Gábor Kismihók

Meta-Analyses from a Collaborative Project in Mobile Lifelong Learning

This paper focuses on the use of mobile technologies in relation to the aims of the European Unions Lifelong Learning programme. First, we explain the background to the notion of mobile lifelong learning. We then present a methodological framework to analyse and identify good practices in mobile lifelong learning, based on the outcomes of the MOTILL project (‘Mobile Technologies in Lifelong Learning: Best Practices’). In particular, we give an account of the methodology adopted to carry out meta-analyses of published literature and accounts of mobile learning experiences. Furthermore, we present the results of an implementation of our Evaluation Grid and the implications arising from it in terms of management, pedagogy, policies and ethical issues. Finally, we discuss lessons learnt and future work.

Ontology-based mobile learning and knowledge testing

The article presents the architecture of a complex, mobilised content authoring system. This approach changes the current structure of content development at Corvinus University of Budapest, serving the needs of mobile learners better by enabling context and location aware learning. Through its main components - educational ontology, content management system, adaptive testing system, mobilised learning management system - the system is also capable to tackle the challenges of communication, collaboration and content delivery regardless of time and space. Certain components were even tested by business informatics BSc students and the outcomes of this pilot are also described in detail.

Ethical and privacy issues in the application of learning analytics

The large-scale production, collection, aggregation, and processing of information from various learning platforms and online environments have led to ethical and privacy concerns regarding potential harm to individuals and society. In the past, these types of concern have impacted on areas as diverse as computer science, legal studies and surveillance studies. Within a European consortium that brings together the EU project LACE, the SURF SIG Learning Analytics, the Apereo Foundation and the EATEL SIG dataTEL, we aim to understand the issues with greater clarity, and to find ways of overcoming the issues and research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions to advance the application of learning analytics technologies.

Text Mining in Organizational Research

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Antecedents of job search self-efficacy of Syrian refugees in Greece and the Netherlands

The goal of the current study was to investigate the relationships among psychological resources, career barriers, and job search self-efficacy in a sample of post-2014 Syrian refugees. Participants included 330 refugees in Greece and the Netherlands. Data were obtained using paper-based surveys, with all measures translated into Arabic. Drawing from career construction theory (Savickas, 2005), we hypothesized that adaptive readiness, operationalized in terms of psychological capital, would be positively related to job search self-efficacy through career adaptability. In addition, social and administrative career barriers were hypothesized to moderate the first stage of the indirect effect between psychological capital and job search self-efficacy, such that this relationship is weaker when refugees experience higher career barriers. Results indicated that individuals with higher psychological capital more confidently engaged in job search behavior in the destination country, mostly due to their enhanced career adaptability. However, this relationship weakened when participants experienced higher social barriers and strengthened when they experienced higher administrative barriers. The findings provide further support for the career construction model of adaptation (Savickas & Porfeli, 2012) and pinpoint career adapt-ability resources as critical self-regulatory strengths that help individuals in this particularly vulnerable group adapt to occupational transitions. Moreover, the results highlight the potentially detrimental role of social barriers in this process. Based on the results, we offer implications for formulating training and career construction theory-based career counseling focused on enhancing career adaptability and psychological capital.

Text Classification for Organizational Researchers: A Tutorial

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output.

Integrating knowledge management in the context of evidence based learning: two concept models for facilitating the assessment and acquisition of job knowledge

Within the field of Human Resource Management (HRM), the role of individual knowledge has received limited research attention despite offering the promise of superior job performance and improved managerial decision-making. In part, this lack of research may be attributed to the difficulty and laboriousness inherent to the adequate and accurate modeling of job relevant knowledge, particularly since such knowledge by definition varies from job to job. Despite this caveat, there is much to be gained from a knowledge based approach to (managing) human resources. The current paper presents two ontology based concepts for modeling job relevant knowledge, namely Meta-Practitioner and Med-Assess. The former focuses on availing to a practitioner audience the evidence that has accumulated in the academic literature, whereas the latter focuses on the facilitation of personnel selection and training in the medical field through a detailed assessment of individual job knowledge and general mental ability. Ultimately both concepts are aimed at knowledge provision to job applicants and incumbents alike. Having discussed the concepts, the paper summarizes the gains that may be expected from their implementation by presenting an integrated framework. The framework focuses on integrating aspects of Knowledge Management (KM) in the context of Evidence Based Learning (EBL) for business organizations. The paper concludes by addressing the challenges that lie ahead, highlighting some of the limitations of this approach and offering suggestions for further research.

Six Scenarios of Exploiting an Ontology Based, Mobilized Learning Environment

In this article, six different exploitation possibilities of an educational ontology based, mobilized learning management system are presented. The focal point of this system is the educational ontology model. The first version of this educational ontology model serves as a foundation for curriculum development and adaptive knowledge testing. The extended educational ontology model is the foundation of a personnel selection and training system, developed by the OntoHR project. This system reveals the candidates competence gap and the missing learning outcomes of their qualifications in the light of a particular job role Information System Analyst. Within the frame of the Contsens project, the educational ontology model supports a context sensitive and location based learning content delivery. The results of these projects can be combined in an educational ontology based, mobilized selection and learning management system, built on transparent content and location dependent curricula.

Learning analytics: European perspectives

Since the emergence of learning analytics in North America, researchers and practitioners have worked to develop an international community. The organization of events such as SoLAR Flares and LASI Locals, as well as the move of LAK in 2013 from North America to Europe, has supported this aim. There are now thriving learning analytics groups in North American, Europe and Australia, with smaller pockets of activity emerging on other continents. Nevertheless, much of the work carried out outside these forums, or published in languages other than English, is still inaccessible to most people in the community. This panel, organized by Europes Learning Analytics Community Exchange (LACE) project, brings together researchers from five European countries to examine the field from European perspectives. In doing so, it will identify the benefits and challenges associated with sharing and developing practice across national boundaries.

An innovative ontology-driven system supporting personnel selection: the OntoHR case

This paper describes the initial development of an HRM system that aims to decrease the gap between higher vocational education and the labour market for a specific job in the ICT sector. This paper focuses specifically on the delineation of a process model and the selection of a suitable job role (information system analyst) that is valid across organisational and cultural boundaries. The process model implies various applied uses of this ontology-based system, including mapping qualifications in vocational education to current and valid job roles, testing and evaluating the student applicant on the basis of labour market driven competencies, and providing ad-hoc support to educational institutions by elucidating the weaknesses of particular VET curricula.