Sense Making Alone Doesn't Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations

摘要

Previous research demonstrates that multiple representations of learning content can enhance students' learning, but also that students learn deeply from multiple representations only if the learning environment supports them in making connections between the representations. We hypothesized that connection-making support is most effective if it helps students make sense of the content across representations and in becoming fluent in making connections. We tested this hypothesis in a classroom experiment with 599 4lh-and 5lh-grade students using an ITS for fractions. The experiment further contrasted two forms of support for sense making: auto-linked representations and the use of worked examples involving one representation to guide work with another. Results confirm our main hypothesis: A combination of worked examples and fluency support lead to more robust learning than versions of the ITS without connection-making support. Therefore, combining different types of connection-making support is crucial in promoting students' deep learning from multiple representations.