Summary of Eduworks project

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Summary of Eduworks project

Summary of Eduworks project

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  • 1. 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
  • 2. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 2 of 21 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
  • 3. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 3 of 21 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)
  • 4. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 4 of 21
  • 5. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 5 of 21
  • 6. EDUWORKS – MULTI-PARTNER ITN (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 Part B - Page 6 of 21
  • 7. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 7 of 21
  • 8. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 8 of 21
  • 9. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 9 of 21
  • 10. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 10 of 21
  • 11. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 11 of 21
  • 12. EDUWORKS – MULTI-PARTNER ITN 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. Part B - Page 12 of 21
  • 13. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 13 of 21
  • 14. EDUWORKS – MULTI-PARTNER ITN 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 Dr Stefan Mol, Dr. Gábor Kismihók and Prof. D.N. den Hartog, UvA-ABS 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) Part B - Page 14 of 21
  • 15. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 15 of 21
  • 16. EDUWORKS – MULTI-PARTNER ITN 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 Dr Stefan Mol, Dr. Gábor Kismihók and Prof. D.N. den Hartog, UvA-ABS 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. Part B - Page 16 of 21
  • 17. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 17 of 21
  • 18. EDUWORKS – MULTI-PARTNER ITN 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 Part B - Page 18 of 21
  • 19. EDUWORKS – MULTI-PARTNER ITN Relevance to the network Methodologies to be applied Nature of data 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. Part B - Page 19 of 21
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