intermediaries and researchers so as to facilitate an optimal collaboration
between these actors
5. The strengthening of interdisciplinary research cooperation so as to advance our understanding of the matching
mechanisms and the interactions between different levels of aggregation, including research cooperation with
private and academic organisations

Achieving the objectives: the EDUWORKS Training Network
To achieve the objectives listed above, the EDUWORKS Training Network brings altogether 19 partners from 8
European countries, including 6 full partners from 5 European countries (ES, HU, IE, NL, DE) and 13 associate
partners from in 9 European countries (ES, GR, HU, IE, NL, UK, SK). Together and in association with local
research schools, these full and associate partners offer an interdisciplinary training programme consisting of
interdisciplinary courses and compulsory tutoring in social science disciplines (economy, sociology, psychology and
knowledge management) and methodological course that will provide the broad education necessary for a future
career in academia, industry or consultancy.
The full partners are already strongly affiliated with one another, because in various settings, they have
cooperated in previous research activities. For example, the USAL and the UvA-AIAS have cooperated in several
projects since 2004, and so have Corvinno, U-Siegen, TCD and the UvA-ABS. The associate partners have joined
EDUWORKS, mostly based on bilateral long lasting collaborations with a full partner. Hence, achieving the
EDUWORKS objectives is grounded in a trusted and proven network of cooperation.
The proposed network is unique for at least five reasons. First, it offers a concentrated effort to advance training
in a new field of research at the interface of Lifelong Learning, HRM, Knowledge Management, Sociology and
Labour Economics – and asks questions that are relevant not only for training, but also for science, empirical use and
policy-making. Third, there is little or no tradition in Europe of professional interaction, let alone training exchanges,
between academic institutions in these fields to explore interdependencies and to include stakeholders for trials and
empirics. Fourth, developments in the abovementioned disciplines have contributed to the scientific urge to deepen
and broaden the collaboration between these research groups. Fifth, this network offers one of the first attempts to
organise such co-operation in a systematic and focused manner across different research institutions. It brings
together scientists and practitioners from different domains with experience of, and a genuine commitment to,
interacting and teaching across disciplines.

B.2.2. SCIENTIFIC QUALITY
Detailed description of the research topics
EDUWORKS is firmly grounded in five different disciplines (see Figure 1) that focus on three levels of aggregation.
At the individual level it focuses on the fit between persons’ abilities and job demands (HRM/Lifelong learning), at
the meso-level on labour supply and demand matching in educational institutes and occupations (Sociology of
occupations). On a national and European level EDUWORKS focuses on labour supply and demand mismatches

(Labour Economics). Finally, the activities within and across the aforementioned disciplines will be catalyzed
through the creation of an ontology based EDUWORKS database (Knowledge Management).

Transitions in lifelong learning and individual level person-organization fit
Lifelong learning (LLL) is ‘All learning activity undertaken throughout life, with the aim of improving knowledge,
skills and competencies within a personal, civic, social and/or employment-related perspective’ (European
Commission, 2002, p. 7). Early understanding of the construct associated it with adult workforce up-skilling to adapt
to rapidly changing society and world demands. A view, which Ingram, Field, and Gallacher (2009)argue, promotes
the economic relevance of adult learning rather than the learners’ need for self-actualisation’ through experiential
and transformative learning (Gouthro, 2010). For Fischer (2001) LLL encompasses more than adult education and
training; it is a mind-set, a habit for people to acquire. Notwithstanding the learner’s perspective, LLL has also
implications for the institutional infrastructure of learning services. To this end, Day argues that only those
institutions which are “concerned about the lifelong development of all their members” can develop lifelong learners
(1999, p. 20). Furthermore while, Fischer & Konomi (2007) argue that LLL outside school is different to schoolbased learning because it is self-directed, driven by interests and needs, informal, often collaborative and carried out
in tool-rich environments; Thorpe maintains that LLL is ubiquitous and it should include education, training,
informal, formal and non-formal learning (2000).
In learning, three interdependent processes of change can be distinguished: identity processes, knowledge
acquisition and sense making; all of these are transition processes (Zittoun, 2008). Transition is a ‘process of change
over time whether the change is conceptualised as being in contexts for learning or in learners’ identities (or both),
whether it takes place over a short or long times pan and whatever the causes and consequences of that change may
be’ (Colley, 2007, p. 428). Although transition implies movement and transfer as it regards learning, it is particularly
concerned with the change and shifts in identity and agency as learners progress through contexts (Ecclestone, Biesta
and Hughes 2010) and how structural factors affect the processes and outcomes of transitions. To this end, learning
is more likely to flow from transition than be the cause of them (Ecclestone, Blackmore, Biesta, Colley, & Hughes,
2005).
Mobile learning ‘supports education across contexts and life transitions’ (Sharples, 2009, p. 17) and it’s
concerned with a learner-centred understanding of learning which studies ‘how the mobility of learners augmented
by personal and public technology can contribute to the process of gaining new knowledge, skills and
experience’(Sharples, Arnedillo-Sánchez, Milrad, & Vavoula, 2009) as they progress through dimension of mobile
learning such as, physical, social or technological contexts.
In a society in which normative transitions are becoming destandardised, increasingly multiple and multilinear,
less defined by age-related stages, occurring more often ‘off-time’ in relation to what once were standardised lifecycles, and involuntary as they are brought about by unpredictable economic, social and personal constraints, there
is a need to investigate what kind of transitions are actually taking place. With the context of EDUWORKS our
research will focus on identifying and mapping learning transitions of mobile learners and how technology (whether
personal, public, portable or fix) supports those transition taking place.
In the HRM field the topic of individual level job demands - persons’ abilities fit addresses a number of
unresolved yet important gaps related to personnel selection, placement and training/lifelong learning. First, the
criterion problem (Austin & Villanova, 1992, Austin & Crespin, 2006, Guion, 1997) refers to “the difficulties
involved in the process of conceptualizing and measuring performance constructs that are multidimensional and
appropriate for different purposes.” (Austin & Villanova, 1992, p. 836). The absence of adequate and accurate
instruments to assess individual job performance, arguably the principal construct in the HRM discipline, across jobs,
organizations, and countries is in dire need of being redressed. Second, it is often asserted that General Mental Ability
(or intelligence) is the single best predictor of job performance across jobs and countries (Schmidt and Hunter, 2004;
Hülsheger, Maier & Stumpp, 2007). Yet, the supposed underlying mediator of this relationship, namely job
knowledge (cf, Schmidt, 2002; Hunter, 1986), is poorly understood, probably largely due to the laboriousness of
elucidating the job knowledge and performance requirements of individual jobs. Yet, creating such understanding
through ESR training may be posited to have tremendous benefits for organizations strategically meeting their HR
needs by i) enhanced person-job matching through improved selection and placement decisions, and ii) individual
training needs analysis in case none of the applicants fully meet the specific requirements of the job in question.
Third, in the person job-fit literature it has often been asserted and meta-analytically shown that demands-abilities fit
is related to a number of desirable outcomes, such as job attitudes, job performance, withdrawal, strain and tenure
(Boon, Den Hartog, Boselie & Paauwe, 2011; Kristof-Brown, Zimmerman & Johnson, 2005). An enhanced
understanding of demands-abilities fit will furthermore allow organizations to sculpt jobs to individuals rather than
vice versa (Tims & Bakker, 2010).
In previous years, the Amsterdam Business School has conducted empirical research on evidence-based testing
of job demands - persons’ abilities fit, and this EDUWORKS partner aims to train researchers in this approach by
improving theoretical insight into data collection, testing, and advanced statistical solutions. Hence, Not only the

HRM field, but also the Lifelong Learning field (LLL) would stand to benefit from an enhanced understanding of
the knowledge requirements of particular jobs.
In the EDUWORKS lifelong leaning domain, Design-Based Research Methods address educational theories and
practises in real-life learning situations, aiming to educate a flexible and adaptable workforce (Collins, Brown, &
Holum, 1991) . In the Individual learning approach, social interactions are one of the key sources of individual’s
knowledge, as is shown in the well-grounded communities of practice theory by Lave and Wenger (Lave & Wenger,
1991; Wenger 1999), Connectivism (Siemens, 2005) and Problem Based Learning theory (Hmelo-Silver, 2004).
Furthermore EDUWORKS is also building on the theories of cognitive apprenticeship (Collins et al., 1991) and
Situated Learning theories (Lave & Wenger, 1991). Since occupational motives, goals and evaluations are presented
marginally in the literature, mostly as Work-Based Learning; Guile & Griffiths, 2001) and Systems Theory
Framework (Patton & McMahon, 2006; McMahon, 2011), we expect EDUWORKS to enrich and further develop
these methodologies and associated skills with an occupational link – a valid link to the world of labour which is part
of individual and institutional Lifelong Learning.

Labour supply and demand matching in educational institutes and occupations in Sociology
The sociology of occupations has a long tradition in investigating occupational boundaries, initially by focussing on
the processes of professionalization, among others, in medical and legal occupations (Macdonald 1995). More recent
approaches draw from theories pertaining to occupational credentialism and social closure (Weeden 2002), thereby
shifting the focus from the professionals themselves as the main actors towards labour organisations as actors. In
organisations, the upgrading and downgrading of occupations might be the result of a general upgrading of tasks
within the occupation or the result of job losses at the lower end of the task spectrum, but this is difficult to conclude
according to Brynin (2002). Today, occupational dynamics are predominantly investigated in case studies, and very
rarely measured in large-scale surveys, because a valid instrument - a library of tasks in occupations for
measuring individuals’ occupation-specific skills - has yet to be developed (Tijdens, De Ruijter, De Ruijter, 2012).
Most surveys aim to assess individuals’ generic skills, but knowledge based on such skills cannot compensate for the
lack of research on occupation-specific competencies (Weinert, 2001). The sociology of occupations is currently
facing the challenge of designing theories and subsequent empirical underpinnings concerning the task and skill
profiles of occupations to understand the division of labour in organisations. Is the assignment of tasks to occupations
driven by skill level (hence cost of labour) and skill domain (hence efficiency of skill use), or is it influenced by the
educational system, specifically VET systems or by professional interest groups? Can employers’ demand for specific
skills be identified and if so, is the skill profile primarily related to the companies’ division of labour or it is influenced
by external factors? In sum, the sociology of occupations will profit from the development of theories and a valid
instrument to assess occupational tasks and skill requirements by surveying both job incumbents and employers. This
follows Keep and Mayhew’s (2010) plea to move analysis and thinking forward in the area of skill and employment
policy, including the development of broader occupational identities and their links to skill. These recent approaches
call for advanced training in the measurement of tasks within an occupation and the concomitant skill levels.

European and national labour supply and demand mismatches in Labour Economics
In Labour Economics the concept of skills-occupation mismatch refers to the degree to which the level of skills and
qualifications of workers fit the requirements of their jobs. At the aggregate level, this primarily depends on the
correspondence between labour demand and supply in the context of advancing educational levels. Recent debates
refer to polarization in the skill level demanded by firms. Most theories tend to emphasize technological change as
the main driver of such polarization (Goos, Manning and Solomons 2010), although some argue that trade
liberalisation raises wage inequality in developing countries (Goldberg and Pavcnik 2007) or that international trade
in the form of offshoring is a major contributor to the recent polarization of job opportunities in the United States
(Autor 2010). A second body of knowledge focuses on forecasting skill needs. The key innovation lies in shifting the
unit of analysis from individuals to “jobs” by defining jobs as specific occupations within specific sectors (FernándezMacías 2007). The whole set of thus defined “jobs” within a national economy comprises a “jobs matrix”, which is
an excellent basis for training in evaluating the implications of transformations of employment structures associated
with periods of economic expansion or contraction. Some have contested the idea that there is a single pattern of
change of employment structures across developing economies: even if all countries are affected by similar
technological and trade factors, these factors interact with structural and institutional differences leading to very
different implications in terms of job quality and even skill requirements (see Fernández-Macías and Hurley 2008).
The EDUWORKS partners aim to further develop expertise in this approach by using new data waves and advanced
statistical solutions which are expected to culminate in improved expertise on the part of researchers and therewith
theoretical insight.

Knowledge Management for supply and demand matches
From a Knowledge Management point of view, activities such as learning, context, teaching approaches, intelligent
tutoring and learning assessment tasks are now being modelled using special ontologies to support the generation of

a learning object sequence. The use of ontologies for context aware e-learning has major advantages: ability to
communicate context information and the ability to deliver just the right amount of knowledge. Draganidis and
Mentzas (2006) have worked out an ontology based competency management system, which integrates eLearning
functionality to map employee and/or organizational skills gaps and to address these with appropriate learning
objects. Their proposed ontology-based system provides a report in which the skills gaps of a particular employee
are identified. Another example is the research of Ng, Hatala, and Gasevic (2006) who developed an ontology-based
competency formalization approach as a way of representing competency-related information together with other
metadata in an ontology, in order to enhance machine automation in resources retrieval. In their approach, learning
objects are annotated with instances of competency specified in a Competency Class. In other cases the ontologybased and competency driven solutions aim to support comprehensive Human Resource Management functions. A
remarkable example is the Professional Learning project of the FZI in Karlsruhe, Germany, which aimed to elaborate
an ontology based reference model for HRM (Schmidt & Kunzmann 2007). In proposing this model, these authors
set out to connect the operational and strategic level of HR development and also to ensure the continuous updating
of an organisation’s competency catalogue. Biesalski and Abecker (2005) presented a solution for the automotive
industry. They applied an ontology based framework for Human Resource and Skill Management at DaimlerChrysler
Wörth. They established similarity measures in order to compare the 700+ skill profiles in their system. A further
ontology-based intelligent system for recruitment – disseminated by Spanish researchers (García-Sánchez, MartínezBéjar, Contreras, Fernández-Breis & Castellanos-Nieves, 2006) – supported job-seekers of the Murcia region in
Spain with an ontology based, collaborative recruitment website. These developers used ontologies to describe and
categorise job offers in order to obtain a faster matching between job seekers and job offers relevant to their profiles.
Reich, Brockhausen, Lau, and Reimer (2002) developed a Skills Management System for Swiss Life (SkiM), which
can be used to expose skill gaps and competency levels, to enable the search for people with specific skills, and to
influence the requirements for training, education and learning opportunities as part of team building and career
planning processes. SkiM formulates every skill, education or job description of employees in terms that are selected
from the corresponding ontology. The topic of Knowledge Management for supply and demand matches will not
only support the training and research within each domain but also aims to facilitate the identification of synergies
between these four content domains.
This domain faces the challenging task to train ESRs in developing a technical and methodological framework,
in which the EDUWORKS database is key. The database will consist of 1) an ontology to identify and classify
occupations and tasks at various levels of aggregation ranging from job-industry cells at the country-level to detailed
task descriptions of jobs in organisations, 2) an ontology to identify and classify skills and competencies at various
levels of aggregation ranging from the major educational categories at the country level to detailed descriptions of
job requirements in organisations, 3) as many interlinkages between the two ontologies as possible, and 4) for each
element in the ontologies empirical data identifying the volumes in terms of jobs, school leavers, job holders, and
tasks distributions. The main types of data sources are recruitment databases, job seekers’ portals, educational
programs’ output, covering as many EU countries as possible, as well as aggregated survey data and administrative
data.

Planned research collaborations
In each Work Package at least two full partners and one to three associate partners will collaborate. See Table B.2.1.
for details which partners are involved in the WPs. Each WP will consist of a Work Package Leader (WPL) and
coordinator, experienced researchers of the domain (not funded from EDUWORKS) and 3-4 early stage or
experienced researchers from different universities or research centers. Within each WP, the researchers will
collaborate closely, based on the WP’s research and training plan and the individual research and training plans. The
research collaboration will result in skilled researchers as well as joint research papers (see Section B.3.).
All ESR projects have been designed to be relatively independent from one another, so that failure in one project
will not result in a domino effect. At the same time ESRs will enrich one another’s projects and outputs by
contributing their data to the central EDUWORKS data repository and running their analyses through the
EDUWORKS research dashboard. In this fashion for instance ESR1and ESR7 can jointly examine the implications
of the same job knowledge data not only for individual (i.e., micro level) job performance but also for diagnostics
pertaining to meso-level educational curriculum content. Along similar lines, the macro level mismatches that are
identified on the basis of data collected by ESR 11 can be integrated with the meso-level data of ER6 and ESR7 to
yield insight into how such mismatches may be addressed through the targeted provision of educational content. As
is also visible from this example the same data may be used for the benefit of all levels, without increasing the risk
of an individual project failing.
The collaboration with private sector associated partners is important, because this will include secondments to
train ESRs in undertaking joint research, resulting in joint research papers. Besides these secondments, private
organisations also participate in knowledge transfer activities, such as workshops and summer schools and they
participate in the Supervisory Board (SB), further underpinning our evidence based approach. The cooperation

between full partners and private sector associate partners is detailed in section B.2.5. ‘Contribution of the private
sector’.

B.2.3.

RESEARCH METHODOLOGY AND APPROACH

The key elements of the research methodologies and approaches
The research methodologies and approaches vary across the four thematic Work Packages. The methodological
approaches predominantly relate to the research level at hand. EDUWORKS explicitly aims at training research
methods used in one WP to the researchers in other WPs.
 In WP2 – the micro-level - the methodological approaches to investigate the fit dynamics between the individual
with a given skills set and the skill requirements of a job and the associated transitions in Lifelong learning are
based on qualitative interviews to determine the content of the job performance and knowledge domains for
particular jobs, and on quantitative surveys and secondary data, employing analytical techniques such as
qualitative content analysis, cluster analysis, structural equations modelling, and others to analyse the data. The
associate partners Ericsson, Randstad and GITP will supply these data for the purposes of the project.
 In WP 3 – the meso-level – the methodological approaches to investigate the clustering of job titles into
occupations and the industries’ skills need for a given set of occupation are based on survey data, among others
unique data from a multi-country web survey that includes jobholders’ frequency and skill ranking in their
occupations, using specific task sets for more than 400 occupations. It employs analytical techniques such as
cluster analyses, among which interrater agreement models, and regression models for binary and continuous
variables. The data will become available through the associate partners Randstad, Ecorys and WageIndicator
Foundation.
 WP 4 – the macro-level – the methodological approaches to investigate European supply and demand matching
will focus on statistical multilevel analyses of large-scale European micro-datasets, such as the European Labour
Force Survey and the European Working Conditions Survey, and on the European-wide aggregated JOBS dataset
developed by associate partner Eurofound.
 WP 5 – the knowledge base – focuses on the Knowledge Management issues related to matches and mismatches
in labour supply and demand, which includes ontology engineering, big data analytics representation, employment
data management by matching job roles to educational competencies, and developing a web-based multi-country
and multi-level occupational information system. The wide variety of research methodologies imposes high
demands on the training of the early-stage researchers. In order to address this issue, Associated Partners AUT,
Netpositive and Ericsson provide data and expertise for this work, and professor Winny Wade of Trinity College
Dublin will contribute his expertise in the area of semantic knowledge management. Furthermore, the WP5 ESRs
can rely on one-to-one contact with the other ESRs through the online platform to address any ambiguities that
emerge.

Ethical issues
Chapter B.6 details that no standard ethical issues arise from the proposal. We do foresee a potential ethical issue
arising from associated partners desiring to protect competition sensitive data which might conflict with a desire on
the part of researchers external to this project to perform reanalysis to confirm certain findings. If such a case should
arise, a legally binding agreement will be drafted, in which the external researcher will be allowed to run such
reanalyses as long as he or she does not disclose the data to third parties.
Fact of the matter is that ethical issues can arise in any phase of research (e.g, planning, design, data collection,
data processing and storage, data analysis, and dissemination. In its training program, EDUWORKS will therefore
include a set courses on privacy-related issues in data-collection and on fraud, plagiarism and related ethical issues,
because instilling an awareness with and compliance to such issues in early stage researchers is critical. Furthermore
the ER experienced researcher will be appointed as ethical counsellor so that ethical issues, when they do arise, can
be confidentially discussed, in order to decide on an appropriate course of action. For further details, please refer to
Chapter B3.

Summary of the research approaches
Following the detailed description of the research topics in section B.2.2., this section provides a summary of the
research approaches. The distinction between the three levels of analysis and the overarching theme of Knowledge
Management is mirrored in the Work Packages and in the subsequent research projects. Table B.2.2 reflect the titles
of the individual projects. Figure B.2.2 puts individual projects into context across disciplines and work packages.
The detailed descriptions of all EDUWORKS projects are in section B.3.1.
Resear Project Title
Host
Work
Duration Start
cher #
Institution
Package(s) (months) date



ESR1
ESR2
ESR3

ESR4

ER5
ER6
ESR7
ESR8
ESR9
ESR10
ESR11
ESR12
ESR13
ESR14

Leveraging the potential of job knowledge to fit
individuals to jobs: Studies in Personnel
Selection
Leveraging the potential of job knowledge to fit
individuals to jobs: Studies in training
Identification and mapping of the lifelong
learning transitions of mobile learners : from
trajectories to pathways
An analysis of lifelong learning transitions of
mobile learners: implications and principles for
the design of technologies to support and
facilitate lifelong learning transitions
The dynamics underlying the division of tasks
into occupations
Identifying companies’ skill needs
Labour market driven learning analytics
The determinants of skills-occupation mismatch
in Europe: a job-level approach
Skills-occupation mismatch in Europe: the
impact of the 2008 crisis
Measuring occupational skill mismatch
On workers’ responsiveness to labour market
shortages: gender, age, and ethnicity
Adaptive assessment interface between
education and workplace
Employment Data Management via Matching
Job role with Educational Competencies
Developing a Web-based Multi-country
Occupational Information System
Table B.2.2

UvA-ABS

2

36

4

UvA-ABS

2

36

4

TCD

2

36

4

TCD

2

36

4

UvA-AIAS

3

22

4

UvA-AIAS
UvA-ABS

3
3

22
36

4
4

USAL

4

36

4

USAL

4

36

4

CEU

4

36

4

CEU

4

36

4

CORVINNO

5

36

4

CORVINNO

5

36

4

Uni-Siegen

5

36

4

List of Researchers' Individual Projects

ESR 10

ESR 11

ESR 8

ESR 9
ESR 14

Sociology of
Occupations

ER 5

ER 6

ESR 7

Human Resource
Management

ESR 1

ESR 13

ESR 2
ESR 12

Lifelong Learning

ESR 3

Figure B.2.2

B.2.4.

WP5 – Knowledge Management

Labour Economics

ESR 4

WP4
Macro – level
Focus
WP3
Meso – level
Focus
WP2
Micro – level
Focus

Individual Projects distribution across work packages and disciplines

ORIGINALITY AND INNOVATIVE ASPECTS

Innovations in the light of the current state-of-the-art
The project provides insight into job-person-education matching in the labour market at different levels of
aggregation. In most studies, only one level of aggregation is examined. Here we study three levels and we apply a
complex data repository with an intelligent interface (researcher dashboard) to allow for interconnections between
the levels, which requires different disciplines. Therefore, we apply a multi-disciplinary approach of HRM , Lifelong
Learning, Sociology of Work and Occupations, Labour Economics and Knowledge Management. This novel

combination will provide exciting new and practically relevant information on empirically grounded matching
processes in the labour market.
The involvement of the private sector will provide access to large-scale data otherwise not accessible, and will
contribute to insights beyond the existing body of knowledge. The associate partners Randstad and GITP for example
will provide access to information about large numbers of individuals with their skills set and the skills in demand
by labour organisations, while the associate partner EuroFound will provide access to its JOBS database, developed
over the previous years. The associate partner WageIndicator offers data from its unique global web survey, which
allows for occupation-specific survey questions about the frequency and skill levels needed for a range of tasks in
these occupations. In exchange for becoming a network member, all associate partners provide their data free of
charge at the disposal of the researchers in the network. Access to and integration of these unique data sources will
contribute to new insights in the job-person-education matching.

Synergies and complementarities
The table B.2.3 below details the complementarities of the partners. It shows that each WP includes two full partners
and at least three associate partners. The lead partners are underlined. At the start of the project, the WP leaders will
detail the research and training plans, as outlined in this proposal. These plans will form the basis of the individual
research and training plans of the ESR/ERs. In this way, EDUWORKS aims to ensure coherence and consistency
within work packages. Across work packages, EDUWORKS has outlined a network-wide training program (see
section B.3), including project meetings, summer schools and workshops to ensure synergies.
WP2 Micro level
Full partners
UVA
Corvinno
TCD
USAL
CEU
U Siegen
Associate partners
AUT
CELSI
CUB
Ecorys
Ericsson
EDEN
EuroFound
GITP
Labour Asociados
Netpositive
Randstad
UAL
WageIndicator

√
√
√

WP3 Meso Level

WP4 Macro
level

WP5 Knowledge
management

√
√
√
√

√
√

√

√

√

√
√
√

√
√
√
√

√
√
√

√
√

√
√

√
√

√
√

√

√
√
√

√
√

Table B.2.3 Partner Involvement in WPs

Values beyond existing programmes
The societal relevance of EDUWORKS is large. Europe faces major structural challenges – globalisation,
unemployment and an ageing workforce. The economic crisis has made these issues even more pressing. The EU’s
Lisbon strategy addresses these challenges – aiming to stimulate growth and create more and jobs, while making the
economy greener and more innovative. A new set of employment guidelines for the period 2005–08 was adopted to
reflect the renewed focus on jobs, stressing the EU’s overall goal of achieving full employment, quality and
productivity at work, and social and territorial cohesion, and advocating a lifecycle approach to work that tackles the
problems faced by all age groups. By the end of 2009, President Barroso set out his vision for where the European
Union should be in 2020.
The current crisis should be the point of entry into a new sustainable social market economy, a smarter, greener
economy where our prosperity will result from innovation and from better using resources, and where knowledge
will be the key input. To make this transformation happen, Europe needs a common agenda: the EU 2020 strategy.
This strategy should enable the EU to make a full recovery from the crisis, while speeding up the move towards a
smart and green economy. The Communication ‘New Skills for New Jobs: Anticipating and matching labour market

needs’ presents a first assessment of the Union’s future skills and jobs requirements up to 2020. Its two main
objectives are to improve Member States' and the EU's capacity to assess, forecast and anticipate the skills needs of
its citizens and companies, and to help ensure a better match between skills and labour market needs. EDUWORKS
aims to contribute substantially to these aims by developing interdisciplinary expertise of experienced researchers,
through training of early stage researchers, and through the establishment of an information exchange model. For
this reason, associate partner EDEN will contribute to the project by means of a dissemination program that reaches
out to policy makers at the European and national levels, and to key persons in educational institutes, labour
organisations and temporary work agencies.

B.2.5.

CONTRIBUTION OF THE PRIVATE SECTOR

Full partner
University of Amsterdam - AIAS

CNTR
NL

University of Amsterdam - Amsterdam
NL
Business School
Corvinno Technology Transfer Center

HU

Trinity College Dublin

IE

University of Salamanca
Central European University
University of Siegen

ES
HU
DE

Associated Partner
Ecorys
WageIndicator Foundation
GITP

CNTR
NL
NL
NL

Randstad

NL

Corvinus University of Budapest (CUB)
Netpositive
European Distance and ELearning Network
(EDEN)
Ericsson
European Foundation for the Improvement of
Living and Working Conditions (EuroFound)
Labour Asociados
CELSI
Aristotle University of Thessaloniki (AUT)
University of Alicante (UAL)

HU
HU
UK
IE
IE
ES
SK
GR
ES

Table B.2.4 Partner Involvement in WPs

The EDUWORKS network includes 13 associate partners:
 Five private sector partners are multinational enterprises (Ecorys, Ericsson, Randstad, GITP) and one is a national
enterprises (Labour Associados (ES). These partners will host researchers for secondments.
 Three universities are an associate partners (AUT, CUB, UAL). From these partners CUB closely cooperates with
the Hungarian partner. Corvinno’s ESRs will be accepted and granted full PhD student status by the Business
Informatics Doctorate School.
 Three highly specialised SMEs are involved: CELSI (Labour Market research, Corvinno (Technology Transfer)
and Netpositive (Software Development)
 One extraterritorial organisation is involved: Eurofound, who will give access to its JOBS database and
supervision with respect to the modelling of the skill-demand.
 Two NGOs are involved. The WageIndicator Foundation (NL), which runs a continuous worldwide web-survey
concerning work and wages on their frequently visited websites in 75 countries and will provide access its data.
The European distance and e-Learning Network (EDEN) is the biggest European professional network in its
domain and will be responsible for the communication of the project towards eLearning professionals, policy
makers and for the greater audience.
The table B.2.4 shows the links between the full partners and their main associate partners. The associate partners
contribute to the ESR/ERs exposure to different research environments, both to commercial research enterprises and
to data-collecting institutions, by offering professional courses, secondment and exchange opportunities. Some
associate partners contribute by providing survey data (Eurofound, Ecorys, WageIndicator Foundation), others by
providing access to their large administrative data (Ericsson, Randstad, GITP). All host institutions including the
associated partners have fluent English capabilities. The individual contribution of the associated partners to the
training program will be discussed in greater detail in section B.3.2.



B.3
B.3.1.

TRAINING
QUALITY OF THE TRAINING PROGRAMME

Objectives of the training program
EDUWORKS aims at training the next generation of scientists in a broad range of skills and competences required
to pursue their career in academic and industrial settings. The research training program will equip the early stage
and experienced researchers to become (1) an expert in their discipline of research, while having knowledge of
cutting-edge technologies in related disciplines; (2) trained in the methodological underpinnings of these
investigations; (3) skilled in presenting their results in writing and orally, including communicating the results beyond
an academic audience; (4) experienced in designing and managing research projects, including cooperation with the
private sector; (5) aware of ethical issues related to their discipline of research.
Ad 1) The scientific competences to be gained are in part discipline-specific and in part interdisciplinary:
 Expertise in bridging the science-practice divide by fostering evidence based approaches
 Expertise in employment structure and occupational change, the characteristics of labour supply and the
evolution of educational mismatch in Europe
 Expertise in the mechanisms underlying the division of tasks across jobs in organisations, the associated skill
levels and degree of educational mismatch related to jobs in Europe
 Expertise in adaptive employee skill and task management and evaluation
 Expertise in the technology behind the exploration and visualisation of linked research data
 Expertise in supporting job knowledge based personnel selection decisions
 Expertise in job knowledge based training needs analysis and content development
 Expertise in job knowledge driven educational curriculum development
 Expertise in theoretical principles and practice of mobile lifelong learning and its transitions and on when
and how technology enables and supports learning transitions in mobile lifelong learning
 Having knowledge of cutting-edge technologies in related disciplines
Ad 2) The methodological competences to be gained are in part discipline-specific and in part interdisciplinary:
 Proficiency in the manipulation and analysis of large Social Sciences datasets
 Proficiency in survey and questionnaire design
 Proficiency in searching and identifying publications using large scale databases
 Proficiency in adaptive learning system design and development
 Proficiency in exploiting and presenting big datasets
 Proficiency in the content analysis of qualitative data
 Proficiency in meta-analysis, regression analysis, hierarchical linear modelling, structural equations
modelling, bootstrapped moderated mediation analyses, exploratory/confirmatory factor analysis
Ad 3) The disseminating skills to be gained are interdisciplinary and include:
 Proficiency in writing and structuring academic papers
 Presentation skills for academic and professional audiences
 Proficiency in disseminating skills using the Internet and social media
 Sound knowledge of English Academic Writing
Ad 4) The leadership skills to be gained are interdisciplinary and include:
 Proficiency in cooperation in teams and in providing and receiving comments
 Proficiency in project management for inter-disciplinary and multi-site projects
 Proficiency in writing fundraising proposals for research projects
 Proficiency in peer review of academic papers
 Preparation of master students for the academic labour market
 Proficiency in intercultural and interdisciplinary collaboration
Ad 5) The expertise in dealing with ethical issues to be gained are interdisciplinary and include:
 Proficiency in avoiding issues associated with plagiarism and fraud in academic research on the basis of
established professional ethical standards and guidelines
 Proficiency in dealing with issues related to privacy of individuals and enterprises and to respondent burden
on the basis of established professional ethical standards and guidelines



Content structure
EDUWORKS will offer a training program that promotes scientific excellence, aims at the objectives listed in
the previous section, and exploits the interdisciplinary expertise and infrastructure in the network. The training
program consists of three building blocks, namely (1) local individual training; (2) network-wide training in research
and transferable skills; (3) secondments at full and associate partners, to be detailed in the next sections.

Local individual training
The early stage and experienced researches will be embedded in the research structures at the partner’s universities
and they will benefit from local training facilities, based on a personal career development plan.
 At the University of Amsterdam the early stage researches will enrol in the PhD training programmes of the
Research Master Business in Society at the Amsterdam Business School (starting 2013), comprising of 120 ECTS
points and focusing on advanced research skills and expertise in the field of business studies.
 At Corvinno Technology Transfer Center the early stage researches enrol in the Business Informatics Doctoral
Programme of Corvinus University of Budapest (CUB), comprising of 180 ECTS and focussing on three research
directions: Business Informatics, Data warehouse–data mining and Opinion mining.
 At Trinity College Dublin the early stage researches will attend the PhD training programmes of the School of
Computer Science and Statistics, comprising of 90-120 ECTS points and focusing on statistics, research methods,
computer science, management science and mathematics, and a Directed Study Module with the supervisor.
 At the University of Salamanca the early stage researches will enrol in the PhD training programmes of the
Department of Applied Economics, comprising of 90-120 ECTS points and focussing on European economics,
employment policies, economic analyses, web data, and multivariate, longitudinal and multilevel statistical
analysis with Stata.
 At the Central European University the early stage researches will be members of the Public Policy Doctoral
Program, comprising of 24 ECTS points and focussing on professional level research and analytical skills in
European and international public policy, comparative policy analysis and political economy.
 At the University of Siegen the early stage researches will enrol in the PhD training programmes of the Faculty
of Science and Engineering, comprising of 90-120 ECTS points and focussing on Knowledge Management,
Database Management Systems, Computational Intelligence, and Software Engineering.

14 individual projects at Local Research Teams
ESR#1,
WP2.1
Supervisor
Discipline(s)
General
description
Relevance to
the network

Methodologies
to be applied

Nature of data
collection
ESR#2,
WP2.2
Supervisor
Discipline(s)
General
description
Relevance to
the network
Methodologies
to be applied

Leveraging the potential of job knowledge to fit individuals to jobs: Studies in Personnel Selection
Dr Stefan Mol, Dr. Gábor Kismihók and Prof. D.N. den Hartog, UvA-ABS
Human Resource Management
The objective of this project is to generate support for the job knowledge mediated relationship between General
Mental Ability and Job performance
The Job knowledge data will form the input of the ontology based selection system. Furthermore we foresee close
collaboration with ESR 12. The project will contribute to our understanding of how intelligent algorithms (ESR12)
may identify (mis)matches between person’s abilities and job demands.
(Quantitative) literature review and practitioner interviews to identify best practices in the validation of job
knowledge tests. Collection of qualitative job knowledge data for a particular job through interviews with job
incumbents (N>50) and HR managers (N>20); additional qualitative data from vacancies and job related
documentation. Data will be content analysed in order to yield the job knowledge dimensions that are key to job
performance in this job. Multisource surveys (N>500) will finally be employed for psychometric validation and to
establish a relationship between job knowledge and job performance.
Interviews, surveys, desk research
Leveraging the potential of job knowledge to fit individuals to jobs: Studies in training
Human Resource Management and Lifelong Learning
The objective of this project is to generate support for the widely held but seldom investigated belief that job related
training contributes to job knowledge and therewith job performance, thereby forging a link between educational
institutions and the labour market.
The Job knowledge data will form the input of the ontology based selection system. Furthermore we foresee close
collaboration with ESR 12. The project contributes to our understanding of how intelligent algorithms (ESR12)
may be used to ameliorate (mis)matches between person’s abilities and job demands.
(Quantitative) literature review and practitioner interviews to investigate the conditions under which organizations
are better off training their incumbents’ job knowledge or hiring new employees with such job knowledge.
Collection of qualitative job knowledge data for a particular job through interviews with job incumbents (N>50)


and HR managers (N>20); additional qualitative data from vacancies and job related documentation.. Data will be
content analysed in order to yield the job knowledge dimensions that are key to job performance in this job.
Multisource and multiwave surveys (N>500) will be employed for psychometric validation and to investigate
temporal dynamism in the co-development of job knowledge and job performance over time.
Nature of data
collection
ESR#3,
WP2.3
Supervisor
Disciplines(s)
General
description
Relevance to
the network

Methodologies
to be applied

Nature of data
collection
ESR#4,
WP2.4
Supervisor
Disciplines

Interviews, surveys, desk research
Identification and mapping of the lifelong learning transitions of mobile learners : from trajectories to
pathways
Prof. Inmaculada Arnedillo-Sánchez & Prof. Frank Bannister, TCD
Lifelong Learning
The objective of this project is to identify the learning transitions that take place when lifelong learners move in
and out of different dimensions of mobility and learning and will attempt to discern how technology supports the
mobility of learners and hence their learning transitions.
With the EDUWORKS focus on mismatches at various levels of aggregation, the question of how individual
learners resolve such mismatches, requires a focus on non-traditional learning arrangements in which context is
critical.
The first stage of the work will involve conducting a literature review with the objective of defining theoretical
constructs to establish a framework to define transitions in lifelong learning based on dimensions of mobility in
mobile learning. This work will inform the development of a survey of transitions in mobile lifelong learning. The
analysis of the survey (N≥500) will support the elaboration of hypotheses pertaining to the transitions that seem to
take place and how the technology supports them. These will then form the basis for semi-structured interviews
with participants in the survey who will be asked to agree/disagree/qualify the hypothesis. After analysis of the
surveys and the interviews an observation protocol will be designed for the shadowing of participants over a period
of 24-48 hours. Through iterative hypothesis forming and testing against new sets of data it is hoped that the
research will yield a map of learning transitions in mobile lifelong.
Literature review, experiments, surveys, interviews, observations and shadowing
An analysis of lifelong learning transitions of mobile learners: implications and principles for the design of
technologies to support and facilitate lifelong learning transitions
Prof. Inmaculada Arnedillo-Sánchez & Prof. Vincent Wade, TCD

ER#5, WP3.1

Lifelong Learning
This project will focus on analysing data from technology usage to develop a set of lifelong learning transition
metrics. These metrics will in turn be used to inform the development of technologies and applications that support
transitions in lifelong learning.
The mismatches that will be identified by ESR1, ESR2, ESR12 and ESR13 will form a fruitful basis for the
identification of labour market driven educational mobile learning content. The employed mobile learning
technologies will draw heavily from the WP5 knowledge base.
Applying a data mining/grounded theory approach the researchers will endeavour to extract a set of learning
transitions metrics from data collected as lifelong learners conduct their normal daily activities.
Two or more sets of data from different cohorts of participants would enrich and provide more validity to the
findings. To this end, it is envisaged that the Ericsson will share usage data of their employee.
Data from technology usage for instance: a) type of technology use (kind of device: desk top, laptop, tablet, phone,
etc; and applications); b) information (documents, presentations, etc) viewed, consulted, retrieved or created; c)
location (home, work, education or training venue, public/private transport, etc); d) time; e) duration; f) social
network (work; education; private etc)
The dynamics underlying the division of tasks into occupations

Supervisor

Prof K.G. Tijdens, UvA-AIAS

Discipline(s)

ER#6, WP3.2

Knowledge Management and Sociology of Occupations
This joint ESR project in the field of knowledge management and sociology of occupations focusses on an empirical
testing of theories concerning the role of skill levels in the dynamics underlying the division of tasks into
occupations within an industry.
Research results will confront the task and skill profiles of the jobholders in the same sector to analyse the volumes
and the characteristics of the mismatch. The assessment of the jobholder’s job is an essential part of the network
objectives.
Task frequencies will be compared across jobholders in similar occupations across countries, using interrater
agreement analyses and multilevel models.
The WageIndicator web-survey will be used to ask jobholders how often they perform a task, using a list of approx.
10 tasks per occupation, specified for 433 occupations in approx. 15 countries, conducted by the associate partner
WageIndicator (N=50,000).
Identifying companies’ skill needs

Supervisor

Prof K.G. Tijdens, UvA-AIAS

General
description
Relevance to
the network
Methodologies
to be applied

Nature of data
collection

General
description
Relevance to
the network
Methodologies
to be applied
Nature of data
collection


Discipline(s)
General
description
Relevance to
the network

Methodologies
to be applied

Nature of data
collection
ESR#7,
WP3.3
Supervisor
Discipline(s)
General
description
Relevance to
the network
Methodologies
to be applied
Nature of data
collection
ESR#8,
WP4.1
Supervisor
Discipline(s)

General
description

Relevance to
the network
Methodologies
to be applied
Nature of data
collection
ESR#9,
WP4.2
Supervisor
Discipline(s)
General
description
Relevance to
the network

Sociology of Occupations
This ER project will be embedded in the field of sociology of occupations and focusses on the development of
insights into the processes skill needs’ formulation in companies and the skill set of jobholders in companies in the
industry.
Research results will confront the companies’ skill needs and the task and skill profiles of the jobholders in the
same sector to analyse the volumes and the characteristics of the mismatch.
Two methodologies are applied: literature review and statistical analyses. After a literature review of skill needs
theories, the company survey data will be analysed with respect to the patterns of companies’ skill needs in the two
countries and the determinants of their skill needs, focusing on within and between country differences (using factor
analyses and multilevel models). Next, the jobholders survey data will be analysed with respect to the patterns of
jobholders’ tasks and the self-perceived skill match of their job and their education in the two countries as well the
determinants of their tasks clusters, focusing on within and between country differences (using interrater agreement
analyses and multilevel models). Regression and multilevel analyses based on survey data concerning companies
current and future skills needs. The interviewees are HR officers in companies in two countries.
Data collection by means of surveys of jobholders and employers in the agricultural industry in two countries (UK
and NL, N=5,000 in each country). The employers’ survey will be conducted as part of the field activities of the
associate partner Ecorys.
Labour market driven learning analytics
Human Resource Management and Knowledge Management
The objective of this project is to improve educational curricula of higher education with the help of valid labour
market data. In order to achieve this, data on graduates of the University of Amsterdam and the Hogeschool van
Amsterdam (HvA) will be matched to employee data obtained from Randstad (one of the largest employers in the
Netherlands) and GITP.
Explores Person-Education-Labour market (mis)matches by aggregating individual level data to the meso-level.
Requires knowledge management techniques for big data analysis.
Big data analytics, including classification, cluster analysis, data fusion and integration, neural networks, pattern
recognition, predictive modelling, regression, time series analysis and visualisation.
This ESR project will link existing secondary Randstad and GITP employment data of individual employees to
existing secondary UvA/HvA data on student performance and curriculum content.
The determinants of skills-occupation mismatch in Europe: a job-level approach
Dr Pablo de Pedraza, USAL
Labour Economics and Sociology of Occupations
This project will consist of a detailed evaluation and analysis of skills-occupation mismatches in Europe, using jobs
as the unit of analysis (occupations within sectors). It will discuss in detail the methodological difficulties involved
in measuring mismatch in a comparative framework, and it will refine and develop existing methodologies. It will
evaluate, using multivariate statistical models, the relative impact of the main determinants of occupational change
according to the literature (technology, trade and institutions) on the degree of mismatch in different European
countries.
In many ways, this PhD project can provide a comprehensive framework for the analysis of mismatch in all the
other domains, since it is the one that discusses the phenomenon at a more general level.
The general methodological approach will draw from the JOBS methodology, applied previously for the analysis
of occupational change in Europe and the US. The determination of the relative importance of the different
explanatory factors will be based on multivariate econometric modelling.
The basic data used in the project will draw from Eurofound’s JOBS dataset, which in turn derives from the
combination of different European sources (most importantly, the European Labour Force Survey and the European
Structure of Earnings Survey). If possible, this PhD project shall contribute to the JOBS dataset, adding further data
on trade openness and technological content for different occupations and sectors.
Skills-occupation mismatch in Europe: the impact of the 2008 crisis
Dr Pablo de Pedraza, USAL
Labour Economics and Sociology of Occupations
This PhD project will look at trends in skills-occupation mismatch in European countries, with a special emphasis
on the impact of the 2008 crisis. It will include a detailed discussion of the rhythm and nature of occupational
change over time, in terms of long and short-term trends. It will evaluate the depth of the break brought about by
the crisis, and its potential long-term effects.
This PhD project brings a general dynamic context to the overall project, evaluating the evolution of skillsqualification mismatch in recent years in Europe. By explicitly discussing the implications of the crisis in this
respect, it also introduces a more forward-looking perspective in the network.


Methodologies
to be applied

Nature of data
collection
ESR#10,
WP4.3
Supervisor
Discipline(s)
General
description
Relevance to
the network

Methodologies
to be applied

Nature of data
collection

The general methodological approach will draw from the JOBS methodology, applied previously for the analysis
of occupational change in Europe and the US. Econometric modelling and longitudinal analysis statistical
techniques will be used to evaluate the nature and implications of change in the long and in the short run on the
skill-occupation mismatch in Europe.
The basic data used in the project will draw from Eurofound’s JOBS dataset, which in turn derives from the
combination of different European sources (most importantly, the European Labour Force Survey and the European
Structure of Earnings Survey). If possible, this PhD project shall contribute to the JOBS dataset, adding further data
on long and short-term trends.
Measuring occupational skill mismatch
Dr Martin Kahanec, CEU
Labour Economics
Research has shown that conceptualizing, measuring and operationalizing skill mismatch in the labour market is a
particular challenge (Zimmermann et al., Bonin, Fahr, Hinte 2007). This project will address this challenge drawing
on the recent advances in the literature and the lessons learnt from the NEUJOBS FP7 project in particular. Its main
objective is to provide and empirically justify theoretical underpinnings for empirical work aimed at identifying
causes and effect of skill mismatches in Europe and beyond.
This project will be done in conjunction with the other three projects in WP4. This project particularly contributes
to the EDUWORKS network because of its insights into the skill mismatch concepts.
As a first step, this project will desk-review recent advances in the literature on measuring skill mismatches in the
labour market. The project will then collect a set of indicators of shortages, whose theoretical validity will be
evaluated using alternative models of the labour market. In the next step, the statistical power of alternative
indicators to predict difficulties of filling in vacancies by sector and occupation reported in employer surveys will
be tested using econometric methods. Principal Component Analysis will then be used to reduce the dimensionality
of the studied indicators, and the encompassing measures will be tested in an empirical analysis of European labour
markets. The proposed measures will be further validated for various sub-populations - men and women, the youth
and the elderly, natives and migrants, and ethnic subpopulations.
The project will use secondary micro-data from the EU Labour Force Survey, from which the indicators of
shortages by sector and occupation will be gauged, as well as micro-data from employer surveys measuring the
difficulty of filling in vacancies.

ESR#11,
WP4.2
Supervisor

On workers’ responsiveness to labour market shortages: gender, age, and ethnicity

Discipline(s)

Labour Economics
Europe faces severe labour market mismatches. Measuring the responsiveness of workers to skill mismatches in
the labour market is a particular challenge (Zimmermann et al., 2007). This project will address this challenge
drawing on the recent advances in the literature and the lessons learned from the NEUJOBS FP7 project in
particular. Its main objective is to measure the responsiveness of various populations – men and women, the youth
and the elderly, natives and migrants, and ethnic subpopulations to skill mismatches in Europe.
This project will be done in conjunction with the other three projects in WP4. This project particularly contributes
to the EDUWORKS network because of its insights into the skill mismatch conceptualisation, measurement and
operationalization. This also connects this project to the other projects in the in EDUWORKS
As a first step, this project will desk-review recent advances in the literature on measuring and empirically testing
skill mismatches in the labour market. The project will then collect a set of indicators of shortages, which will be
used to measure the responsiveness of various subpopulations in Europe to labour market shortages across sectors
and occupations using various econometric techniques. Eventually, an encompassing measure developed in project
ESR#10 will be utilized and further test the responsiveness of men and women, the youth and the elderly, natives
and migrants, and ethnic subpopulations to skill mismatches in Europe.
Jointly with project ESR#10, this project will use micro-data from the EU Labour Force Survey, from which the
indicators of shortages by sector and occupation will be gauged, as well as micro-data from employer surveys
measuring the difficulty of filling in vacancies.

General
description

Relevance to
the network

Methodologies
to be applied

Nature of data
collection
ESR#12,
WP5.1
Supervisor
Discipline(s)

General
description

Dr Martin Kahanec, CEU

Ontology based context aware content management
Dr. Réka Vas, CUB; Dr. András Gábor, Corvinno.
Human Resource Management, Knowledge Management, and Lifelong Learning
The aim of the research is to develop content management solutions that make use of semantic technologies to
provide online content (curriculum or learning material) recommendation services. Learning contents and user
profiles are described in terms of concepts with the help of domain ontologies. Based on the similarities between
item descriptions and user profiles, and the semantic relations between concepts, the system is envisaged to offer
the following services: a) personalized set of learning objects for the user according to his/her profile (that includes
the learner’s (or teachers) interest, aims and objectives, pre-requisites, background, the current level of



Relevance to
the network

Methodologies
to be applied

Nature of data
collection
ESR#13,
WP5.2
Supervisor
Discipline(s)

General
description

Relevance to
the network

Methodologies
to be applied

Nature of data
collection

ESR#14,
WP5.3
Supervisor
Discipline(s)

General
description

understanding etc.), and b) reordered list of learning objects taking into account the current semantic context of
interest of the user.
This solution will facilitate the acquisition and use of knowledge, skills and qualifications by providing an ontology
supported content management system that can identify and address users’ (learners’) needs. This solution is
innovative in that the domain knowledge is adapted into contextual learning content (for students) or training
content (for teachers or HR managers).
The first phase of the research consists of ontology engineering (based on ontology standards such as RDF and
OWL that support inference mechanisms that can be used to enhance content retrieval). The second concerns the
creation of an ontology-based interface for information retrieval that automatically and periodically retrieves
learning materials from several open educational content repositories. The third phase requires the development of
a web interface that allows for the automatic storage of all users’ inputs. Finally, the system also has to be tested
through trials..
Data for the field experiment will be automatically retrieved from the system (system logs, user assessments) and
throughout survey data are analysed with standard analytical software (e.g. SPSS) and techniques.
Employment Data Management by Matching Job roles to Educational Competencies
Dr. András Gábor, Corvinno
Human Resource Management and Knowledge Management
The main focus of the research is the multidimensional analysis of the labour markets’ demand compared to the
supply provided by the formal, informal and non-formal education/training services. The desired equilibrium fits
to the timely structured demand and supply in terms of professions, geographical characteristics and competencies.
In the EU many thousands of web portals contain publicly available job relevant data. The same is true for the
supply side of jobs. The system will provide an automated tool for finding, screening and pooling occupational
data, outputs of different types of educational data, and lists of matching and/or mismatching of educational
categories and job roles, including occupations, competencies and work tasks.
The project is innovative from the aspect of interlinking domains related to employment and education. The
innovative character of the project is the matching application based on existing and diverse data. As a result an
EDUWORKS dashboard will be available for all researchers in the network with a wide variety of available data
sources.
For these data sources this research will use advanced information retrieval (crawlers). The proposed solution will
reflect to the up-to-date architectures, as the cloud computing, namely the Software as a Service (SaaS) service
model. We will use the public cloud to get data (recruitment data, employment data and educational output) and
we will deploy our solution as a hybrid cloud (composed of private, community or public cloud) that remain unique
entities, but are bound together by standardized or proprietary technology that enables data and application
portability).
(a) National qualification frameworks with mapping into the standard educational levels ISCED, (b) the national
accredited educations with mapping to EQF and mapping to the national main educational categories, (c)
educational requirements of unstructured job advertisements and vacancies of employment agencies; and for the
job roles ontology (d) a database with 1,700 occupational titles classified into ICSO-08, (e) unstructured data of
job titles and educational requirements from job advertisements and vacancies of employment agencies; (f)
competency dictionary from the ONtoHR project, (g) competency dictionaries from web-portals covering partial
labour markets (e.g. nurses), (h) competency requirements from unstructured job advertisements and vacancies of
employment agencies; (i) work task lists from the EurOccupations project, (j) work task lists from the ONtoHR
project; (k) work task lists from web-portals covering partial labour markets (e.g. nurses), (l) work task lists from
unstructured job advertisements and vacancies of employment agencies.
Measuring occupations, using dynamic text fields in web-based data collection
Prof Dr. –Ing. Madjid Fathi, Uni-Siegen; Prof K.G. Tijdens, UvA-AIAS
Knowledge Management, Labour Economics and Sociology of Occupations
Occupation is a key variable in EDUWORKS. Yet, the measurement and the classification of job titles is not
particularly valid, particularly for cross-border comparisons. Traditionally, two types of question formats are used
in forms: open response formats (e.g. text fields) and closed formats (e.g. multiple choice lists). Open response
formats put high cognitive demands on respondents and are expensive to evaluate, closed formats limit the answer
choices. Dynamic text fields are innovative tools for self-directed online data collection on occupational titles,
which mitigate these disadvantages of the two formats and combine their benefits. Dynamic text fields pose high
demands to a database directing the respondent’s choices. First this project aims to systematically investigae
opportunities and challenges in the use of dynamic text fields in the continuous, 75-country WageIndicator websurvey. Because the survey uses a well-defined set of terms (all words from one specific domain: occupation), it
offers cross-language and cross-country comparisons concerning the use of the autocomplete tool, including
response times and dropout rates. Inspired by findings from Internet science, memory research and survey
methodology, psychological factors that may affect data quality arising from the use of autocomplete and
autosuggest technology are investigated. Second, it aims for an exploration of the requirements to the underlying
database with more than 1,700 occupational titles and their translations, in order to assure consistency in how



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collection

respondents fit their detailed job titles into the aggregated occupational categories. Third, it aims for the
development of a procedure how respondent-side newly added occupational titles, derived from the web-survey,
are to be classified in the database.
This project will closely cooperate with ESR8 and ESR9. The project aims for synergies between the Knowledge
Management approaches combined with the content knowledge of occupational databases and their classification
systems.
Multiple methodologies are applied, ranging from regression analyses to matching programs for respondent-side
job-titles into the database.
The data collection is derived from the occupation auto completion and search tree tool in the web-surveys of the
industrial partner WageIndicator.



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Summary of Eduworks project

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