PhD Project – Navigation Support for informal Learning Networks (2006-2009)
Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks.
TENCompetence will support individuals, groups and organisations in Europe in lifelong competence development by establishing the most appropriate technical and organisational infrastructure, using opensource standards-based, sustainable and innovative technology TENCompetence is a 4-year EU-funded Integrated IST-TEL project that will develop a technical and organisational infrastructure for lifelong competence development. The infrastructure will use open-source, standards-based, sustainable and innovative technology. With this freely available infrastructure the European Union aims to boost the European ambitions of the Knowledge Society, by providing all European citizens, SMEs and other organisations easy access to facilities that enable the lifelong development of competencies and expertise in the various occupations and fields of knowledge.
The TENCompetence infrastructure will support the creation and management of networks of individuals, teams and organisations in Europe who are actively involved in the various occupations and domains of knowledge. These ‘learning networks’ will support the lifelong competency development of the participants from the basic levels of proficiency up to the highest levels of excellence. The network consists of learners, educational institutes, libraries, publishers, domain specific vendors, employers, associations, and all others who deliver services or products in the specific field.
LTfLL – Language Technology for LifeLong Learning (2009-2011)
The LTfLL (Language Technologies for Lifelong Learning) project will create next-generation support and advice services to enhance individual and collaborative building of competences and knowledge creation in educational and organizational settings. The project makes extensive use of language technologies and cognitive models in the services.
The research activities are enveloped by activities that ensure common ground in use cases and pedagogically sound scenarios that steer the design and development of the services and guide the validation; a technical infrastructure for the creation and integration of the services and a validation structure that ensures rigorous evaluation in realistic settings, with several languages supported.
The research in the project is organized in 3 themes, each leading to particular types of services and infrastructures:
In theme 1 services are developed to establish the current position of the learner in a domain. Services will offer semi-automatic analysis and comparison of learner portfolios to the domain knowledge and continuous modelling and measurement of conceptual development.
In theme 2 support and feedback services are developed based on analysis of the interactions of students – using Natural Language Processing (NLP) and Social Network Analysis (SNA) and textual output of students – using Latent Semantic Analysis with contributions from NLP.
In theme 3 a knowledge sharing infrastructure is construed that allows comparison and sharing of private knowledge to give rise to new common knowledge and social learning. Ontologies for formal domain representation are combined with social tagging.
The services are expected to result in improved appreciation of learner requirements, leading to better recommendations on study plans and resources. Progress monitoring based on learning activities, rather than on formal assessments, will improve recommendations for further competence building and improved co-construction of knowledge in social and informal learning. This project has been funded with support from the European Commission.
SCLLLL – Service Centrum for Lifelong Learning Limburg (2010-2012)
Future scenario’s of the region Limburg show a knowledge intensive economy with innovative products and services. In the current situation however we see a low employability and a mismatch between supply and demand on the labour market in terms of qualifications and competences. Also the problem of dejuvenation and ageing is emerging, in Limburg more (and earlier) than in the rest of the Netherlands.
Education plays a crucial role in the socio economic policy of the region. There are several projects in the region to address these problems. Mainly on a sectoral level, like the project Zorgacademie Parkstad Limburg (healthcare and welfare, see below), License to Operate (engineering) and Leisure, but also on specific topics like RPL and ePortfolio.
The risk is a lack of coherence and true focus. The expertise and experiences of the different initiatives need to be bundled. In this way everyone can profit from lessons learned. That’s why the four main educational providers in the region1, from vocational to academic level joined hands and started ‘Lifelong Learning Limburg (LLLL)’. Hogeschool Zuyd coordinates this LLLL initiative.
Goal of the project SCLLL is to built a knowledge infrastructure (Service Centre) and instruments needed for this. The Service Centre will support, stimulate and organize current projects on lifelong learning in the region and initiate future projects. The Service Centre supports employers, employees and jobseekers in developing and assessing competences on a vocational, professional and academic level to match demand and supply on the labour market. Key factors in LLLL are a demand steered and a chain oriented approach.
The HANDOVER project is about: Improving the Continuity of Patient Care Through Identification and Implementation of Novel Patient Handover Processes in Europe. The overall objective is to optimize the continuum
of clinical care at the primary care hospital interface by reducing unnecessary and avoidable treatment – medical errors and loss of life, by identifying and studying best practices and creating standardized approaches to handover communication at the primary care hospital interface, and by measuring the effectiveness of these practices in
terms of costs and impact on patients. HANDOVER will focus on specific areas that will be used for defining best practices that could be applicable to other areas of the health care system at the primary care / hospital interface. The focus will be determined by four elements:
- Specific diseases: In order to properly address to health care continuum specific diseases have been identified in which the entire chain of care (primary care – referral – hospital – discharge – after care by primary care physician) is represented. The selected diseases include chronic health issues such as diabetes, heart diseases, asthma, COPD, poly-pharmacy patients.
- Focus groups: HANDOVER pay special attention to handoffs in elder patients of the age group above 60, young patients below the age of two and on handoffs in patients with multiple diseases (especially co-morbidities). Special attention will also be paid to minority groups and people with communication problems because of language problems and/or hearing/seeing difficulties.
- Specific microsystems: HANDOVER study the primary care / hospital interface from the point of view of different microsystems including transfers of the first aid team to the emergency department.
- Regional setting: HANDOVER include different partners from different regions / countries in Europe so that regional differences throughout Europe are taken into account, as they are vital elements for a successful dissemination of identified best practices.
dataTEL (2010 – 2011)
The growth of data in the knowledge society creates opportunities for new insights through advanced analysis methods based on information retrieval technologies. Educational institutions also create and own huge datasets on their students and course activities. But they make little use of the data when considering new educational services, recommending suitable peers or content, and improving the personalization of learning. Nevertheless, personalized learning is expected to have the potential to create more effective learning experiences, and accelerate the study time for students. In the educational world, only very limited datasets are publicly available and no agreed quality standards exist on the personalization of learning. The SIG dataTEL aims to address these issues by advancing data-driven research to gain verifiable and valid results and to develop a body of knowledge about the personalization of learning. However, Learning Analytics confront researchers with a new set of challenges, for instance, a lack of common dataset formats or policies to share educational datasets, a huge variety of different evaluation methods for comparing diverse personalization techniques, and new ethical and privacy issues that arise from mining information.
The dataTEL SIG builds upon the positive outcomes of the dataTEL Theme Team funded by the STELLAR Network of Excellence. It’s intentions are to foster the cooperation between different Learning Analytics research units and to act as their representative to other relevant communities. The SIG for Data-driven Research and Learning Analytics (dataTEL) is intended as a network to collect, validate, and discuss different Learning Analytic approaches based on datasets from real educational settings.
- Fostering of a research network on educational dataset driven research
- Improving the exchange with relevant research communities
- Representing dataTEL researchers to promote the release of open datasets from educational providers
Privacy and Ethics:
- Contributing to policies on ethical implications (privacy and legal protection rights)
- Suggesting guidelines for the anonymisation of data and reusing publicly available data for Learning analytic research Evaluation of Technologies
- Fostering a shared understanding of evaluation methods in Learning Analytics
- Encouraging data competitions similar to TREC and CLEF to compare TEL research and guide people in evaluating and comparing their results
- Fostering the standardizations of datasets to enable exchange and interoperability
- Clustering of educational datasets
- Evaluate how linked data can be applied for the SIG goals
- Fostering technology for data research to filter, adapt, convert, visualize and self-extract datasets
- Promoting real-world data applications that show a measureable impact on the TEL target groups
The unexploited potential of educational datasets for theory building in Technology Enhanced Learning.
Knowledge Workers use for their learning processes increasingly information from the Internet. They follow specific blogs, twitter feeds, and take advantage of other Web2.0 instruments to share knowledge. These networked social interactions are covered in the Learning Network concept (Koper, 2009). The Knowledge Workers leave traces on the Internet that are stored in the applied web environments. Some of this data is publicly available on the Internet and it can be used as models of a learner. Recommender systems can take advantage of such models by guessing the most important topics and recommending related content from the Internet to the lifelong learners.
Until now most recommender studies in TEL base their learner model on literature reviews on learner characteristics and combine that with certain recommender algorithms. As a consequence, there are plenty of recommender studies in TEL published but the outcomes are barely comparable with each other because each of them follows an unique approach (Manouselis et al., 2011). The reported results are hardly repeatable and comparable to together experiments because the underlying datasets are not published with them. Although they offer valuable insights in the usefulness and relevancy of recommender systems for learning, stronger conclusions about the validity and generalizability of recommender experiments are needed to achieve valid knowledge for personalization of learning (Drachsler et al., 2010). The emerging amount of data in Web2.0 services, Learning Management System, and Personal Learning Environments enable us to turn the research process around and create bottom-up learner models of lifelong learners form the available data sets. Thus, the AlterEgo project is about the evaluation visualisation and analysis of educational data sets to create more reliable and comparable evaluation criteria for recommender systems for Knowledge Workers.
Funded by the Netherlands Laboratory for Lifelong Learning (NeLLL)
The recent EU publication of the Innovation Union Scoreboard brings the need for improved innovation initiatives in higher education institutions (HEIs) across Europe into sharp focus. The aim of this proposal is to address this ‘innovation emergency’ by developing a framework for undergraduate innovation in biomedical engineering which is suitable for implementation in HEIs across Europe. We propose the design and delivery of a novel educational module which offers practical, interdisciplinary and effective learning opportunities to undergraduate engineering and medical third level students across Europe. Specifically, BIOAPP describes a module which is tailored towards integration of systematic innovation teaching tools with biomedical devices design learning in a multidisciplinary learning environment. While alternative educational modules in this space have emphasised the development of commercially viable devices as a core deliverable, the key objectives in BIOAPP are learner-centered, focused on learning outcomes. The proposed module has a pedagogical focus which incorporates the following innovating elements: (1) creation of an integrative interdisciplinary learning environment; (2) introduction of a structured innovation process; (3) clinical immersion; (4) development of commercialisation and business plan development skills. The involvement of SME partners during both the development and delivery of module content ensures the transfer of industry-centered and employability skills. Bringing together a consortium of partners from medical education and engineering schools across several European countries, as well as enterprise partners, BIOAPP combines the expertise of partners in these disciplines to formulate a targeted educational programme which addresses specifically the unsolved problem of educating for creativity and innovation (as applied to biomedical design) in third level education.
Open Discovery Space (2012-2015)
Is a project to develop a socially-powered and multilingual open learning infrastructure to boost the adoption of eLearning resources. It is a larger European initiative with 51 organisations from 23 countries over 36 months and will advance various research initiatives like Liked Data, Learning Analytics, and dataTEL.
Open Discovery Space will achieve its ambitious goals by engaging teachers and pupils in the co-creation of innovative, new educational practices rather than simply introducing them to the ‘new.’ In doing so, Open Discovery Space will advance the modernization of school education by increasing the digital competences of key stakeholders whilst simultaneously stimulating demand for innovative eLearning resources. Ultimately, Open Discovery Space will engage stakeholders in the production of new user-generated educational activities in a socially-empowered, multilingual environment and empower them with integrated access to unique eLearning resources from educational repositories around the world.
The goal of LinkedUp (Linking Web Data for Education Project) is to push forward the exploitation of public, open data available on the Web by providing a data competition and evaluation framework which identifies innovative success stories of robust, Web-scale information management applications. LinkedUp will in particular focus on the education sector by proposing an open competition aimed at eliciting Web-data driven application for personalised, open and online university-level studies. This goal directly requires to solving critical issues with respect to Web-scale data and information discovery and retrieval, interoperability and
matchmaking, data quality assurance and performance.
Building on a strong alliance of institutions with expertise in areas such as open Web data management, data integration and Web-based education, LinkedUp will devise, organise and deploy an open application competition (The LinkedUp Competition), to elicit, evaluate and promote innovative technologies for the integration of open Web data within large scale, robust and efficient educational applications. The main objectives of LinkedUp are:
- Open Web Data Success Stories: gathering innovative and robust scenarios of deployed tools integrating and analysing large scale, open Web data (in the education sector).
- Evaluation Framework for Open Web Data Applications: providing a complete framework for the evaluation of large-scale open Web data applications, taking into account educational aspects as
well as generic, technological aspects.
- Technology Transfer in the Education Sector: demonstrating and promoting the benefit of open Web data technologies in education, and provide a reusable testbed in this domain.
The PATIENT project is an implementation project in the health domain that aims to innovate practice through short product cycles that directly connect education-research-innovation processes. The main objective is to overcome a pressing medical research topic – accurate and safe handover processes.
Therefore, it aims to integrate the various components (the HANDOVER Toolbox and its content plus various mobile applications) in an innovative and practice oriented study module that allows students to participate, contribute and benefit from the latest research findings and apply this innovative knowledge directly in the workplace.
The European inter-institutional nature of PATIENT plays a significant role for rising awareness of handover issues across health care professions in Europe. By providing a general training schema and innovative learning tools and expert and community content, PATIENT stimulates a standardisation of the handover process across different European countries. Its goal is to define learning outcomes and quality criteria which are transnationally applicable. The emphasis on an adaptable, flexible approach significantly enhances the potential to facilitate transfer into higher education institutions across Europe but also to other domains that are involved in handover processes like retirements homes, General Practice, people with disability and paediatric communities.