Technologies for Open Education

Objectives and competences

The aim of this course is to provide an overview and to build fundamentals needed to under-stand technologies supporting educational pro-cesses. Students will explore and evaluate vari-ous technologies and determine how, when, and why such technologies can/should be in-fused into face-to-face, hybrid, or fully online learning situations.

Students will obtain the following competenc-es:

  • the ability to plan, choose, implement, and evaluate technology for teaching and learning;

  • the ability to decide autonomously about the most suitable technology to establish learning environment aimed at different target groups in concrete educational contexts;

  • the ability to produce, test and evaluate basic open education solutions (applications, tools, services, platforms);

  • the ability to blend/scaffold different technologies to enhance capabilities of specific learning environments.


Students should have a basic understanding of the learning process in open education, as thaught in the Introduction to Open Edication course in the first semester of the first year. They should be able to use computer tools, as well as communication and collaboration tools. Additionally, they should be prepared to work in interdisciplinary teams.


Information and Communication Technolo-gies (ICT) create new opportunities and new expectations for learning and teaching. This course will focus on understanding of cur-rent technology trends and explaining their relation with learning and teaching pro-cesses. The following topics will be ex-plored in more detail:

  1. Definition, types and examples of ICT enabled learning environments (Learning systems platforms and architectures)
  2. Opportunities and challenges for Im-plementation of learning environments
  3. ICT in Classrooms, Ubiquitous and Mobile Technologies
  4. Computer-supported Collaborative Learning Technologies
  5. Media technologies and Multimedia
  6. Authoring and Design Tools for Development of digital content for e-learning
  7. Artificial Intelligence in Education/Intelligent Tutoring System and Adaptive Learning
  8. Game and Toy Enhanced Learning Technologies
  9. Learning Analytics (LA) / Educational Data Mining (Technologies for Testing and Assessment )
  10. Social computing for learning

Intended learning outcomes

After completion of this course, students will be able to:

• Understand and differenciate between different kinds of learning environments

• Evaluate different learning environments and to select learning environment for specific context (target audience, learning preferences, ...)

• Understand principles of designing and constructing effective learning environments by using appropriately selected technologies and tools.


  • Tim D. Green, Abbie H. Brown, The Educator's Guide to Producing New Media and Open Educational Resources, Routledge; 1 edition (July 6, 2017)
  • Rajiv Jhangiani, Open: The Philosophy and Practices that are Revolutionizing Education and Science, Ubiquity Press (April 11, 2017) E-version)
  • Nick Rushby and Dan Surry , The Wiley Handbook of Learning Technology (Wiley Handbooks in Education), Wiley-Blackwell; 1 edition (April 25, 2016)
  • Gordon Lewis, Learning Technology (Language Education Management), Oxford University Press (April 10, 2017)


• Interim presentations • Final written exam will combine the theoretical knowledge and the practical work on adapting existing courses to fit a different scenario.

Lecturer's references

Prof. Suzana Loshkovska, Ph.D. received the bachelor and master degrees in computer science and automation from the Faculty of Electrical Engineering, Skopje, in 1988 and 1992, respectively, and the Ph.D. from the Technical University of Wien, Wien, Austria in 1995. She is a full professor of Computer Science and the head of the Department of Software Engineering at the Faculty of Computer Science Engineering, “Ss. Cyril and Methodius” University in Skopje. Her research interests include programming, visualization, human-computer interaction, virtual reality, medical imaging, and technologically enhanced learning. Suzana Loshkovska has over 25 years of experience in teaching, supervising and guidance of undergraduate and graduate students in the fields of programming, medical informatics, content-based image retrieval, visualization and human-computer interaction. Also, she has over 10 years of experience in developing and ensuring quality assurance of study programs for higher education institutions. She entered the Open Education for a Better World program as a mentor from the very beginning, and acts as a mentor in international teams.

Selected bibliography:

  1. Dimitrovski, Ivica & Spasev, Vlatko & Loskovska, Suzana & Kitanovski, Ivan. (2024). U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery. Remote Sensing. 16. 2077. 10.3390/rs16122077.
  2. Trojachanec, D., Kitanovski, D., Dimitrovski, I., Loshkovska, S., (2023). A Multi-Modality Approach to Medical Case Retrieval for Alzheimer's Disease. In 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, Proceedings. ISBN 978-989-758-631-6, ISSN 2184-4305, (pp. 554-561). DOI: 10.5220/0011939800003414.
  3. Dineva, K.T., Kitanovski, I., Dimitrovski, I., Loshkovska, S. and Alzheimer’s Disease Neuroimaging Initiative (2022). Combining Static and Dynamic Features to Improve Longitudinal Image Retrieval for Alzheimer’s Disease. In ICT Innovations 2022. Reshaping the Future Towards a New Normal: 14th International Conference, ICT Innovations 2022, Skopje, Macedonia, September 29–October 1, 2022, Proceedings (pp. 107-120). Cham: Springer Nature Switzerland.
  4. Loshkovska, S. (2022) Virtual and Augmented Reality in Education, International Conference on New Approaches (ICNAE’22)/2022, pp. 86-90
  5. Trojachanec, D., Kitanovski, D., Dimitrovski, I., Loshkovska, S., (2022). Missing Data in Longitudinal Image Retrieval for Alzheimer’s Disease. In 19th International Conference for Informatics and Information Technology, Proceedings (pp.69-74). 2022.
  6. Ademi, N. and Loshkovska, S., (2020) Clustering Learners in a Learning Management System to Provide Adaptivity, ICT Innovations 2020, web-Proceedings (, pp. 82-95
  7. Ademi, N., Loshkovska, S., (2020) Weekly Analysis of Moodle Log Data in RStudio for Future Use in Prediction, 17th International Conference on Informatics and Information Technologies - CIIT 2020, 8-May-2020, Mavrovo, North Macedonia.
  8. Ademi, N., Loshkovska, S., Chorbev, I., (2020) User Modeling Approaches in Adaptive Learning Systems, International Journal of Technical and Natural Sciences, Skopje, North Macedonia, 2020, Vol.1(2), [Available at:]
  9. Ademi N., Loshkovska S. (2019) Exploratory Analysis of Student Activities and Success Based on Moodle Log Data, CiiT - 16th International Conference on Informatics and Information Technologies, Mavrovo, Macedonia, May 10-12, 2019.
  10. Ademi N., Loshkovska S., Kalajdziski S. (2019) Prediction of Student Success Through Analysis of Moodle Logs: Case Study. In: Gievska S., Madjarov G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham
  11. Ademi, N., Loshkovska, S. (2019). Early Detection of Dropouts in E-Learning Systems. Academic Perspective Procedia, 2 (3), 1008-1015. DOI: 10.33793/acperpro.02.03.112
  12. Ademi, N., Loshkovska, S., (2019) User Data In Adaptive Learning Systems, Turkish Studies Information Technologies and Applied Sciences, Volume 14 Issue 4, 2019, p. 507-518.
  13. Ademi, N., Loshkovska, S., Chorbev, I., (2019) Reinforcing motivation and engagement by behavioral design in learning systems, International Open & Distance Learning Conference, IODL 2019, 14-16 November 2019, Eskisehir, Turkey, p. 237-244.