Adaptive support for representational competencies during technology-based problem solving in chemistry

ABSTRACT Background: A key aspect of STEM learning is the use of visual representations for problem solving. To successfully use visuals, students need to make sense of how they show concepts and to fluently perceive domain-relevan information in them. Adding support for sense making and perceptual fluency to problem-solving activities enhances students’ learning of content knowledge. However, students need different types of representational-competency supports, depending on their prior knowledge. This suggests that adaptively assigning students to sense-makingand perceptual-fluency support might be more effective than assigning all students to the same sequence of these supports. Method: We tested this hypothesis in an experiment with 44 undergraduate students in a chemistry course. Students were randomly assigned to a ten-week sequence of problem-solving activities that either provided a fixed sequence of sense-making support and perceptual-fluency support or adaptively assigned these supports based on students’ problem-solving interactions. Findings: Results show that adaptive representational-competency supports reduced students’ confusion and mistakes during problem solving while increasing their learning of content knowledge. Contribution: Our study is the first to show that adaptive support for representational competencies can significantly enhance learning of content knowledge. Given the pervasiveness of visuals, our results may inform general STEM instruction.