Join Dr Strangelove or: How I learned to stop worrying and love the Learning Outcome, a Training Club session led by Matthew Gemmell (University of Liverpool). This practical, research-led workshop explores how SOLO and Bloom taxonomies can help educators design clear, achievable learning outcomes in higher education, particularly within computational biology and bioinformatics.

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This Training Club session introduces an evidence-based approach to developing effective learning outcomes (LOs) in higher education, with particular attention to their application in computer science and related disciplines. Participants will examine the theoretical foundations of LOs, explore the challenges in their formulation, and apply a combined SOLO–Bloom taxonomy framework to design outcomes that are both realistic and pedagogically sound.

Learning outcomes

By the end of the session, participants will be able to:

• Understand the use and benefit of LOs in HE
• Explain the difficulties of creating LOs
• Know the categories in the SOLO and Bloom’s taxonomy
• Apply the SOLO and Bloom’s taxonomy to LOs
• Create LOs with the combined SOLO and Bloom’s taxonomy

Challenges

Developing meaningful LOs is often complex, particularly within computer science education, where cognitive processes can span from conceptual understanding to creative problem-solving. Assigning the appropriate cognitive level within Bloom’s taxonomy has proven difficult, and even experienced educators report uncertainty when applying these frameworks. Poorly calibrated outcomes can risk demotivating learners if perceived as unattainable.

A combined SOLO–Bloom approach

This session proposes the integration of the SOLO and Bloom’s taxonomies as a pragmatic solution for structuring LOs. SOLO provides a framework for increasing complexity of understanding, while Bloom’s taxonomy categorises cognitive processes. The combined matrix offers nine potential pairings, supporting a more nuanced calibration of expectations. Selecting realistic levels helps align learning tasks with achievable progression, while higher-order combinations encourage deeper cognitive engagement.

Session format

Participants will work collaboratively to analyse sample learning outcomes, identify their taxonomic levels, and reformulate them using the combined model. The exercise will encourage reflection on clarity, cognitive depth, and assessment alignment.