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.