13–15 September 2024, Bath Spa University, UK
Tina Rozsos
Find me at t.rozsos@ucr.nl, or reach out to the UCR Data Center at datacenter@ucr.nl.
With the rise of AI, Big Data, and other technological developments, higher education institutions increasingly face the challenge of how to give students a skill set that allows them to succeed in this fast-changing and increasingly data-driven world. To prepare students for a modern world where transferable skills like data literacy – the ability to “think with data” – are essential, we established the UCR Data Center (DC). In the past three years we learned best practices both from our own experiences and from observing other liberal arts colleges with initiatives that promote data literacy across the curriculum and beyond. As a next step, we hope to share what we have learned so far in this attempt at improving data literacy in a liberal arts and sciences college.
The DC facilitates the integration of data-related work in course assignments, internships, and student and faculty research, with the goal of helping students from all backgrounds get more comfortable with working with data. First, we have introduced “data encounters” in UCR courses across the curriculum: we collaborate with course instructors and undergraduate interns to develop course materials, workshops, and assignments that teach basic data literacy skills in the disciplinary context of each course. So far we planned data encounters in 10 different courses in a wide range of fields, such as Archaeology, Ecology, or Cognitive Science; all materials we develop are publicly accessible on our website: https://ucrdatacenter.github.io/. Second, we started an apprenticeship program (based on a similar project at Wesleyan University) where students have an opportunity to work with faculty on research projects that involve working with data: we support these collaborations by running workshops and providing individual guidance to the participating students. In this apprenticeship program we have also explored recent developments in AI tools to make learning data science skills more accessible for beginners.
Our approach to teaching data literacy fits well into the liberal arts teaching philosophy and environment. We use an across-the-curriculum approach because it allows us to show students both the interdisciplinarity and breadth, and the disciplinary relevance of data science. In addition, we contribute to the college’s explicit focus on cultivating students’ research skills by facilitating student involvement in (potentially interdisciplinary) faculty research, and by having the data encounters require practical, hands-on work that relate to real problems encountered in academia and the industry.