About Resource
Data Jams are a tool for engaging students with authentic scientific data sets and improving their skills in data literacy and science communication. Students learn to interpret numerical data and explain it using creative, artistic expressions such as dance, poetry, songs, videos, and sculpture. The model was first developed in 2012 by the Asombro Institute for Science Education in coordination with the Jornada Basin Long-Term Ecological Research site using ecological and environmental datasets.
Data Jams can be applied in both informal and formal settings, but always take on the following flow:
- Students are provided authentic, local data sets
- Students engage in supported data exploration and learn to plot data, explore trends, and/or compare variables
- Students create a scientific product (poster, presentation, report) that includes claim-evidence-reasoning
- Students communicate major findings from their data exploration in a creative medium of their choosing
- Projects are shared and assessed
How to Use
The Data Jam model taps into shared STEAM and NGSS goals by encouraging collaboration, supporting interdisciplinary thinking, enhancing data analysis and communication skills, strengthening connections to the local environment, and fostering a deeper understanding of core scientific concepts. As such, Data Jams are a suitable match for informal science centers where ongoing educational programming for high school students exists. Practically speaking, students should be allowed multiple sessions to explore, thoroughly grasp, and reinterpret their dataset. (See full article for more on this and other practical recommendations.)
As designed, Data Jams do not engage students in scientific study design or execution but rather rely on high-quality (sometimes complex) data, to encourage students to explore inquiry-related science practices. Applied within the context of the ASTC Community Science Framework, Data Jams may represent an engaging extension after student data collection, or a precursor before students learn to collect their own local environmental datasets.
Location: USA
Author(s)/Organization: Michelle Forster, Stephanie Bestelmeyer, Noelia Baez-Rodriguez, et al.
Publication Year: 2018
Tags
Attributes:Centers Community Priorities
Outcomes:Increased Science Agency, Strong Community Partnerships
Approaches:Participatory Research
Type:Program Examples