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Facilitating Team Based Learning – tools to succeed: pre-class preparation.
Traditionally, the pre-class preparation phase of TBL involved students covering readings prior to a session (Michaelsen, 2002). Pre-class readings may have included chapters/excerpts from textbooks and articles or written content created by the educator. Nowadays, we can share a variety of different types of content with students (Parmelee et al., 2012). We are not confined to “text”. We can also provide access to content electronically e.g. through learning management systems (LMSs). This helps to cut down on paper usage. Here we outline 5 technology-related tips to keep in mind when we are considering how we will design the pre-class preparation phase. Click on the links throughout the document for more information and guidance.
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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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