An adaptive collaboration script for learning with multiple visual representations in chemistry

Undergraduate STEM instruction increasingly uses educational technologies to support problem-solving activities. Educational technologies offer two key features that may make them particularly effective. First, most problem-solving activities involve multiple visual representations, and many students have difficulties in understanding, constructing, and connecting these representations. Educational technologies can provide adaptive support that helps students make sense of visual representations. Second, many problems with visual representations involve collaboration. However, students often do not collaborate effectively. Educational technologies can provide collaboration scripts that adaptively react to student actions to prompt them to engage in specific effective collaborative behaviors. These observations lead to the hypothesis we tested: that an adaptive collaboration script enhances students' learning of content knowledge from visual representations. We conducted a quasi-experiment with 61 undergraduate students in an introductory chemistry course. A control condition worked on a traditional worksheet that asked students to collaboratively make sense of connections among multiple visual representations. An experimental condition worked on the same problems embedded in an educational technology that provided an adaptive collaboration script. The experimental condition showed significantly higher learning gains on a transfer test immediately after the intervention and on complex concepts on a midterm exam three weeks later.