The European Network for Catalysing Open Resources in Education (ENCORE+) responds to the European priorities of opening up and modernising education and training through the creation of a network that supports innovation and entrepreneurship with OER.

ENCORE+ starts from the insight that while there are viable, established strategies for OER there is no integrated European OER university-business ecosystem able to identify, catalyse and share best practices.

ENCORE+ will support the uptake of OER through business and academia by sharpening value propositions and implementation strategies for OER in higher education and the world of work.

  1. ENCORE+ develops a European OER innovation area by connecting stakeholder communities & fostering knowledge exchange
  2. ENCORE+ realises a new vision for collaboration and connection between OER repositories in a European OER Ecosystem, encouraging entrepreneurship and empowerment through OER
  3. ENCORE+ fosters the uptake of OER in Europe by stimulating the integration of organisational strategies for OER in business & academia, encouraging both to co-learn from implementations
  4. ENCORE+ establishes open, distributed & trusted community review strategies for OER and involves businesses and HEIs in dialogues on quality and innovation
  5. ENCORE+ engages businesses in the OER ecosystem, demonstrating the innovation potential of open content for human resource development

ENCORE+ will bring about an overarching consensus through sharing expertise across business and higher education; and will sustain community ‘Circles’ that will apply, validate and disseminate outcomes.

ENCORE+ builds the foundation for the European OER Ecosystem which can best support innovation and inclusion in education and training; improve digital skills; improve employability; and share the benefits of open online learning with as many Europeans as possible.

  • Developing a validated, shared vision and roadmap for OER in Europe
  • Providing a sustainable European collaboration model which responds to authentic needs
  • Working with diverse stakeholders to create and pilot the ENCORE+ OER Quality Framework
  • Establishing European OER Strategy Guidelines for higher education & businesses
  • Establishing a well connected European OER Ecosystem of technologies & communities

ENCORE+ will engage within a strategic and sustainable way a number of relevant academia and business stakeholders. We will support their collaborative activity in line with the project five specific objectives:

  • Objective 1: Setup and support a sustained and well mapped European OER stakeholders network based on well connected higher education and business communities (supported in particular through WP2).
  • Objective 2: Foster collaboration and connection of OER European repositories into a European OERrepository ecosystem (WP3).
  • Objective 3: Stimulate the integration of OER institutional strategies within business and academia (WP 4).
  • Objective 4: Demonstrate the innovation potential of OER within business settings and promote successful OER business models (WP 6).
  • Objective 5: Establish an open, distributed and highly trusted community-based OER quality review paradigm and working mechanism (WP 5).


  1. ICDE – International Council for Open and Distance Education, Norway
  2. DHBW – Baden-Wuerttemberg Cooperative State University, Germany
  3. OUUK – The Open University, United Kingdom
  4. UNIR – International University of La Rioja, Spain
  5. K4A – Knowledge 4 All Foundation, United Kingdom
  6. JOUBEL – Joubel AS, Norway
  7. FPM – Fondazione Politecnico di Milano, Italy
  8. CANVAS – Instructure Global, Norway
  9. DCU – Dublin City University, United Kingdom
  • Work package 1 – Project Management and Administration
  • Work package 2 – Building the European Network for Catalysing Open Resources in Education
  • Work package 3 – Technology for the Future European OER Repository Ecosystem
  • Work package 4 – Open Educational Policies and Practices for the Future OER Ecosystem
  • Work package 5 – Quality for the Future European OER Ecosystem
  • Work package 6 – Innovation in a European OER Ecosystem
  • Work package 7 – Quality management and evaluation of the ENCORE+ implementation
  • Work package 8 – Information, dissemination & exploitation

The HumanE AI Net brings together top European research centres, universities and key industrial champions into a network of centres of excellence that goes beyond a narrow definition of AI and combines world-leading AI competence with key players in related areas such as HCI, cognitive science, social sciences and complexity science. This is crucial to develop a truly Human Centric brand of European AI.

We will leverage the synergies between the involved centres of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans both individually and societally and adheres to European ethical values and social, cultural, legal, and political norms.

The core challenge is the development of robust, trustworthy AI capable of what “understanding” humans, adapting to complex real-world environments, and appropriately interacting in complex social settings.

The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self-determination.

The HumanE AI Net project will engender the mobilization of a research landscape far beyond direct project funding, involve and engage European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that provides a many-fold return on investment for the European economy and society.

We will make the results of the research available to the European AI community through the AI4EU platform and a Virtual Laboratory, develop a series of summer schools, tutorials and MOOCs to spread the knowledge, develop a dedicated innovation ecosystem for transforming research and innovation into an economic impact and value for society, establish an industrial Ph.D. program and involve key industrial players from sectors crucial to the European economy in research agenda definition and results evaluation in relevant use cases.

Keywords: Artificial Intelligence & Decision support, Learning, development and adaptation, Knowledge representation and reasoning, Robotic perception, Natural language processing, Human computer interaction, Human Centric AI, Ethical AI, Ubiquitous Computing

Scientific partners include 53 institutions:

1 – Deutsches Forschungszentrum für Künstliche Intelligenz GMBH (Coordinator), DE
2 – AALTO Korkeakoulusaatio SR, FI
3 – Airbus Defence and Space SAS, FR
4 – Algebraic AI S.L., ES
5 – Athena-Erevnitiko Kentro Kainotomias Stis Technologies Tis Pliroforias, Ton Epikoinonion Kai Tis Gnosis, EL
6 – Vysoke Uceni Technicke V Brne, CZ
7 – Barcelona Supercomputing Center – Centro Nacional De Supercomputacion, ES
8 – Közép-Európai Egyetem (CEU), HU
9 – Consiglio Nazionale Delle Ricerche, IT
10 – Centre National De La Recherche Scientifique CNRS, FR
11 – Agencia Estatal Consejo Superior Deinvestigaciones Cientificas, ES
12 – Univerzita Karlova, CZ
13 – Consorzio Internuviersitario Nazionale Informatica, IT
14 – Eotvos Lorand Tudomanyegyetem, HU
15 – Eidgenoessische Technische Hochschule Zuerich, CH
16 – Fondazione Bruno Kessler, IT
18 – Fraunhofer Gesellschaft Zur Foerderung der Angewandten Forschung E.V., DE
19 – Generali Italia S.p.A., IT
20 – German Entrepreneurship GMBH, DE
21 – INESC TEC – Instituto De Engenhariade Sistemas E Computadores, Tecnologia E Ciencia, PT
23 – Institut National De Recherche En Informatique Et Automatique, FR
24 – Instituto Superior Tecnico, PT
25 – Institut Jozef Stefan, SI
26 – Knowledge 4 All Foundation, UK
27 – Ludwig-Maximilians-Universitaet Muenchen, DE
28 – Orebro University, SE
29 – Philips Electronics Nederland B.V., NL
30 – SAP SE, DE
31 – Sorbonne Universite, FR
33 – Thales Six Gts France SAS, FR
34 – Telefonica Investigacion Y Desarrollo SA, ES
36 – Technische Universitat Berlin, DE
37 – Turkiye Bilimsel Ve Teknolojik Arastirma Kurumu, TR
38 – Technische Universiteit Delft, NL
39 – Technische Universitaet Kaiserslautern, DE
40 – Technische Universitaet Wien, AT
41 – University College Cork – National University of Ireland, Cork, IE
42 – Kobenhavns Universitet, DK
43 – Universite Grenoble Alpes, FR
44 – Universiteit Leiden, NL
45 – Umeå Universitet, SE
46 – Alma Mater Studiorum – Universita Di Bologna, IT
47 – Universita Di Pisa, IT
48 – University College London, UK
49 – Uniwersytet Warszawski, PL
50 – The University of Sussex, UK
51 – Universidad Pompeu Fabra, ES
52 – Vrije Universteit Brussel, BE
53 – Volkswagen AG, DE

The focus of ELISE is excellence in research into the foundations and practical implications of artificial intelligence.

The goal of ELISE is to make Europe competitive by setting up a Powerhouse of AI. ELISE employs the best European research in machine learning to create a network of artificial intelligence (AI).

The network is set up to be attractive to students and experienced researchers, to sustain itself at the highest level research in academia, and to spread its knowledge and methods in research, industry and society.

Where ELISE starts from machine learning as current core technology of AI, the network is inviting all ways of reasoning, considering all types of data, applicable for almost all sectors of science and industry, while being aware of data safety and security, and while striving to explainable and trustworthy outcomes.

The main objectives of ELISE are:

  1. Establish a network of excellent researchers and laboratories that act as local anchors across Europe, representing the community, provide experts within society, and coordinate a joint effort across the continent
  2. Build an attractive training network for junior scientists that keeps them in Europe, by offering the highest quality training for Academic Excellence in Machine Learning
  3. Make industry involved with elite academic research so that participating companies’ AI/ML research competences will rival those of large industry laboratories
  4. Establish a sustainable ecosystem of machine learning stakeholders covering the value network to facilitate and accelerate a broad integration of machine learning technologies
  5. Make concrete research-based steps to obtain ethical, robust, and trustworthy AI / ML methods and practices.

Keywords: artificial intelligence, decision support, learning, development, adaptation

Scientific partners include 23 institutions from 10 EU countries:

1 – Aalto University (Aalto), FI
2 – University of Tübingen (EKUT), DE
3 – University of Amsterdam (UvA), NL
4 – Czech Technical University (CTU), CZ
5 – Technical University of Denmark (DTU), DK
6 – Johannes Kepler Universität (JKU), AT
7 – University of Cambridge (UC), UK
8 – Consorzio Interuniversitario Nazionale Per L’informatica (CINI), IT
9 – Universitat de València (UVEG), ES
10 – Max Planck Society (MPG), DE
11 – Radboud University Nijmegen (RU), NL
12 – ETH Zürich (ETH), CH
13 – Oxford University (UOXF), UK
14 – University College London (UCL), UK
15 – Knowledge 4 all Foundation (K4A), UK
16 – Saidot OY (saidot), FI
17 – Oticon S/A (Oticon), DK
18 – FundingBox Accelerator (FBA), PL
19 – Siemens AG (Siemens), DE
20 – Zalando SE (Zalando), DE
21 – Fraunhofer (FRA), DE
22 – Spinverse (SPV), FI
23 – EnliteAi (Enlite), AT


What is this hackathon about?

A number of countries are developing Open Education with AI. The ability to generate dynamically created courses will lead to a number of innovative solutions to build capability and skills. The hackathon will comprise three categories that seek to prototype knowledge exchanges that build capability across administrative, health and engineering skills, with application to NGOs and their staff as an example of its use. The solutions will account for multilingual and literacy needs alongside various types of educational bias.

Who is supporting this hackathon

This hackathon supported by the UK Science & Innovation Network at the British Embassy in Paris, the United Nations ANCSSC, X5GON and UCL Computer Science with the support of Knowledge 4 All Foundation intends to allow students at several international universities to take part in an early requirements capture process, leading to design and build stage to develop a working prototype that supports Open Education with AI.

X5GON Overview

X5Gon is a breakthrough EU H2020 project, creating a solution that will help users/students find what they need not just in Open Education Resource (OER) repositories, but across all open educational resources on the web. This solution will adapt to the user’s needs and learn how to make ongoing customized recommendations and suggestions through a truly interactive and impactful learning experience.

This new AI-driven platform will deliver OER content from everywhere, for the students’ need at the right time and place. This learning and development solution will use the following solutions to accomplish this goal:

  • Aggregation: It will gather relevant content in one place, from the projects case studies as well as external providers and other preferred resources.
  • Curation: AI and machine learning will be key to curate relevant and contextual content and external students at the right time and point of need.
  • Personalization: It will make increasingly personalized recommendations for learning content to suit students’ needs, based on the analysis of relevant factors.
  • Creation: Large, small and medium-sized universities have tacit knowledge that can be unlocked and re-used. This approach will allow any organization to release and build their own content libraries quickly and conveniently to share with the world and vice versa.

Hackathon Process

There are 3 themes for students to choose from, which are:

  1. NGO Knowledge exchange: How can we build capability through the exchange of knowledge between established and less accomplished organisations and their staff?
  2. Health knowledge exchange: How can we build capability where there is poor access to healthcare training and education?
  3. Engineering knowledge exchange: How we can we build capability through engineering education for all, including access to international conference coverage ?

There are 4 Hackathon Events in this series.

For the first event, first year (new) students are taking part in a Ideation session, on September 24th to come up with requirements and early sketch designs. This session is to be done in pairs and will be submitted on Moodle.

The second event, Design Prevision, on October 16th , will be undertaken by second year CS student teams of 3s where they will take part in design and HTML walk-through building a mash up or mock up of the projects features. There will be special focus lab sessions on October 28th, 29th and 30th where invited guest speakers and industry mentors will be available to support the teams. They will submit by November 10th on Moodle.

The third event, Build and Test Hackathon, takes place over 48 hours on November 25th and Tuesday 26th November  with several leading companies joining to support the mentoring of the teams with the actual build of their solutions.

For the final fourth event, any partner country can submit up to 3 teams to the final event at the British Embassy in Paris in February 2020. The final teams will take part in a final Hackathon in Paris where all of the European teams will compete in a new one day Open Education and AI challenge that will be presented back to the judging panel on Day 2. The judging panel will announce the overall winning team in Paris.

The themes that back our students will be supported by industry leads from those countries that will provide the students with data, methods, materials, and, most importantly, their mentorship.

It is expected of the participating universities to organise events 1-3, capturing the evolution of their students projects portfolio for review with an abstract, requirements features list, early prototype sketches and application building. At least one of the projects from each of the countries must match the given hackathon series themes.

The students then have until January 10th  to submit their technical build demos with a supporting video and GitHub repository. The top 3 teams from each country will be nominated by the British Embassy panel and the selected 9 students from each country will have a final 2 day hack in Paris in February 2020.

Formal participation on hackathon is also available to all organisations that would like to sponsor the hackathon programme. Please contact for further details.

Hackathon materials for X5GON

The provisional learning materials that are presented to the participants are available on this page



AI for the Common Good: F’AI’R Education Hackathon

Credentify is a decentralized micro-credentials clearinghouse powered by a blockchain network across European universities allowing safe transfer of millions of micro-credentials as smaller units summing up into ECTS credits.

Credentify clearing house for digital credentials
Credentify clearing house for digital credentials

This empowers European students, educational workers and universities across Europe to make the accreditation of their traditional learning experience fast, dynamic, safe, reliable, transparent and accountable.

Credentify ensures that micro-credentials are certified and mapped to European qualifications frameworks and can scale into other forms of Higher Education. Credentify therefore empowers students and universities with equitable knowledge accreditation by allowing it to be more fair and flexible in its delivery.

Credentify is built on native European technologies, extensive policy and research analysis and is integrated with ESCO to maximize impact in the European Education Area and Digital Single Market.

The Humane AI Flagship will develop the scientific foundations and technological breakthroughs needed to shape the ongoing artificial intelligence (AI) revolution. The goal is to design and deploy AI systems that enhance human capabilities and empower both individuals and society as a whole to develop AI that extends rather than replaces human intelligence.

This vision fits very well into the ambitions articulated by the EC in its Communication on AI but cannot be achieved by legislation or political directives alone. Instead it needs fundamentally new solutions to core research problems in AI and human-computer interaction (HCI), especially to help people understand actions recommended or performed by AI systems.

Challenges include: learning complex world models; building effective and fully explainable machine learning systems;

  • Adapting AI systems to dynamic open-ended real-world environments (in particular robots and autonomous systems)
  • Achieving in-depth understanding of humans and complex social contexts
  • Enabling self-reflection within AI systems.

The focus is on human-centered AI, with a strong emphasis on ethics, values by design, and appropriate consideration of related legal and social issues.

The HumanE AI project will mobilize a research landscape far beyond the direct project funding and create a unique innovation ecosystem that offers substantial return on investment. It will result in significant disruption across its socio-economic impact areas, including Industry 4.0, health & well-being, mobility, education, policy and finance. It will spearhead the efforts required to help Europe achieve a step-change in AI uptake across the economy.

The preparatory action consortium, with 35 partners from 17 countries, including four large industrial members, will define the details of all aspects necessary to implement a full Flagship project, and mobilize major scientific, industrial, political and public support for the vision.

Keywords: Artificial Intelligence, Human-Computer Interaction, digitisation, ICT and society, robotics, human centered design, human centered machine learning, Responsible AI.

Scientific partners include 35 institutions, 68 persons

1 – DFKI (DE)
2 – LMU (DE)
3 – ETH Z (CH)
4 – U Warsaw (PL)
5 – CNR (IT)
6 – CEU (HU)
7 – U Sussex (UK)
8 – U Copenhangen(DK)
9 – U Pisa (IT)
10 – Aalto U (FI)
11 – FBK (IT)
12 – VU Amsterdam (NL)
13 – UCL (UK)
14 – TU Berlin (DE)
15 – CSIC (ES)
16 – INRIA (FR)
19 – JSI (SI)
20 – TU Delft (NL)
21 – TU Kaiserslautern (DE)
22 – U Leiden (NL)
23 – U Pompeu Fabra (ES)
24 – TU Wien (A)
25 – U Umea (S)
26 – U Freiburg (DE)
27 – Phillips Electronics (NL)
28 – Knowledge for All Foundation (UK)
29 – Thales Group (FR)
30 – ING Group (NL)
31 – Athena Research and Innovation Center (GR)
32 – Volkswagen AG (DE)
33 – German Enterpreneurhip GmbH (DE)
34 – UCC Cork (IE)
35 – Sorbonne Universite (FR)

Translexy is an API that enables you to translate your content. It is already integrated into MOOC platforms and video repositories to create new educational experiences.

It also offers tools for developers and researchers. Translexy API is the first free and open translation service to use Neural Machine Translation Models (NMT) for educational content, which immensely improves translation results. NMT has recently emerged as a disruptive technology and has become the dominant paradigm in machine translation.

Discover here how you can use, integrate and customize this service in your own application or MOOC solution. Translexy API can be used on its own or its results can be customized for pre-publishing with our partners.

Translexy API provides translation from English into nine European and two BRIC, languages, namely German (DE), Italian (IT), Portuguese (PT), Dutch (DU), Bulgarian (BG), Greek (EL), Polish (PL), Czech (CS), Croatian (CR), Russian (RU) and Chinese (ZH).

Academic Challenge

Degrees are broken into modules; modules into courses; courses into short segments. MOOCs ahve shown that six minutes are the exact right lenght for online video and four weeks for a course. Universities are responding to this trend by becoming more modular.

Industry Needs

The private sector is proposing solutions to recognise learning in smaller segments, from nanodegrees, to centralised skill-banks verified by standardised testing to online systems of recommendation similar to those to peer-reviewed literature.

The MicroHE solution

The specific objective of the project is to examine the scope for and impact of micro-credentials – a form of short-cycle tertiary qualification – in European Higher Education. 

MicroHE is explicitly designed to match several of the priorities of the EU strategy on higher Education:

  • It will encourage a greater variety of study modes as well as exploit the potential of ICTs to enable more effective and personalized learning experiences by supporting the expansion of micro-credentials, i.e. of
    higher education provided digitally in units of fewer than ECTS
  • It will help to ensure the efficient recognition of credits gained abroad through effective quality assurance, comparable and consistent use of ECTS and the Diploma Supplement, and by linking qualifications to the European Qualifications Framework, by piloting tools (such as a module supplement and a recognition clearinghouse) to allow these instruments to be used to assess, certify and recognize micro-credentials.

By ensuring that micro-credentials are certified and mapped to qualifications frameworks, the project will also ensure that clear progression pathways are developed from this form of education into other forms of Higher Education.

The MicroHE project has received funding from the European Union’s Erasmus+ programme.

The project aims to provide the most comprehensive policy analysis of the impact of modularisation, unbundling and micro-credentialing on Higher Education in Europe yet conducted, and will address the challenges described above by:

  • Gathering the state of the art in micro-credentialing in European Higher Education today, by organizing the first European survey on micro-credentials in HE, surveying at least 70 institutions across the continent, with
    the aim of understanding the current level of provision, the types of micro-credentials offered and future trends in provision of micro-credentials
  • Forecasting the impacts of continued modularisation of Higher Education on HEIs by using forwardscanning techniques, specifically through the use of DELPHI methodology
  • Examining the adequacy of European recognition instruments for micro-credentials in particular ECTS, the diploma supplement and qualification frameworks
  • Proposing a ‘credit supplement’ to give detailed information about micro-credentials in a way compatible with ECTS, the diploma supplement and qualification frameworks
  • Proposing a meta-data standard and developing an online clearinghouse to facilitate recognition, transfer and portability of micro-credentials in Europe

Through these activities, the project intends to:

  • Promote increased choice for students and lifelong learners by increasing the range of educational opportunities offered to them;
  • Equip universities (esp. public universities) to adequately adapt to the changes brought about modularisation of education;
  • Improve the recognition and transfer of learning between different educational organizations as well as the world of work, including transnationally;
  • While maintaining the European tradition of high quality education and high-levels of student-protection, provided through systems of accreditation and quality assurance.
  1. WP 1 – Analysing and Modelling Micro-Credentials in Europe
  2. WP 2 – Scenario Building for Micro-Credentials in Europe
  3. WP 3 – Creating a Recognition-Framework for Micro-Credentials
  4. WP 4 – Facilitating Portability of Micro-Credentials
  5. WP 5 – Quality Assurance
  6. WP 6 – Marketing and Message Management
  7. WP 7 – Management


  1. Cooperative State University Baden-Wuerttemberg, Germany
  2. European Distance and E-Learning Network, United Kingdom
  3. Vytauto Didžiojo universitetas, Lithuania
  4. Institut “Jožef Stefan”, Slovenia
  5. TTY-säätiö sr (TTY Foundation sr), Finland
  6. Knowledge Innovation Centre (Malta) Ltd., Malta
  7. Fondazione Politecnico di Milano, Italy
  8. Knowledge 4 All Foundation Ltd., United Kingdom


We are leading a breakthrough EU H2020 project, creating a solution that will help users/students find what they need not just in OER repositories, but across all open educational resources on the web.

This solution will adapt to the user’s needs and learn how to make ongoing customized recommendations and suggestions through a truly interactive and impactful learning experience.

This new AI-driven platform will deliver OER content from everywhere, for the students’ need at the right time and place. This learning and development solution will use the following solutions to accomplish this goal:

  • Aggregation: It will gather relevant content in one place, from the projects case studies as well as external providers and other preferred resources.
  • Curation: AI and machine learning will be key to curate relevant and contextual content and external students at the right time and point of need.
  • Personalization: It will make increasingly personalized recommendations for learning content to suit students’ needs, based on the analysis of relevant factors.
  • Creation: Large, small and medium-sized universities have tacit knowledge that can be unlocked and re-used. This approach will allow any organization to release and build their own content libraries quickly and conveniently to share with the world and vice versa.
The X5GON project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 761758.

August 1, 2017 – Data Scientist career opportunity – Recommendations  Job Description: University of Nantes wishes to recruit an engineer for project X5-GON. The goal is to specify, develop and deploy a recommender system solution.

August 2, 2017 – X5GON to be presented @ European Commission The X5GON project on converging OER was invited to present at the workshop “H2020 Media Projects’ Workshop: Collaboration Towards the Future of Media” on 17th October 2017, at EC premises in Avenue de Beaulieu 25 in Brussels.

X5gon stands for easily implemented freely available innovative technology elements that will converge currently scattered Open Educational Resources (OER) available in various modalities across Europe and globally.

X5gon will combine content understanding, user modelling and quality assurance methods and tools to boost creating a homogenous network of (OER) sites and provides users (teachers, learners) with a common learning experience. X5gon will deploy open technologies for recommendation, learning analytics and learning personalisation services that will work across various OER sites, independent of languages, modalities, scientific domains, and cultural contexts.

It will develop services for convergence of OER media which includes full courses, course materials, modules, textbooks, streaming videos, tests, software, related events and any other tools, materials, or techniques used to support access to knowledge.

The solutions that will be offered to OER sites are fivefold:

  • Cross-modal: technologies for multimodal content understanding
  • Cross-site: technologies to transparently accompany and analyse users across sites
  • Cross-domain: technologies for cross domain content analytics
  • Cross-language: technologies for cross lingual content recommendation
  • Cross-cultural: technologies for cross cultural learning personalisation

The project will collect and index OER resources, track data of users and their progress and use that to drive an analytics engine driven by state-of-the-art machine learning that can improve recommendations through better understanding of users, their progress and goals, and hence their match with knowledge resources of all types. In addition X5gon will implement innovative models and methods for OER quality assessment and assurance, including trust networks between teachers for OER creation and exchange, automatic content validation and user experience.

The project will run a series of pilot case studies that enable the measurement of the broader goals of delivering a useful and enjoyable educational experience to learners in different domains, at different levels and from different cultures. The two exploitation scenarios are planned: (i) free use of services for OER, (ii) commercial exploitation of the multimodal, big data, real-time analytics pipeline.


Objective 1: X5gon, will combine content understanding, user modelling and quality assurance methods and tools to boost creating a homogenous network of OER sites and provides users (teachers, learners) with a common learning experience independent of the OER site.

Objective 2: X5gon will create a self-sustainable and growing mode of operation based on bottom-up OER sites collaboration, easy to install open software and Ministry of Education with governmental channels and UNESCO National Commissions for wider adoption and showcase for policy makers and industries with a clear potential for adoption in the Digital Single Market.

Objective 3: X5gon will tackle several online learning research and innovation challenges related in particular to big data, real-time response and multimodality.

Objective 4: The market opportunity for a Europe-centric education platform is considerable. Two important outcomes from this project that can be viewed as separate entities for the purposes of exploitation are (i) free use for OER of cross-recommendation, cross-site learning analytics and learning personalisation functionalities (ii) the enabling technology stack. Given the different risks/reward profiles, these outcomes require different strategies for exploitation.

  1. University College London
  2. Institut Jozef Stefan
  3. Knowledge 4 All Foundation
  4. Universitat Politecnica de Valencia
  5. Université de Nantes
  6. Universitaet Osnabrueck
  7. Slovenian Post
  8. Ministry of Education of Slovenia
  • WP1: Learning rich content representations (UCL) This work package will automatically learn representations of OERs so that they can later be used by the X5oerfeed and recommendation engine. In context of education, providing the learner with a high quality resource (quality assurance) that is reliable, authoritative and is topically relevant to the learner’s interests is highly important. Through this WP, we will devise methods that can automatically infer the quality and authority of each OER, as well as the topics covered by the OERs so that they can be used in recommendation. We will further devise evaluation methodologies for measuring the quality of content representation models and quality assurance models.
  • WP2: Analytics Infrastructure, Services and API (JSI) is concerned with the infrastructure for representing the relevant information developing an API that all the WPs make use of. Additionally it creates (1) the software platform which will be the main link connecting the different components of the system, (2) services and products and (3) respective APIs to connect. The three services to be offered are X5oerfeed, X5analytics and X5recommend.
  • WP3 Learning Analytics Engine (NA) is the analytics engine for the analysis of learning and testing aspects, links with educational theories, affective computing, etc. including cross-modal cross-lingual and cross-cultural aspects. The information generated from the tools developed in WP1 and WP2 need to be cross referenced in order to obtain high level information. It will propose personalised learning as an individualized navigation through the OERs by the different sites. A more complex issue will consist in answering the following: what resource should the system propose to a specific learner, at a specific moment? This requires to be able to predict the intent of the user. Finally, prediction and recommendation will depend on collected personal information. The ethical issues related with this collection and with what should be allowed, will be discussed, and compared with international standards. A specific effort will be made here.
  • WP4 Recommendation Engine (JSI) is concerned with designing rich models of users on learning sites, and use these models for recommendation and personalization of learning material. Initially we will leverage other users but in the latter stages relying increasingly on the analytics engine developed in WP3.
  • WP5 Piloting (UPV) is concerned with piloting successive versions of project components and providing feedback to other WPs.
  • WP6 Studies in the wild (UCL) is concerned with the studies, their design and goals, their execution and the linking of the results with the knowledge produced by the analytics engine.
  • WP7 Dissemination (K4A) is concerned with the dissemination of the results. It will be innovative in order to acquire new and unmapped users. It will be tailored to the needs of the identified target audiences, including groups beyond the project’s own community. As X5gon has a clear interdisciplinary and intersectoral dimension: it transcends several domains: education and communication and information and science, aspects of public/societal engagement on issues related to the project will also be addressed.
  • WP8 Exploitation (PO) is focussed on exploitation, a key component of the project. This will focus on two scenarios and will feed technology and data-drive results into the policy making partner MIZS.
  • WP9 Management (UCL) arranges for the management of the project.

X5gon is an analytic platform with open services, APIs and scripts supported with AI enabled technical pipeline to converge dispersed open educational resources (OER) media content to learners and users into a one-stop-shop data-driven learning environment.

Keywords: Open Educational Resources, machine learning, cross-site, cross-domain, cross-modal, cross-language, cross-cultural, cross-social, adaptive learning, policy making, web and information systems, database systems, communication networks, media, information society, accessibility, cultural studies, cultural diversity.


Advisory Board (AB) is formed with high level professionals in the field of artificial intelligence, educational technologies and open education. The main purpose of the Advisory Board is to provide management advice about the general direction the project should follow. The Advisory Board members are:

  • Rayid Ghani (m) is the Director of the Center for Data Science and Public Policy, Chief Data Scientist at the Urban Center on Computation and Data, Research Director at the Computation Institute (a joint institute of Argonne National Laboratory and The University of Chicago), and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. He is also the co-founder of Edgeflip, an analytics startup that grew out of the Obama 2012 Campaign, which was led by Rayid and focused on social media products for non-profits, advocacy groups, and charities.
  • Jan Newman (m) is working as head of legal affairs and organization at the North Rhine-Westphalian Library Service Centre (hbz). He is a member of the educational advisory board of the German UNESCO chapter and blogs about Open Educational Resources (OER) on Since 2014 he is the project manager of the OER World Map project, which is funded by the William and Flora Hewlett Foundation and aims at collecting data on OER actors and activities worldwide. Jan is author of several publications on OER, frequent speaker at OER conferences and participated in the organization of the German OER conferences OERde 14, OERde 15 and OERde Festival.
  • Dr Javiera Atenas (f) is the co-coordinator of the open education working group for Open Knowledge International, senior fellow of the higher education academy and associate lecturer at Aga Khan University and University de Barcelona.
  • Professor Pierre Dillenbourrg (m) has been professor assistant at TECFA, University of Geneva. He joined EPFL in November 2002. He has been the director of CRAFT, the pedagogical unit for 10 years and is now the academic director of the EPFL Center for Digital Education and head of the CHILI Lab: “Computer-Human Interaction for Learning & Instruction”.
  • Rose Luckin (f) is Professor of Learner Centred Design at the UCL Knowledge Lab, focusing on Artificial Intelligence and Educational Technology. Until 2011 she was a member of the board of BECTA (the British Educational Communications and Technology Agency), the body charged with implementing the UK government’s eLearning strategy. Her work is interdisciplinary and encompasses education, psychology, artificial intelligence and HCI. Rose investigates the relationship between people, their context, the concepts they are learning, and the resources at their disposal. Rose holds a 1st class BA in Computing and Artificial Intelligence and a PhD in Cognitive Science.
  • TJ Bliss (m) is the Director of Development and Strategy at Wiki Education, a non-profit that connects higher education to the publishing power of Wikipedia. Bridging Wikipedia and academia creates opportunities for any learner to contribute to, and access, open knowledge. Before joining Wiki Education, TJ was a Program Officer in the Education Program at the William and Flora Hewlett Foundation. In that role, he gave $45M in grants to over 30 organizations working to expand the reach and efficacy of Open Educational Resources (OER)

Massive Open Online Courses have been growing rapidly in size and impact. Yet the language barrier constitutes a major growth impediment in reaching out to all peoples and educating all citizens.

TraMOOC is a H2020 project to start in February 2015 and aims at tackling this impediment by developing high-quality translation of all types of text genre included in MOOCs (e.g. assignments, tests, presentations, lecture subtitles, blog text) from English into eleven European and BRIC languages (DE, IT, PT, EL, DU, CS, BG, CR, PL, RU, ZH) that constitute strong use cases, are hard to translate into and have weak MT support, thus complying with the call objectives. Phrase-based and syntax-based statistical machine translation models will be developed for addressing language diversity and supporting the language-independent nature of the  methodology.

For a high quality, automatic translation approach and for adding value to existing infrastructure, extensive advanced bootstrapping of new resources will be performed. An innovative multi-modal automatic and human evaluation schema will further ensure translation quality. For human evaluation, an innovative, strict-access control, time- and cost-efficient crowdsourcing setup will be used. Translation experts, domain experts and end users will also be involved

Case studies in the project into which the results will be showcased and tested are the openHPI MOOC platform and the VideoLectures.Net digital video lecture library.


statistical machine translation, MOOCs, topic detection, sentiment analysis, English, German, Italian, Portuguese, Bulgarian, Croatian, Polish, Greek, Dutch, Czech, Russian, Chinese, crowdsourcing

Visit the project website.

List of Beneficiaries:

1 Humboldt-Universitaet Zu Berlin – UBER
2 Dublin City University – DCU
3 The University of Edinburgh – UEDIN
4 Ionian University – IURC
5 Stichting Katholieke Universiteit
6 Easn Technology Innovation Services Bvba – EASN TIS
7 Deluxe Media Europe Ltd
8 Stichting Katholieke Universiteit Brabant Universiteit Van Tilburg
9 Knowledge 4 All Foundation