Events

Representational competence under the magnifying glass: Instructional landscape, student reasoning, and a refined model

Currently, there is little consensus on how science education researchers conceptualize representational competence. Even though understanding what it means to be representationally competent is fundamental for supporting learners in developing this important set of skills, there is little agreement about capturing and characterizing these skills. This lack of agreement might be partly due to the lack of specificity and explanatory power of the current models that conceptualize representational competence. Within this talk, I will describe our refined model of representational competence – the Interconnected Model of Representational Competence Skills. This model has been elucidated as part of our efforts to characterize chemistry students’ representational competence in the context of representations of molecular structure. Specifically, we conducted semi-structured interviews to investigate how students engage with these representations across tasks associated with multiple representational competence skills. Our results indicate that student competence varies across different skills which supports the notion that to make comprehensive inferences about representational competence, one needs to characterize and synthesize findings across multiple skills associated with this construct. Furthermore, we found that the skills are interconnected with each other, as some serve as prerequisites for others. These findings informed the Interconnected Model of Representational Competence Skills. Importantly, the model provides new ways of thinking about instruction and research.

Hosted by Professor Mary Emenike

Location: Zoom Video Conferencing

Please contact Loretta Lupo at This email address is being protected from spambots. You need JavaScript enabled to view it. for Zoom information