Intergenerational Family Storytelling and Modeling with Large-Scale Data Sets

Open large-scale data sets (LSDS; 4) and visualization tools have opened a new design space for youth and family collaborative learning. Using a corpus of video-recorded interviews, we examine how youth and parents together explore their personal family migration histories---their geobiographies---and broader socioeconomic, historical trends using dynamic data visualization tools. We introduce the Family Alignment of data Models and Stories (FAMS) process to describe how parent-child interactions with LSDS, through cycles of aligning family narratives to data, produce family stories about migration. Applying the FAMS model to four family cases revealed that grounding family narratives in jointly constructed interpretations of data sets encourages generative co-constructions of those stories, which contrasts with family storytelling settings in which narrators' interpretations compete to become the basis for the accepted version of events 23. We discuss how the FAMS model can facilitate deeper understandings of intergenerational engagement with LSDS.