<br/>Laboratory courses are an important component of university science education as students progress on their career trajectories in science, technology, engineering, and mathematics (STEM) careers. As a teaching and learning setting, a lot of learning occurs when students apply chemistry knowledge in the real world by engaging novel skills like measurement/instrumentation and scientific practices like justified argumentation. Improving learning in laboratory settings has the potential to accelerate student understanding of science and increase their likelihood of pursuing STEM careers beyond their undergraduate studies. However, one challenge to studying laboratory contexts is that they—unlike their lecture counterparts—still remain highly analog where the knowledge, practices, and skills that a learner engages must be directly documented and observed in order to gain insight into what students know. In this study, we investigated the affordances of a scalable, digital laboratory learning platform (Labflow) to collect and analyze rich outcome and process datasets from an undergraduate chemistry laboratory course. We highlight what data sources have successfully been generated through Labflow, e.g. aggregate achievement measures like auto- and hand-graded scores as well as process metrics like activity with course materials (PDFs, videos, reports) and time on task. We conclude by situating the discussion of how Labflow can be used to collect data for common research design prototypes like pre/post test interventions, qualitative response analysis, and path analysis.

Using labflow as a platform to accelerate research in laboratory education

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.