Organic.Lingua
Organic.Lingua is an international ICT PSP project with the aim to build an automated multi‐lingual service that facilitates the usage, exploitation and extension of digital educational content related to Organic Agriculture and Agroecology. The project builds upon the Organic.Edunet Web portal that was developed in the context of the eContentplus project "Organic.Edunet”. The Organic.Edunet portal uses a federated, standards‐based approach that allows the incremental growth of educational content and the collection and affiliation of additional members. Currently, the Organic.Edunet provides access to more than 7,000 resources from 11 institutional collections. Organic.Edunet is now available in nine languages ‐ Greek, Spanish, Swedish, Norwegian, German, Estonian, English, Hungarian, and Romanian. However, the current process makes translation error prone and time consuming, and misses opportunities to enhance the quality and efficiency of cross‐language functions with available technology.
Organic.Lingua aims to capitalise on international demand for Organic.Edunet by transforming it into a truly multilingual service: Applying automated language tools to translate descriptions will enable multi‐lingual search for the largest part of the content base and fulfill the growing demand for a genuinely multilingual service that Organic.Edunet has identified. The Organic.Lingua project aims to do achieve this end by extending the current Organic.Edunet portal to fill the abovementioned gaps in multilingual support and cross‐language resource organization and search by significantly expanding its linguistic coverage. It particularly aims at analyzing and re‐engineering the service infrastructure and related facilities and business models in order to support multilingual access and use more widely and effectively.
Organic.Lingua will use the Organic.Edunet platform – and its existing transnational service and user community – to enhance the linguistic coverage of existing and planned Organic.Edunet services. In this way, it aims to cover public sector needs, by providing agricultural and environmental researchers and educators with a pan‐European information, communication and collaboration platform that already involves users and operators from a wide range of countries as well as to create new business opportunities for the relevant private sector by demonstrating how a commercial system that serves a global education market can be deployed.
MAKIN' IT
The general objective of the MAKIN’ IT project is to develop a consistent database of academic knowledge, driven by semantic algorithms, to efficiently manage and share academic content, and to achieve superior recommendations based on personalised user profiles and user preferences.
WIQ-EI: Web Information Quality Evaluation Initiative
The mission of WIQ-EI is to develop mechanisms for estimating the quality of textual Web documents and to evaluate these mechanisms for their effectiveness and efficiency. This will be done on a global scale by organising researcher exchanges between renowned organisations from European and third countries which have their expertise in topic relevant fields.
WIQ-EI's motivation starts from the observation that today’s information and data pools on the Web focus on the quantity of information rather than its quality; a fact observable through the increasing size of the blogosphere, the number of growing artificially created data, the well established copy & paste syndrome, the lack of semantically enriched data as well as the intentional and unintentional information misuse. The assessment of the quality of information is especially important because decisions are often based on information from multiple and sometimes unknown sources, though, the reliability and accuracy of the information are questionable. However, the web lacks quality-dependent filter mechanisms, automatic identification of misuse patterns, as well as tools to establish user trust in information and authors.
Given this motivation, WIQ-EI aims at the development of mechanisms to automatically estimate information quality aspects, which is inexpensive, complete and can be performed continuously to cope with large amounts of data. Thus, WIQ-EI will result in a set of algorithms, tools and test data sets to estimate the quality of textual Web documents and evaluate the algorithmic effectiveness and efficiency.
From a technical point of view three thematic objectives were identified.
- Objective I: Development of Web Content Information Quality Measures
- Objective II: Plagiarism Detection and Authorship Attribution
- Objective III: Multilingual Opinion and Sentiment Mining
From an organisational point of view, researchers from European – Austria, Germany and Spain – and third countries – Mexico, Argentina and India – will be exchanged. Transfer activities include the organisation of workshops embedded in international conferences. Thus, the WIQ-EI contributes to strengthening relationships between organisations beyond Europe and to transfer knowledge beyond the project partners.
The project is funded within the FP 7 IRSES Marie Curie Scheme.
MIRROR – Reflective Learning at Work
MIRROR will enable employees to learn lessons from their own and others experiences. Dr. Stefanie Lindstaedt from the Know-Center is the scientific leader of this innovative project.
MIRROR is an EU funded project in the Technology-Enhanced Learning Unit. Its duration is four years (July 2010 – June 2014) with a budget of around 9 million Euro. 15 research and industry partners from 6 European countries are involved.
The overall objective of MIRROR is to empower and engage employees to reflect on past work performances and personal learning experiences in order to learn in “real-time” and to creatively solve pressing problems immediately. The Know-Center will focus on collecting, analyzing and visualizing data about the activities of knowledge workers (nurses, care personnel, IT-consultants, workers in emergency management, etc.). Concerning the data collection, we emphasize on the one hand that data are collected unobtrusively and integrated into existing (IT) work environments, and on the other hand an easily intelligible and configurable privacy and data security model in order to protect the collected data due to their sensitive nature. With respect to data analysis, we aim to lift the collected low-level data to a more abstract and conceptual level, the knowledge level (e.g., inferring workers’ competencies, or abstract activities like “plan operation in XY on day Z”). These inferences can be taken via logic-based rules, statistical methods, or machine learning methods. Finally, the abstract knowledge about knowledge worker activities will be visualized in an intuitive and interactive manner, such that the knowledge workers can understand the data collected and knowledge inferred about them, and edit, delete, extend, etc. the data. The functionalities of collecting, analyzing and visualizing user and u sage data will be provided as services and Apps in the MIRROR App-Sphere, and in the course of the project will be published as open-source to the international research community.
The collected data and knowledge inferred through analysis, as well as the interactive visualization, serves in the context of MIRROR primarily to support the single knowledge worker in reflecting on past activities in his or her work environment.
MIRROR will be the first technology-enhanced learning approach that can be used in highly dynamic working situations where no teachers, no formal content, and no explicit knowledge are available.
MIRROR builds on existing approaches to work-integrated learning as developed in the IPs APOSDLE, PROLIX and MATURE, and will refine them.
STELLAR NoE
STELLAR is an EU-funded Network of Excellence aimed at unifying and sustaining the multidisciplinary field of Technology Enhanced Learning. The Know-Center is involved with ... - identifying the grand research challenge for TEL and with
- providing a variety of Science 2.0 tools and technologies to support researchers in TEL.
MATURE – Continuous Social Learning in Knowledge Networks
MATURE is a large-scale integrating project (IP), co-funded by the European Commission, Unit for Technology-Enhanced Learning (TEL) within Call 1 of the Seventh Framework Programme (FP7). It runs from April 2008 to March 2012. The agility of organizations has become the critical success factor for competitiveness in a world characterized by an accelerating rate of change. Agility requires that companies and their employees together and mutually dependently learn and develop their competencies efficiently in order to improve productivity of knowledge work. Failures of organisation-driven approaches to technology-enhanced learning and the success of community-driven approaches in the spirit of Web 2.0 have shown that for that agility we need to leverage the intrinsic motivation of employees to engage in collaborative learning activities, and combine it with a new form of organisational guidance. For that purpose, MATURE conceives individual learning processes to be interlinked (the output of a learning process is input to others) in a knowledge-maturing process in which knowledge changes in nature. This knowledge can take the form of classical content in varying degrees of maturity, but also involves tasks & processes or semantic structures. The goal of MATURE is to understand this maturing process better, based on empirical studies, and to build tools and services to reduce maturing barriers. MATURE’s outcome will be
- an analysis of real-world maturing practices, resulting in a sound general conceptual model of the knowledge maturing process and ways to overcome barriers to it (particularly including motivational and social)
- a Personal Learning & Maturing Environment (PLME), embedded into the working environment, enabling and encouraging the individual to engage in maturing activities within communities and beyond
- an Organisational Learning & Maturing Environment (OLME), enabling the organisation to analyze and to take up community activities, to reseed innovation processes and to apply guiding strategies
- reusable Maturing Services for seeding and reseeding, and creating awareness of maturing-relevant individual and community activities
MATURE brings together an experienced multi-disciplinary team of outstanding experts. To leverage their combined skills, it utilizes a participatory design methodology, involving companies inside and outside the consortium. MATURE has also begun to set up an associate partner network to maximize the project impact.
APOSDLE – A new way to work, learn and collaborate
Lifelong Learning has become an essential ingredient for success within our knowledge society. The EU project APOSDLE develops a software platform and tools to support you to learn @ work: Learn within the context of your immediate work and within your current work environment.
The new Advanced Process- Oriented Self- Directed Learning Environment will provide you with practical guidance, learning content and expert advice when you need it and where you need it. APOSDLE offers individual learning support to people working with information and contributing new content to an organisation’s knowledge pool. These “knowledge workers” may include e.g. engineers, researchers, software developers, consultants, or designers. It follows a “learn @ work” approach meaning that learning takes place in the user’s immediate work environment and context.
The key distinction to traditional eLearning systems is that APOSDLE provides integrated technological support for all three roles a knowledge worker fills at the workplace: the role of the worker, the role of the learner, and the role of the expert. This integrated support is represented by the three rings of the APOSDLE logo: work, learn, collaborate.
