Twenty Constructionist Things to Do With Data Science

Building on Seymour Papert and Cynthia Solomon’s 1971 memo on “Twenty Things to Do with a Computer,” we consider burgeoning areas of data science as a context to affirm and extend constructionist tenets concerning the power of material production in learning. Like Papert and Solomon’s original treatise about computing, our considerations are motivated by the reality that much of the developmental and learning benefits of engaging with data remain out of reach for youth. Consistent with their argument, we suggest this is not because of any requisite complexity or skill but because education fields appear reluctant to progress toward more generative stances about what it means to learn from material features of data, which are distinctly emphasized in data science. The twenty things we highlight include a variety of topic contexts and epistemic pathways learners can pursue when engaging with data as material for generative inquiry and production. The things are meant to highlight what data materiality means from a constructionist viewpoint – and how data as a science might help us nuance these conceptualizations. This includes more critical consideration of the myriad ways material production fruitfully reflects learner priorities and, consequently, the social and cultural tensions accompanying outcomes. We discuss these observations in terms of their implications for constructionism and learning.