Visualization as theory and experience

Interactive qualitative data visualization for the learning sciences

Datos Bibliográficos

ID21633137
AutoresBen Rydal Shapiro (0000-0001-8070-0952, Georgia State University, autor de correspondencia), Deborah Silvis (0000-0001-5139-9048, SUNY Cortland), Rogers Hall (0000-0003-0849-6058, Vanderbilt University)
Año2025
Volumen34
Número5
Páginas840-871
Fecha de publicación2025-10-20
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of the Learning Sciences (JOURNAL)
Identificadores de la revistaISSN: 1050-8406 • E-ISSN: 1532-7809
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10508406.2025.2537945
OpenAlexW4413680461
IdiomaEN
Referencias citadas77

Background A long-standing challenge in the learning sciences is how to visualize qualitative data, such as video and transcripts, in ways that illuminate the layered, relational, and dynamic nature of teaching and learning.Methods This methodological article integrates methods of interaction analysis (IA) with recent developments in interactive and alternative visualization to expand the kinds and qualities of representations used to conceptualize teaching and learning.Findings We introduce three open-source visualization tools we designed to support IA that offer different framings of video as qualitative data. Transcript Explorer frames video as playscript-like records, offering ways to dynamically explore transcripts of multimodal interaction linked to video. The Interaction Geography Slicer frames video through the lens of movement, providing an approach to dynamically visualize interaction over geographic space and time. The Pointillizer frames images in video to characterize material and affective dimensions of a scene.Contribution We illustrate how these tools embody distinct epistemological commitments and foster new researcher-data-relationships that enliven the analysis and experience of teaching and learning. In doing so, we contribute to broader shifts in the methodological landscape of the learning sciences, with practical implications for fields such as teacher education and the use of artificial intelligence in educational research

Data science · Data visualization · Human–computer interaction · Information visualization · Visualization · Computer Science · Data Visualization and Analytics · Participatory Visual Research Methods · Qualitative Research Methods and Applications · Artificial Intelligence