Educational Technology

With the completion of the course, the student will be able:

  • to know and understand the key concepts of educational design for technology-supported and technology-enhanced educational innovations (including flipped classroom for blended learning and Massive Open Online Courses).
  • to analyse and critique the basic elements of the ADDIE educational design process when applied to design, develop and evaluate technology-supported and technology-enhanced educational innovations (including flipped classroom for blended learning and Massive Open Online Courses).
  • to design and implement pedagogically grounded technology-supported and technology-enhanced educational innovations focusing to flipped classroom for blended learning and Massive Open Online Courses.

Course Contents

  • Educational Design for Technology-supported and Technology-enhanced Educational Innovations
    • What is Educational design? Definitions – Basic Principles – Models
    • The ADDIE Model: Analysis of each Phase
    • Analyse Learners and Learning Context
    • Educational Objectives and Assessment of Learning and/or Performance
    • Strategies for Teaching and Learning
  • Digital Media in Education and Training
    • Educational Videos
    • Interactive Digital Textbooks
    • Educational Games and Gamification
    • Educational Mobile Apps
    • Educational Web 2.0 Application
    • Educational Augmented Reality and 3D Virtual Worlds in Education & Training
  • Case Studies
    • Blended Learning: the Flipped Classroom model
    • Massive Open Online Courses (MOOCs)

Recommended Readings

  • Textbook in Greek (provided for free)
  • Additional Open Access Educational Resources available through the course management system

Assessment in Digital Learning

Goal and Learning Outcomes

The course seeks to both conceptualize and redefine the purpose and objectives of the 21st century assessment for learning, as well as to outline modern and easy-to-use techniques and tools used by teachers in modern formal and informal learning environments.

After the successful completion of the course, the students will be able to:

  • State the modern forms of educational evaluation, utilizing educational technologies
  • Understand methodological issues regarding the organization and conduct of quality assessment of the educational process.
  • Utilize modern tools and techniques to evaluate trainees’ performance.
  • Apply a variety of techniques for systematic evaluation of instructional process.



  1. Basic principles of evaluating the quality of educational interventions
  • Formative
  • Holistic-Summative
  1. Principles of Monitoring and Assessing Students’ Performance
  2. Known techniques for evaluating the performance of trainees
  3. Categories of assessment tools
  • Quiz-tests
  • Rubrics
  • Mindmaps
  • Learning Analytics
  • Computational Thinking Assessment tools
  1. Quality Evaluation Techniques for Learning Resources and Learning Systems
  2. Process of creation and application of evaluation techniques and tools.

Suggested Reading

  1. A. Tomlinson & T. R. Moon (2013). Assessment and Student Success in a Differentiated Classroom, Association for Supervision & Curriculum Development, ISBN 1416616179 (ISBN13: 9781416616177)
  2. Cowie, B., Moreland, J. and Otrel-Cass, K. (2013). Expanding Notions of Assessment for Learning. Dordrecht: Springer.


Learning Outcomes

This course provides an introduction to the principles of analysis and design of programming languages as well as to the ways these principles are applied in modern programming languages. The successful completion of this course will allow students:

  • to understand the basic and important features of the design, implementation and analysis of compiler systems for modern programming languages.
  • to know the basic features of the tools and the development techniques for the creation of modern programming languages.

Course Contents

  • Introduction – Overview of Modern Programming Languages.
  • Language Definition and Design (Regular Expressions – Automata – Context-Free Grammars).
  • Programming Language Structure (Variables, Types and Scoping, Control Flow and Evaluation of Expressions, Subroutines, Iterative and Recursive Processes, Memory Management and Communication).
  • The Compiling/Interpretation Process (Lectical Analysis, Syntactic Analysis, Code Production & Optimization, Linking).

Recommended Readings

  • Scott, M. L., Programming Language Pragmatics, 2nd edition, Morgan Kaufmann, 2009
  • Instructor Notes

Digital Signal Processing

Learning Outcomes

The course introduces students to the design theory for continuous and discrete time linear systems. Based on this theory, students will be able to design analog and digital filters based on specifications in the frequency domain.

Upon successful completion of the course the student will be able to:

  • Use algorithms to design Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filters.
  • Obtain transfer function of prototype analog filters based on required frequency response.
  • Obtain IIR and FIR filter implementation methods: serial and parallel structures.
  • Design FIR and IIR filters using the Matlab software tool.

Course Contents

  • Frequency response of discrete time signals
  • Finite impulse response (FIR) digital filters with linear phase.
  • FIR filter design using the window method.
  • FIR filter design using the frequency sampling method.
  • FIR filter design using the optimal method.
  • Prototypes of analogue lowpass filters: Butterworth polynomials and Chebyshev polynomials.
  • Frequency translation of ptototype analogue filters for creating analogue filters with arbitrary frequency response.
  • Design of digital infinite impulse response (IIR) filters using bilinear transformation.
  • Frequency transformation of digital filters.
  • Implementation issues and techniques for IIR and FIR digital filters
  • Telecommunication filters raised cosine.

Recommended Readings

  • Vinay Ingle & John Proakis, (2012) Digital Signal Processing using Matlab, 3rd edition, Cengage Learning
  • C. Ifeachor & B.W. Jervis, (2002): DSP A Practical Approach, 2nd edition, Prentice Hall, ISBN 0201-59619-9
  • J. Proakis & D. Manolakis, (2007): Digital Signal Processing: Principles, Algorithms and Applications, 4th Edition, Prentice Hall.

Digital Image Processing

Learning Outcomes

Digital image processing is used for two distinct purposes: (1) improving the appearance of the image so that it is easier for an observer to interpret and (2) digitally analyzing the image for the purpose of describing, identifying and interpreting the content of an image. image. The course will present the basic algorithms and methodologies for both purposes in the field of space and in the field of frequencies.

Students, upon successful completion of the course, will be able to:

A) Understand basic methodologies and basic knowledge of designing and developing image processing systems

B) Know the stages of digital image processing and analysis (optical sensors, image capture, digitization, transformation, coding, compression, transmission, segmentation, recognition)

C) Analyze problems across different application areas and select the right mechanisms for managing and processing digital images

D) Evaluate digital image processing systems and algorithms

Course Contents

  • Introduction to Digital Image Processing
  • 2-D Signals and Systems – Background Information
  • Sampling and Digitization Issues
  • Image Enhancement and Restoration
  • Binary Image Processing – Morphological Operators
  • Image Segmentation – Edge Detection
  • Image Transformations (Fourier, DCT, Hadamard, etc.)
  • Analysis in the frequency domain
  • Digital Image Compression
  • Digital Image Analysis – Computer Vision
  • Texture Analysis – Region of Interests
  • Other areas: eg Watermarking, Information Retrieval, etc.

Recommended Readings

  • Rafael C. Gonzalez & Richard E. Woods Digital Image Processing CRC Press 4th Edition

Privacy Enhancing Technologies

Learning Outcomes

The purpose of the course is to highlight the concept of privacy, especially in relation to personal and / or sensitive data exchanged through open public networks, such as the Internet, in the context of various electronic services. Existing privacy enhancing technologies are introduced and special reference is made to the privacy problems faced by specific categories of applications. The proposed treatment mechanisms are also presented.

In this context, the learning outcomes of the course, after its successful completion, are that the students will be able:

  • to understand the basic concepts of privacy and personal data protection as well as how to recognize and analyze privacy requirements.
  • to know the basic privacy requirements that need to be taken into account when designing, and to be satisfied in the implementation, of an information system.
  • to analyse, evaluate and justify alternative technologies / mechanisms to protect privacy and meet the requirements.
  • to design systems that protect the privacy of its users

Course Contents

  • Definition of Privacy.
  • Legal Framework for the Protection of Personal Data.
  • Attacks on Privacy and Subjectivity of Impact in case of Privacy violation incidents.
  • Requirements for anonymity, unlinkability, undetectability and unobservability.
  • Pseudo-anonymity.
  • Identity Management.
  • Privacy Enhancing Technologies (Anonymizer, LPWA, Onion Routing, Crowds, MixNets, etc.).
  • Privacy protection in Ubiquitous Computing (RFIDs, Positioning Services), Internet Telephony, Health Information Systems, etc.
  • The Greek Framework for Digital Authentication and the Unique Citizen Identification Number for Electronic Services Offered by Government Bodies.
  • Privacy Economics

Recommended Readings

  • A. Acquisti, S. Gritzalis, C. Lambrinoudakis, S. De Capitani di Vimercati (Eds) (2008) Digital Privacy, Theory, Technology and Practices., Auerbach Publications.

Associated scientific Journals

  • IEEE Security and Privacy Magazine, IEEE
  • International Journal of Information Security, Springer
  • Computers and Security, Elsevier
  • Requirements Engineering, Springer
  • IEEE Transactions on Software Engineering, IEEE
  • Security and Communication Networks, Wiley

Multimedia Technology

Learning Outcomes

This course is the basic introductory course for the perception, representation and management of digital media through computational methods.

The course material focuses on the introduction of the students to the basic concepts and algorithms for the representation, processing and interaction with digital audiovisual media. Moreover, the course material refers to the description of the correlation between computational techniques and human perception in media environments.

The course seeks to make students understand the ways with which is possible the development and management of media sources in coomputational systems.

With the successful completion of the course the student will be capable of:

  • understanding the basic and important features of the computational representation, processing and interaction with digital audiovisual media.
  • knowing the major features of the tools and development methods of digital audiovisual environments and applications.

Course Contents

  • Definition and classification of multimedia technologies.
  • Audio and visual perception.
  • Audio processing.
  • Image and video processing.
  • Design and development of multimedia systems.

Optimization Techniques

Learning Outcomes

The course pertains to the modeling and solving of operational research problems via linear programming, integer programming and related optimization models. In this context, the theoretical foundations of these optimization models are developed; solving algorithms are presented, for global optimization (e.g., the Simplex method, Branch-and-Bound), as much as the design and analysis of heuristic methods, inclusively of local search and approximation algorithms.

Upon successful completion of the course, the students will be in position:

  • to develop the formal/abstract mathematical representation of an operational optimization problem, given its description in natural language along with the problem’s parameters and input data.
  • to choose appropriate solving methods for a given mathematical model of an operational optimization problem.
  • to program a mathematical optimization model in an appropriate programming language, while using relevant software for solving the model.
  • to assess and evaluate the solution to a mathematical optimization model, along with the performance of the chosen solving method.
  • to discriminate between computationally tractable and hard mathematical models for operational research problems.

Course Contents

  • Modeling Problems through Linear Programming.
  • Linear Programming Theory, Duality.
  • The Simplex Algorithm.
  • Integer Linear Programming, Branch and Bound Method.
  • Transportation and Assignment Problems.
  • Network Optimization (paths, trees, flows, matchings, cuts).
  • Computationally Hard Optimization Problems.
  • Introduction to Approximation Algorithms.
  • Local Search Methods.

Recommended Readings

  • F. S. Hillier, G. J. Lieberman. Introduction to Operations Research. McGraw-Hill Higher Education, 2004.
  • J. Kleinberg, E. Tardos. Algorithm Design. Pearson, 2013.

Collaborative Learning Environments

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.



Advanced Artificial Intelligence Topics

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).
  • 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