Blended Sensory Walks: Teaching Data Literacy Through Anthropological Practice

Anthropology curricula often neglect quantitative and computational methods, leaving students underprepared for data-rich research. Yet ethnographic practices such as sensory walks naturally generate digital materials suited for computational analysis. We developed a blended intervention that combines sensory walks with image clustering, introducing data science while preserving anthropology’s experiential and interpretive focus. Participants used smartphones to document sensory impressions during 30–45-minute walks, creating small personal datasets. Images were embedded and clustered with hierarchical clustering. Tested in four workshops with groups ranging from primary school pupils to postgraduate students, the results were explored interactively, interpreted qualitatively, and discussed collectively, linking algorithmic outputs with ethnographic reflection. The activity produced three main outcomes. First, clustering highlighted thematic patterns in images, such as contrasts between urban and natural motifs. Second, participants gained accessible exposure to embeddings, clustering, and exploratory analysis, building data literacy. Third, the exercise prompted critical reflection on algorithmic methods, raising questions of trust, interpretability, and limits. Participants also reported greater sensory awareness of their surroundings. This approach shows how computational methods can be integrated into anthropology without sacrificing interpretive depth. Its simplicity, adaptability, and low cost make it a scalable model for teaching and research.