summary of eduworks project

EDUWORKS – MULTI-PARTNER ITN

Part B - of 21

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 - of 21

B.1 LIST OF PARTICIPANTS

Partnership Private

Sector CNTR

Legal Entity

Name

Department /

Division /

Laboratory

Scientist-in-Charge

Role of

Associated

Partner

University of

Amsterdam

(coordinator)

NL University Amsterdam

Business School

(UvA-ABS),

Amsterdam

Institute for

Advanced

labour Studies

(UvA-AIAS)

Prof Dr. Kea Tijdens

Corvinno

Technology

Transfer Center

(Corvinno)

HU Non-Profit

SME

Company

Dr. András Gábor

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)

IE University School of

Computer

Science &

Statistics

Prof. Dr. Inmaculada

Arnedillo-Sanchez

University of

Salamanca (USAL)

ES University Department of

Sociology

Prof Dr. Rafael

Muñoz de Bustillo

Central European

University (CEU)

HU University Department of

Public Policy

Dr. Martin Kahanec

University of Siegen

(U-Siegen)

DE University Institute of

Knowledge

Based Systems

and Knowledge

Management

Prof Dr. –Ing.

Madjid Fathi

Associated

Partners

Aristotle University

of Thessaloniki

GR University Department of

Informatics

Dr. Lefteris Angelis TRA, SEC

NET, DIS

Central European

Labour Studies

Institute (CELSI)

SK Company,

SME

Dr. Marta

Kahancová

TRA, SEC

NET, DIS

Corvinus University

of Budapest (CUB)

HU University Informatics

Institute

Dr. Zoltán Szabó TRA (toge-

ther with

Corvinno),

NET, DIS

Ecorys NL Company Labour & Social

Policy Depart-

ment

Peter Donker van

Heel

TRA, SEC

DAT, NET,

DIS

Ericsson IE Company Ericsson

Academy

Sean Delaney TRA, SEC,

DAT, NET,

DIS


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European Distance

and ELearning

Network (EDEN)

UK Association Dr. András Szűcs DIS, NET,

DIS

European Founda-

tion for the Impro-

vement of Living

and Working Condi-

tions (EuroFound)

IE Foundation Employment

and Compe-

titiveness team

Donald Storrie TRA, SEC,

DAT, NET,

DIS

GITP NL Company Research Dr. Alec W. Serlie TRA, SEC,

DAT, NET,

DIS

Labour Asociados ES Company Ricardo Rodriguez TRA, NET,

DIS

Netpositive HU Company,

SME

Mátyás Török TRA, SEC,

NET, DIS

Randstad NL Company Labour Market Marjolein ten

Hoonte;

TRA, SEC,

DAT, NET,

DIS

University of

Alicante (UAL)

ES University Office for

Research,

Development

and Innovation

prof Dr. Amparo

Navarro Faure

TRA, NET,

DIS

WageIndicator

Foundation

NL Foundation Paulien Osse TRA, DAT,

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

Location of

research

premises

(city/country)

Type of R&D

activities

No. of full-

time

employees

No. of full-

time

employees

in R&D

Annual turnover

(approx, in Euro)

CELSI Bratislava/

Slovakia

Labour

economics 2 2 100 000

Corvinno

Technology

Transfer Center

Budapest/

Hungary

Semantic

technologies,

knowledge

management

10 6 1 000 000

Netpositive Budapest/

Hungary

Software

development 11 5 250 000


Part B - of 21

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

Macro – level Focus

Labour Economics

Meso – levelFocus

Micro – level Focus

Human Resource Management

Sociology of Occupations

Lifelong Learning

Kn

ow

led

ge M

anag

emen

t

Labour Market Matching Processes

Educational Outcomes

Job Require-

ments

Individual skills

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


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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


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(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 school-

based 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 life-

cycles, 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


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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 (Fernández-

Macías 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


Part B - of 21

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 ontology-

based 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, Martínez-

Béjar, 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


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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

cher #

Project Title Host

Institution

Work

Package(s)

Duration

(months)

Start

date


Part B - of 21

ESR1

Leveraging the potential of job knowledge to fit

individuals to jobs: Studies in Personnel

Selection

UvA-ABS 2 36 4

ESR2 Leveraging the potential of job knowledge to fit

individuals to jobs: Studies in training UvA-ABS 2 36 4

ESR3

Identification and mapping of the lifelong

learning transitions of mobile learners : from

trajectories to pathways

TCD 2 36 4

ESR4

An analysis of lifelong learning transitions of

mobile learners: implications and principles for

the design of technologies to support and

facilitate lifelong learning transitions

TCD 2 36 4

ER5 The dynamics underlying the division of tasks

into occupations UvA-AIAS 3 22 4

ER6 Identifying companies’ skill needs UvA-AIAS 3 22 4

ESR7 Labour market driven learning analytics UvA-ABS 3 36 4

ESR8 The determinants of skills-occupation mismatch

in Europe: a job-level approach USAL 4 36 4

ESR9 Skills-occupation mismatch in Europe: the

impact of the 2008 crisis USAL 4 36 4

ESR10 Measuring occupational skill mismatch CEU 4 36 4

ESR11 On workers’ responsiveness to labour market

shortages: gender, age, and ethnicity CEU 4 36 4

ESR12 Adaptive assessment interface between

education and workplace CORVINNO 5 36 4

ESR13 Employment Data Management via Matching

Job role with Educational Competencies CORVINNO 5 36 4

ESR14 Developing a Web-based Multi-country

Occupational Information System Uni-Siegen 5 36 4

Table B.2.2 List of Researchers' Individual Projects

WP4Macro – level

Focus

Labour Economics

WP3Meso – level

Focus

WP2Micro – level

Focus

Human Resource Management

Sociology of Occupations

Lifelong Learning

WP

5 –

Kn

ow

led

ge M

anag

emen

t

ESR 1

ESR 2

ESR 3 ESR 4

ESR 7

ER 6 ER 5

ESR 8 ESR 9

ESR 10ESR 11

ESR 12

ESR 13

ESR 14

Figure B.2.2 Individual Projects distribution across work packages and disciplines

B.2.4. 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


Part B - of 21

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 WP3 Meso Level WP4 Macro

level

WP5 Knowledge

management

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 √ √ 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


Part B - of 21

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

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.

Full partner CNTR Associated Partner CNTR

University of Amsterdam - AIAS

NL

Ecorys NL

WageIndicator Foundation NL

University of Amsterdam - Amsterdam

Business School

NL

GITP NL

Randstad NL

Corvinno Technology Transfer Center

HU

Corvinus University of Budapest (CUB) HU

Netpositive HU

Trinity College Dublin

IE

European Distance and ELearning Network

(EDEN) UK

Ericsson IE

University of Salamanca

ES

European Foundation for the Improvement of

Living and Working Conditions (EuroFound) IE

Labour Asociados ES

Central European University HU CELSI SK

University of Siegen DE

Aristotle University of Thessaloniki (AUT) GR

University of Alicante (UAL) ES


Part B - of 21

B.3 TRAINING

B.3.1. 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


Part B - of 21

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 Leveraging the potential of job knowledge to fit individuals to jobs: Studies in Personnel Selection

Supervisor Dr Stefan Mol, Dr. Gábor Kismihók and Prof. D.N. den Hartog, UvA-ABS

Discipline(s) Human Resource Management

General

description

The objective of this project is to generate support for the job knowledge mediated relationship between General

Mental Ability and Job performance

Relevance to

the network

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.

Methodologies

to be applied

(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.

Nature of data

collection Interviews, surveys, desk research

ESR#2,

WP2.2 Leveraging the potential of job knowledge to fit individuals to jobs: Studies in training


Discipline(s) Human Resource Management and Lifelong Learning

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

(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)


Part B - of 21

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 Interviews, surveys, desk research

ESR#3,

WP2.3

Identification and mapping of the lifelong learning transitions of mobile learners : from trajectories to

pathways

Supervisor Prof. Inmaculada Arnedillo-Sánchez & Prof. Frank Bannister, TCD

Disciplines(s) Lifelong Learning

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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.

Nature of data

collection

Literature review, experiments, surveys, interviews, observations and shadowing

ESR#4,

WP2.4

An analysis of lifelong learning transitions of mobile learners: implications and principles for the design of

technologies to support and facilitate lifelong learning transitions

Supervisor Prof. Inmaculada Arnedillo-Sánchez & Prof. Vincent Wade, TCD

Disciplines Lifelong Learning

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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.

Nature of data

collection

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)

ER#5, WP3.1 The dynamics underlying the division of tasks into occupations

Supervisor Prof K.G. Tijdens, UvA-AIAS

Discipline(s) Knowledge Management and Sociology of Occupations

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

Task frequencies will be compared across jobholders in similar occupations across countries, using interrater

agreement analyses and multilevel models.

Nature of data

collection

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).

ER#6, WP3.2 Identifying companies’ skill needs

Supervisor Prof K.G. Tijdens, UvA-AIAS


Part B - of 21

Discipline(s) Sociology of Occupations

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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.

Nature of data

collection

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.

ESR#7,

WP3.3 Labour market driven learning analytics


Discipline(s) Human Resource Management and Knowledge Management

General

description

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.

Relevance to

the network

Explores Person-Education-Labour market (mis)matches by aggregating individual level data to the meso-level.

Requires knowledge management techniques for big data analysis.

Methodologies

to be applied

Big data analytics, including classification, cluster analysis, data fusion and integration, neural networks, pattern

recognition, predictive modelling, regression, time series analysis and visualisation.

Nature of data

collection

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.

ESR#8,

WP4.1 The determinants of skills-occupation mismatch in Europe: a job-level approach

Supervisor Dr Pablo de Pedraza, USAL

Discipline(s) Labour Economics and Sociology of Occupations

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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.

Nature of data

collection

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.

ESR#9,

WP4.2 Skills-occupation mismatch in Europe: the impact of the 2008 crisis

Supervisor Dr Pablo de Pedraza, USAL

Discipline(s) Labour Economics and Sociology of Occupations

General

description

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.

Relevance to

the network

This PhD project brings a general dynamic context to the overall project, evaluating the evolution of skills-

qualification 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.


Part B - of 21

Methodologies

to be applied

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.

Nature of data

collection

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.

ESR#10,

WP4.3 Measuring occupational skill mismatch

Supervisor Dr Martin Kahanec, CEU

Discipline(s) Labour Economics

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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.

Nature of data

collection

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

Supervisor Dr Martin Kahanec, CEU

Discipline(s) Labour Economics

General

description

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.

Relevance to

the network

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

Methodologies

to be applied

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.

Nature of data

collection

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.

ESR#12,

WP5.1 Ontology based context aware content management

Supervisor Dr. Réka Vas, CUB; Dr. András Gábor, Corvinno.

Discipline(s) Human Resource Management, Knowledge Management, and Lifelong Learning

General

description

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


Part B - of 21

understanding etc.), and b) reordered list of learning objects taking into account the current semantic context of

interest of the user.

Relevance to

the network

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).

Methodologies

to be applied

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..

Nature of data

collection

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.

ESR#13,

WP5.2 Employment Data Management by Matching Job roles to Educational Competencies

Supervisor Dr. András Gábor, Corvinno

Discipline(s) Human Resource Management and Knowledge Management

General

description

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.

Relevance to

the network

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.

Methodologies

to be applied

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).

Nature of data

collection

(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.

ESR#14,

WP5.3 Measuring occupations, using dynamic text fields in web-based data collection

Supervisor Prof Dr. –Ing. Madjid Fathi, Uni-Siegen; Prof K.G. Tijdens, UvA-AIAS

Discipline(s) Knowledge Management, Labour Economics and Sociology of Occupations

General

description

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 web-

survey. 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


Part B - of 21

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.

Relevance to

the network

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.

Methodologies

to be applied

Multiple methodologies are applied, ranging from regression analyses to matching programs for respondent-side

job-titles into the database.

Nature of data

collection

The data collection is derived from the occupation auto completion and search tree tool in the web-surveys of the

industrial partner WageIndicator.


Part B - of 21

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