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This is an interactive multimedia open educational resource (OER), created using H5P, on the topic of Open Education. It has been developed by the Department of Technology Enhanced Learning at Munster Technological University.
The purpose of which is to give an overview of OEPs, using interactive features of H5P
This resource can be used by educators, teaching about Open Educational Practices in a live teaching setting or through online or digitally mediated courses.
Creative Commons Attribution-NonCommercial (CC BY-NC)
This work is licensed under a CC BY-NC license, permitting sharing and adaptation for non-commercial purposes with proper attribution.
https://creativecommons.org/licenses/by-nc/4.0/If you have modified or adopted this resource, share your version here. Tracking adaptations helps us measure impact and connects others with useful updates.
This OER presents findings from a comparative study of novice programmers engaging in face-to-face and hybrid pair programming. It explores collaboration patterns, role switching, satisfaction, and challenges, offering evidence-based insights for educators seeking to prepare students for effective teamwork in modern hybrid software development.
This OER explores novice programmers’ experiences of pair programming across face-to-face, hybrid, and remote settings. It provides insights into collaboration, role switching, satisfaction, and challenges, helping educators and students understand how to effectively prepare learners for modern hybrid software development environments.
This OER guides students through human-in-the-loop software development, demonstrating how AI tools can be effectively supervised, refined, and integrated across the Software Lifecycle. Designed for computing educators and learners, it combines agile practice, teamwork, DISC awareness, testing, and critical reflection on human–AI collaboration.
This OER introduces students to designing and developing AI-powered assistants for agile software development using Flowise (no code). Learners as a team explore Retrieval Augmented Generation (RAG) and agent-based systems, applying AI to real-world agile practices while considering technical design, evaluation, and cost-aware decision-making
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