Adaptive Support for Representational Competencies during Technology-Based Problem-Solving in STEM

Abstract

Most STEM problem solving involves visual representations. To successfully use visuals, students need to make sense of how they show concepts and to fluently perceive domain-relevant information in them. This chapter presents a series of studies showing that providing support for sense making and perceptual fluency in problem-solving activities enhances students’ learning of STEM content knowledge. Further, it describes how learning analytics revealed that prior knowledge affects how students interact with representational competency supports. These analyses showed that students with different levels of prior knowledge need different types of representational-competency supports. Together, these studies led to the hypothesis that adaptively assigning students to sense-making and perceptual-fluency support might be more effective than assigning all students to the same sequence of these supports. A recent experiment tested this hypothesis within an undergraduate chemistry course. Adaptive representational-competency supports reduced students’ confusion and mistakes during problem solving while increasing their learning of content knowledge. Taken together, the research presented in this chapter shows that adaptive support for representational competencies can significantly enhance STEM learning. It concludes by discussing how – given the pervasiveness of visuals – these findings may inform general STEM instruction, and how future research can build on these findings.