Learning Outcomes
Upon successful completion of the course the students will be able:
- to know and understand the key concepts of digital teaching and learning
- to analyse, assess, select and justify pedagogically appropriate e-learning methods and tools for digital teaching and learning innovations.
- to design and create pedagogically grounded online courses.
Course Contents
- Online Teaching and Learning: Theoretical Underpinnings
- Educational Design for Online Teaching and Learning
- An hierarchical Open Access to Online Education framework: Elements (Open Educational Resources, Learning Activities and Lesson Plans, Online Courses, Digital Learning Spaces). Tools and Key Roles (Online Education Instructional Designers, e-Tutors, e-Learning Systems Administrators, Managers)
- Open Educational Resources: Learning Objects, Educational Metadata, Repositories of Learning Objects. Case Studies: the National Repositories of Learning Objects
- Learning Activities and Lesson Plans: Authoring Tools for Learning Activities and Lesson Plans, Repositories of Learning Activities and Lesson Plans. Case Studies: the National Repositories of Learning Activities and Lesson Plans
- Design, Development and Delivery of Online Courses: Methodology for Designing Online Courses. Authoring Tools for Developing Online Courses. Course Management Systems. Case Study: Open edX, MOODLE
- Digital Learning Spaces: 3D Virtual Classrooms and Laboratories
Recommended Readings
- Textbook in Greek (provided for free)
- Additional Open Access Educational Resources available through the course management system
Learning Outcomes
Upon successful completion of this course, students should be able to
Know principles, paradigmatic architectures and methods for developing single agent and multi agent systems, have a critical and informed view of strengths and limitations regarding agents and multi agent systems, towards designing and delivering such systems.
Specifically, students know and acquire the abilities to develop
- Architectures of single and multi-agent systems
- Methods for agents coordination, collaboration and competition in specific settings and paradigmatic environments and problems
- Agents’ communication methods and protocols
Via the critical view of agents technology and experience in developing such systems.
Course Contents
- Agents: Principles, architectures and application examples
- Deliberation vs Reaction: Architectures
- Mental attitudes, states and their representation
- Multi-agent Systems: Interactions and dependencies
- Multiagent organizations and communication
- Cooperation and collaboration
- Agents communication
- The JASON framework and Agent Speak language
Recommended Readings
- Michael Wooldridge, Introduction to MultiAgent Systems, 2008.
- Yoav Shoham, Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009.
- Gerhard Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 2000.
- John Miller Scott Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity), Princeton University Press, 2007.
- David Easley, Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press 2010.
- JASON, https://github.com/jason-lang/jason
Learning Outcomes
This course introduces students to theoretical and applied research of collaborative learning (CSCL/W) depending in social cognition and social constructivism learning theories (situated learning – cognitive apprenticeship).
On completion of the course, the students will be able:
- to demonstrate knowledge in designing CSCL/W in educational and business settings.
- to choose and critically evaluate the perspectives of social & dialectical constructivism:
- to realize how CSCL/W can facilitate sharing and distributing of knowledge and expertise among community members.
- to synthesize projects in the context of socio-cognitive and social constructivism models in a CSCL/W.
- to create CSCL project in schooling, training/vocational environments.
- to realize the added value of collaboration in global society (multicultural awareness).
Course Contents
- CSCL/W in educational and working environments for peers in shared/collaborative settings.
- Socio-Cognitive approaches of learning.
- The social & dialectical constructivism: CSCL theories, principles, strategies, roles, artifacts/activities.
- The Vygotskian theory, situated learning, cognitive flexibility theory, cognitive apprenticeship, problem/project-based learning, self-regulated learning, self-directed learning, communities of practice.
- Shared and distributed knowledge and expertise with peers and community members (community of practices).
- Authentic assessment in collaborative learning on digital systems related to school/training/vocational environments.
Recommended Readings
Dillenbourg P., Fischer F., Kollar I., Mandl H. & Haake J.M. (2007): Scripting Computer-Supported Collaborative Learning, Springer.
Kobbe L. (2006): Framework on multiple goal dimensions for computer-supported scripts, Kaleidoscope.
Recommended Readings
Barkley, E & Major, C. H. & Cross, K.P. (2016) Collaborative Learning Techniques: A Handbook for College Faculty 2nd Edition, Jossey-Bass.
Goggins, S.P., Jahnke, I. & Wulf, V. (2013). Computer-Supported Collaborative Learning at the Workplace: CSCL@Work, Elesevier.
Sharratt, L.D. & Planche B. M. (2016). Leading Collaborative Learning: Empowering Excellence, Corwin.
Learning Outcomes
This course introduces students in consulting procedures for the personal and professional development in an IT context. It addresses the needs of students as future workers on ‘how to be involved in a IT workforce community’ by enhancing them to provide emerging professional development opportunities and practices.
On completion of the course, the students will be able to:
-
- understand the theoretical background of the consulting (in physical and IT context).
- select and design the appropriate components for their academic and career path (KPIs).
- critically evaluate a set of skills for professional development.
- design and build products/services via appropriate components to an institutional IT context and demands (needs, motivations, attitudes, ethics).
- compose a personal/professional career plan for further development in the society (KPIs).
Course Contents
- Basic consulting theories and practices necessary for the development of effective performance on an academic and professional environment in IT business community (Kirkpatrick model, SRL, SDL).
- Continuing Professional Development programs (CPD).
- Skills and Competencies.
- Communication and Collaboration (active listening, verbal, non-verbal, communication).
- Μentoring and coaching.
- Personal and affective factors in performing (needs, attitudes, motivation, self-esteem etc.
- Organizational factors (ethics, leadership).
- Problem solving, innovation, creativity.
- Evaluation (KPIs).
Recommended Readings
- Robinson D. & Robinson J. (2008): Performance Consulting: A practical Guide for HR and Learning Professionals, Berrett-Koehler Publishers.
- Rosenberg M. (2001): E-Learning Strategies for Delivering Knowledge in the Digital Age, McGraw-Hill.
Learning Outcomes
This course focuses on wide area wireless networks and addresses advanced topics in physical layer design, multi-carrier systems and wireless standards evolution.
At the end of this course, students will have acquired advanced/in depth knowledge in the field of Wireless Communications, with particular emphasis on wireless channel modelling, Multiple Input Multiple Output systems design, and performance evaluation in terms of capacity.
The students will be capable of performing numerical calculations of various wireless parameters, stochastic modelling of wireless transceivers and performance assessment by means of analytical evaluations and simulations, with main focus on baseband processing and radio resources management.
Course Contents
- Advanced physical layer design topics: modulation and coding
- Multiplexing in time, space, frequency, code
- Multiple Input Multiple Output Systems
- Multi-carrier systems: OFDM/OFDMA.
- Radio resource allocation: multi-user communications and scheduling, cross-layer optimization.
- Wireless standards: 3G evolution, IEEE 802.x, 4G and 5G
Recommended Readings
- Behrouz A. Forouzan, “Data Communications and Networking”, Fourth edition, McGraw-Hill, 2007
- W Stallings, Wireless Communciations and Networks, Pearson, 2004.
- D. Tse, P. Viswanath, Fundamentals of Wireless Communciations, 2005.
- T. S. Rappaport, Wireless communications – Principles and practices, Pearson, 2002.
- Harri Holma, Antti Toskala, WCDMA for UMTS: HSPA Evolution and LTE, Wiley, 2010.
- Andrea Goldsmith, Wireless Communications, Cambridge University Press, 2005.
Learning Outcomes
he course presents simulation techniques with particular emphasis on discrete event simulation and applications in computer computational systems and communication networks.
Upon successful completion of the course, the students will be in position to:
- design system models with the required details level that serves better the problem at hand
- develop simulation programs in general purpose programming language (e.g C++) to simulate and evaluate the behavior of simpler systems
- use more sophisticated simulation software for the study and performance evaluation of more complex communication networks and computational systems (e.g network simulator ns3 and CloudSim for cloud computing systems)
- design experiments, collect measurements and interpret and evaluate simulation results
Course Contents
- Introduction to dynamic discrete event systems.
- Development of discrete system models, event-advance design, time-advance design, activity-based design.
- Pseudorandom number generation, random variables generation.
- Overview of simulation languages and platforms.
- Development of simulation programs using general purpose programming languages.
- Measurement techniques, traffic load and experiment design.
- Statistical analysis of simulation experiments, transient and steady state, data collection, confidence intervals, variation reduction techniques.
- Simulation exercises and examples of data networks and cloud computing systems. Theoretical results verification.
Recommended Readings
- Roumeliotis and Souravlas, “Simulation Techniques”, Epikentro Publications.
- Kouikoglou and Konstantas, “Simulation of Discrete Event Systems”, Disigma Publications, 2016.
- Harry Perros, “Computer Simulation Techniques – The Definitive Introduction”, free download from https://people.engr.ncsu.edu/hp/files/simulation.pdf
- Averill M. Law and W. David Kelton, “Simulation Modeling and Analysis”, McGraw-Hill, Inc.
- NS manual and tutorials, https://www.nsnam.org/documentation/
Learning Outcomes
Upon successful completion of this course, students should be able to know and develop basic decision making abilities of intelligent agents who are capable of acting in the real world.
Specifically, students acquire knowledge and the abilities to develop and apply
- planning algorithms
- methods for re-planning and computing actions’ schedules for acing in the real world
- knowledge representation and reasoning with ontologies and real-world data
- basic principles and algorithms for (simple or advanced) decision making
- algorithms for learning policies towards acting in the real world
Through a critical view of methods and by acquiring experience in building systems in paradigmatic cases.
Course Contents
- Basic and advanced planning algorithms
- Replanning and scheduling actions with duration.
- Reasoning and representation with ontologies and data
- Decision making principles and methods
- Reinforcement learning, introduction.
Recommended Readings
- Stuart Russel and Peter Norvig. Artificial Intelligence: A Μodern Approach, Prentice Hall, 2nd edition (2003). http://aima.cs.berkeley.edu/.
- Yoav Shoham, Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009
Associated scientific Journals
- Artificial Intelligence, Elsevier, ISSN: 0004-3702
- Journal of Web Semantics, Elsevier, ISSN: 1570-8268
Learning Outcomes
The students after the successful completion of the course will be able:
- to model and analyze data with appropriate analysis techniques, assess the quality of input
- to choose the appropriate exploratory and/or inferential method for analyzing data, and interpret the results contextually.
- to use supervised and unsupervised learning techniques for solving many analysis problems such as prediction, classification, segmentation.
- to apply methods for the evaluation of the data analysis results.
Course Contents
- Collection, preparation and representation of data for analysis
- Linear, logistic regression
- Classification Techniques (probabilistic classification, decision trees, support vector machines)
- Predictive analytics and neural networks
- Recommender systems
- Graph analysis (applications on social networks)
- Text mining – sentiment analysis
- Evaluation of data analysis results
Recommended Readings
- Mohammed J. Zaki, Wagner Meira Jr. (2018): Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge University Press.
- Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman (2014): Mining massive datasets, Cambridge University Press.
- Top of Form.
- Bottom of Form.
Students can choose it only once during undergraduate studies (either the 7th or the 8th semester).
Learning Outcomes
The objective of this course is to present fundamentals concepts regarding Healthcare Information Systems (HIS). HIS are described at both conceptual and technical level and types of HIS are studied thoroughly. In addition, best practices regarding HIS architectural design, development methodologies and interoperability are analysed. Challenges and perspectives of HIS are presented with reference to modern digital technologies of data analytics and artificial intelligence. The course will incorporate a significant laboratory component with various digital tools (mainly open source) that allow student to implement HIS.
Upon successful completion of the course the students will be able to:
- Understand the connection between healthcare systems and healthcare information systems
- Define the users of information and decision support based on existing data
- Describe the general functions, objectives and advantages of HIS
- Describe contemporary architectural trends and HIS, in the form of services provided, for supporting important healthcare processes
- Compare various HIS characteristics and choose the most appropriate systems for specific needs and operational frameworks
- Develop HIS, by using open source tools, inventing innovative practices in the fields of medical data architecture and management for their multiple exploitation.
Course Content
- Healthcare Information Systems: General characteristics. HIS evolution.
- HIS analysis, design and implementation.
- Patient-oriented HIS development.
- Process-oriented healthcare organizations. Healthcare process and data management.
- Specialized HIS. Contribution to provided healthcare services.
- HIS architectures, integration and interoperability.
- HIS security. Standards and security policies.
- Presentation of well-known commercial HIS of the global market regarding electronic health records.
- HIS challenges and perspectives. HIS in Greece.
- HIS development (analysis, design, implementation, testing, operation, maintenance).
Suggested Bibliography
- Karen A. Wager, Frances W. Lee and John P. Glaser (2009): Health Care Information Systems: A Practical Approach for Health Care Management, Jossey-Bass.
- Joseph Tan (2010): Developments in Healthcare Information Systems and Healthcare Informatics: Improving Efficiency and Productivity, IGI Global.
- Charlotte A. Weaver, Marion J. Ball, George R. Kim, Joan M. Kiel, (2015): Healthcare Information Management Systems: Cases, Strategies, and Solutions, Springer.
- Sean P. Murphy, (2015), Healthcare Information Security and Privacy, McGraw-Hill Education.
- Pamela K Oachs, Amy Watters, (2016), Health Information Management: Concepts, Principles, and Practice, American Health Information Management Association.
- International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global
- International Journal of Healthcare Technology and Management, Inderscience
- International Journal of Medical Informatics, Elsevier.
