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Supporting Representational Competences Through Adaptive Educational Technologies

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Towards a Framework for Representational Competence in Science Education

Part of the book series: Models and Modeling in Science Education ((MMSE,volume 11))

Abstract

Helping students acquire representational competences is an important educational goal in many STEM domains. In particular, students need to acquire connection-making competences: they need to conceptually understand how different representations map to one another, and they need to be perceptually fluent in translating between representations. I present a number of experiments on instructional support for connection-making competences. The experiments were conducted in the context of intelligent tutoring systems for elementary-school fractions and undergraduate chemistry. Intelligent tutoring systems are educational technologies that adapt to the individual student’s knowledge level in real time, based on a cognitive model of their learning that is updated throughout the learning experience. Results show that the effectiveness of different types of instructional support depends on a number of student characteristics. Support for conceptual connection-making competences is most effective for students who have a basic level of prior domain knowledge but who are not yet proficient. Support for perceptual fluency in translating between representations is most effective for students with high mental rotation ability. Furthermore, the sequence in which conceptual and perceptual connection-making support should be provided depends on the students’ prior conceptual understanding of connections. These findings suggest that adaptive educational technologies might be more effective if they adaptively select the appropriate type of representational support based on a real-time assessment of the student’s current knowledge level. Such adaptive support is likely to not only result in better learning of the domain knowledge but also in better attainment of representational competences.

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  1. University of Wisconsin, Madison, Madison, WI, USA

    Martina A. Rau

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  1. Texas State University, San Marcos, Texas, USA

    Kristy L. Daniel

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Rau, M.A. (2018). Supporting Representational Competences Through Adaptive Educational Technologies. In: Daniel, K. (eds) Towards a Framework for Representational Competence in Science Education. Models and Modeling in Science Education, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-89945-9_6

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