Author: Nikos Kavallaris

A bit of a belated post for reflection week, but a very necessary one.

During the first half of the ONL course, I truly enjoyed the interaction with the rest of the group members. Working together in a supportive and open atmosphere made it easier to share ideas and experiment with new approaches. I feel that I have learned a great deal about different platforms and AI tools that can be used for online teaching, in particular from colleagues working at US universities, who often seem more experienced and confident in this area. Their examples and practical tips have broadened my understanding of what is possible in online and blended learning.

Looking ahead, I would like us as a group to challenge ourselves more in the second half of the course. In particular, it would be interesting to experiment with more creative and varied ways of presenting our discussions and outcomes for each topic, following the FISh document. For example, we could try short video explainers, interactive presentations, infographics, or simple prototypes of online activities rather than relying mainly on slides or written summaries. We could also rotate roles more intentionally (facilitator, timekeeper, summariser, tech host) to ensure everyone has the chance to contribute in different ways and to keep our collaboration dynamic.

On a more personal level, I see several areas where I would like to improve. I would like to take a more active role in proposing tools or formats we can try as a group, rather than mainly responding to others’ suggestions. I also want to prepare more systematically before meetings—for example, by exploring at least one new platform or AI-based tool that might be relevant for our topic—so that I can bring concrete examples to the discussion. Finally, I hope to reflect more deliberately on how what we do in ONL can be transferred to my own courses, for instance by keeping a small “idea log” of activities, tools and strategies that I can test in my future online and blended mathematics teaching.

Through my engagement in the ONL course, I have learned a great deal about concrete practices for designing and running online courses and using different platforms more effectively. Interacting with colleagues from various institutions, especially from US universities, was particularly eye-opening. They often seemed more experienced and confident with online and networked learning, which both inspired me and challenged me to rethink my own assumptions about what is possible in an online environment.

What I take with me into my own practice are many of the ideas and strategies discussed in the four topic areas of the course. I plan to experiment more systematically with student engagement strategies (e.g. structured interaction, clearer scaffolding, and more varied activities) and with design principles for online and blended courses. My aim is to build courses that are not only well organised, but that also make it easier for students to participate actively and feel supported throughout.

In my own context of teaching mathematics, I see great potential in using technology to enhance learning and teaching. I am eager to explore online platforms for interactive problem-solving, as well as AI tools that can support the development of course materials, visualisations, and even draft feedback or alternative explanations of concepts. I believe these tools, if used transparently and critically, can help students see mathematics as more accessible, exploratory and relevant.

As a result of my involvement in ONL, I intend to explore more systematically both synchronous and asynchronous strategies for an online mathematics course I will teach next year. I want to combine live sessions, where we work through examples and questions together, with asynchronous activities that encourage reflection, practice and collaboration. My goal is to boost students’ active participation and create a stronger sense of continuity between sessions.

For the development of eLearning in computational mathematics in particular, I see several promising directions: integrating interactive notebooks where students can experiment with code and immediately see the mathematical consequences; designing small computational projects where students model real-world problems; using auto-graded exercises for routine skills, so that class time can focus more on interpretation and problem-solving; and encouraging students to work in pairs or small groups on shared computational tasks. These approaches could make the learning of computational mathematics more engaging, authentic and aligned with how mathematics is used in practice.

Building trust in online and blended learning environments requires being consistently present, responsive, and transparent with students. I can actively foster this trust by creating clear communication channels, offering timely feedback, and demonstrating empathy toward individual learning needs. Establishing a supportive environment also means being intentional about facilitation—guiding discussions, encouraging participation, and using scaffolding techniques that break complex tasks into manageable steps. This helps students feel confident and supported as they progress.

At the same time, new technologies offer powerful opportunities to enhance learning and assessment. By thoughtfully integrating tools such as interactive platforms, collaborative workspaces, and adaptive assessment tools, I can create more engaging, personalized learning experiences. These technologies allow for ongoing formative assessment, real-time feedback, and diverse ways for students to demonstrate understanding. Ultimately, trust-building and effective use of technology work hand in hand: when students feel supported and see technology used meaningfully, they are more likely to engage deeply and take ownership of their learning.

Reflecting on the scenario of Topic 3: Learning in communities – networked collaborative leaning, I recognize that technology can play a transformative role in moving students from simply dividing tasks to truly engaging in social learning. By integrating digital tools that make thinking visible — such as Google Docs, Miro, or Padlet — I can help participants co-construct ideas in real time and see how understanding evolves collectively. Platforms like Teams, Zoom, and Canvas enable dialogic spaces where learners question, challenge, and refine each other’s thinking. To deepen reflection, tools such as OneNote, Seesaw, or Mentimeter can capture metacognitive insights about how the group collaborates and learns. Through intentional design, I aim to shift collaboration from a task-oriented process to a community of inquiry, where technology supports transparency, empathy, and shared meaning-making. Ultimately, these practices will not only enhance the quality of group work but also nurture participants’ capacity for authentic, lifelong collaboration beyond the course.

The debate between open and closed technologies continues to be a central issue in discussions about innovation, equity, and sustainability in the digital age. Open technologies—such as open-source software, open-access publishing, and open educational resources—are often celebrated for their inclusivity, transparency, and collaborative potential. They empower users to access, modify, and share knowledge freely, thus fostering creativity and global participation. From an educational standpoint, open technologies democratize learning, allowing individuals and institutions with limited resources to benefit from high-quality tools and materials.

However, openness also presents challenges that are often less visible. The sustainability of open initiatives depends heavily on voluntary contributions, which can lead to inconsistencies in quality, maintenance, and support. Moreover, the assumption that “open” automatically equals “accessible” can be misleading; for instance, while open software might be free to use, it often requires technical skills or infrastructure that not all users possess. Similarly, in the context of open-access publishing, authors from less affluent institutions may face financial barriers to publication despite the ideal of openness.

Closed technologies, on the other hand, offer a different kind of value. Their controlled environments can ensure higher standards of security, quality assurance, and long-term stability. Companies managing closed systems can provide dedicated customer support and invest in continuous improvement, funded through licensing or subscription models. Yet, such control often comes at the cost of user autonomy and transparency. Closed technologies can create dependency, limiting innovation to what corporations deem profitable and potentially exacerbating global inequalities in access to information and tools.

Ultimately, neither open nor closed technologies offer a perfect solution. Both reflect deeper ethical, economic, and cultural values shaping our digital ecosystem. Perhaps the most critical question is not which model is superior, but how societies can balance openness and protection—ensuring that technological progress remains both inclusive and sustainable.

In the context of Professional and Lifelong Learning (PNL), the rise of artificial intelligence (AI) tools has significantly reshaped how openness is understood and practiced. Openness—traditionally linked to free access and sharing of educational resources—now extends to how content is created, adapted, and verified in digital learning environments.

AI enhances openness by making learning more inclusive and accessible. Automated translation, text-to-speech, and content generation tools allow learners from diverse backgrounds to access materials in multiple languages and formats. This supports the PNL goal of widening participation and lifelong access to education.

At the same time, AI raises new challenges. It blurs the lines between human and machine authorship, creating uncertainty around originality, intellectual ownership, and ethical use. Without transparency about AI’s role in creating or adapting content, the spirit of openness may be undermined.

Therefore, openness in the age of AI requires more than free access—it demands honesty, critical awareness, and ethical responsibility. PNL practitioners must model transparent AI use, verify generated outputs, and help learners engage critically with these tools. In doing so, AI can become not a threat to openness but a means of enhancing inclusion, innovation, and trust in lifelong learning.

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