Advanced Technologies for Open Education

This course is part of the programme
Master in Leadership in Open Education (Second Level)

Objectives and competences

The aim of this course is to give students an overview and specific knowledge about advanced and emerging technologies that are either already in operation in the context of OE or could be very effective in OE in the future.

After completing this course, students will be able to:

  • Select and introduce appropriate advanced ICT tools and open education methods, including artificial intelligence, blockchain technologies in education, micro-learning and serious games;

  • Understand the basics and the use of advanced information technologies in the context of OE, based on deep knowledge about the specifics of a subset of these technologies, technical background, use scenarios and how to evaluate the pros and cons when applying them to a OE process;

  • Select proper advanced technologies to support particular processes in OE, and to fit them to OE strategies and implementation plans;

  • Propose and run RTD projects in the area of advanced technologies for OE.


Prerequisits include knowledge obtained in Year 1 courses Introduction to OE and Technologies for OE, as students should have a deep understanding of OE environments, processes, actors, OE strategies, technological needs and preferences for OE, concepts and mechanisms of openness. They should be familiar with the existing technologies used in ICT supported education, basic ICT infrastructures and architectures. They should understand the mechanisms behind ICT supported collaboration, sharing and interoperability. Students should be able to use communication and collaboration tools, and should be prepared to work in interdisciplinary teams.


During the course students will learn about var-ious advanced and emerging technologies that are already being used in OE or might be very relevant to make OE more effective, personal-ised and appealing. Special attention will be put on technologies that are transforming tradi-tional education towards user-centred and per-sonalised learning, AI moderated learning, vir-tual and collaborative learning, and unobtrusive micro-learning.

Students will learn about technologies from the broad areas of artificial intelligence, augment-ed reality and immersive technologies, distrib-uted technologies such as block-chain, cyber-physical systems (Internet of Things), virtual labs and simulations, and open source infra-structures for learning, collaboration and shar-ing. During the lectures they will learn about the basics of the advanced technologies, exist-ing solutions, services and platforms, strengths and weaknesses, their existing and potential use in OE and their costs and user experiences in OE.

The following topics will be covered in more detail:

1) Data mining, text mining, and web min-ing for education
2) Content understanding: multimodality, multlinguality, syntactic and semantic technologies, knowledge discovery and formalisation
3) User interface design for an enhanced learning experience in open education.
4) The inclusion of modelling human expe-rience in the context of OE and its rela-tion to modelling behaviour change.
5) User modeling (learning analytics): modelling of needs and preferences, background knowledge, motivation and aspirations, learning style, social en-gagement
6) Process (personalization): automatic course development and distribution
7) Competency modelling, social network analysis, cause effect graphs, compe-tency trends detection, learning paths and networks
8) Distributed architectures (including block-chain technologies)
9) Virtual and immersive reality technolo-gies
10) On-line open virtual labs
11) Cyber-physical systems
12) Language Technologies

Then students will combine seminars, online lectures and self-learning to gain a more de-tailed and specific knowledge about a subset of these technologies with an emphasis on scien-tific methods for evaluating the contribution that the technologies are making to the overall aims of the OE environment.

Intended learning outcomes

After completing this course, students will:

• Have a good overview and detailed knowledge about the specific advanced information technologies and how they can support OE deployment;

• Master the selection process of proper technologies and relate them to a specific challenge to be solved in OE;

• Become proposers of new technology development and deployment in OE;

• Be able to prepare and manage RTD projects in implementing ICT in OE;

• Be able to evaluate, validate and critically assess the emerging technologies prospects for OE;

• Realistically assess their personal abilities in terms of integrating advanced and emerging technologies in OE, hence being able to identify appropriately skilled teams for specific integration projects.


• Max Bramer, Principles of Data mining, Springer, London 2007.
• Helen Sharp, Jenny Preece, and Yvonne Rogers, Interaction Design: Beyond Human-Computer Interaction, Wiley, 3rd Edition.
• John Vince, An Introduction to Virtual Reality, Wiley 2004.
• Dimitrios Serpanos and Marilyn Wolf, Internet-of-Things (IoT) Systems Architectures, Al-gorithms, Methodologies, Springer.


• Interim presentations • Final exam, evaluating theoretical knowledge in addition to practical abilities in designing the deployment of appropriate technologies to a particular OE challenge.

Lecturer's references

Dr. John Stewart Shawe-Taylor is Director of the Centre for Computational Statistics and Machine Learning at University College, London (UK). His main research area is statistical learning theory. He has contributed to a number of fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, including the mapping of these approaches onto novel domains including work in computer vision, document classification and brain scan analysis. More recently he has worked on interactive learning and reinforcement learning. He has also been instrumental in assembling a series of influential European Networks of Excellence (initially the NeuroCOLT projects and later the PASCAL networks). The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. He has published over 300 papers with over 42000 citations. Prof. Shawe Taylor became UNESCO Chair on Artificial Intelligence at UCL in 2018. He has been working with the Knowledge Societies Division, CI sector of UNESCO to prepare with the Knowledge 4 All Foundation a mapping of AI hotspots in emerging economies. He is also playing an instrumental role in designing a global AI powered infrastructure to deliver equitable access to education via open educational resources in the project X5GON.

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