Marrying Asset- and Deficit-Based Approaches: A Data Feminist Perspective in Learning Analytics

This workshop explores how learning analytics can reconcile deficit- and asset-based approaches. Deficit-based models focus on identifying and remedying learner shortcomings in order to move them towards a specific learning standard. However, this approach may neglect learners’ existing strengths. An asset-based approach may support this, where learners' identities, values, and existing knowledge are considered as assets to their learning. In this workshop, we advocate for a combination of both. We ground our discussion in the data feminism framework, which examines power structures in data design and interpretation. We will delve into three core data feminism principles: examine power, challenge power, and rethink binaries and hierarchies, to construct narratives affirming students' diverse identities.