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(Group Leaders: Christoph Hoyer, Stefan Küchemann & Jochen Kuhn)
For scientific learning, multiple external representations (MERs) play a beneficial role, which is well documented for the natural sciences (Tytler et al., 2013) and for physics (Treagust et al., 2017). MERs are especially important for conceptual understanding (Verschaffel et al., 2010) and are discussed as a necessary condition for in-depth learning (diSessa, 2004). Ainsworth (2006) created a conceptual framework that provides indications and criteria for the effective use of MERs in teaching and learning scenarios and their unique benefits when dealing with complex or new scientific content. In this taxonomy, learning with MERs refers to learning from two or more external representations (e.g., diagrams, formulas, and data tables) simultaneously. According to the suggested framework, especially the design, functions, and tasks (DeFT) of multi-representational systems are considered. Although MERs demonstrably have the potential to support learning processes, their use in learning situations is also associated with learner demands that can increase learners’ cognitive load and even negatively affect the learning process (de Jong et al., 1998). Indeed, many studies point toward student difficulties with MERs (e.g., Ainsworth, 2006; Nieminen et al., 2010). Consequently, the cognitive load of the learning environment must be considered and managed carefully. In this context, technology support can help reduce cognitive load when learning with MERs, and it can therefore facilitate the learning-promoting effect of MERs (e.g., Horz et al., 2009).
In this line, each representation brings specific affordances for learners and to learn effectively with representations learners need a set of skills, so called representational competencies. Representational competencies describe the ability to handle different types of disciplinary representations skillfully, i.e., to extract information from them, to switch between different types of representations, to generate them as well as to reason with their help (Rau, 2017; Disessa, 2004). These abilities are argued to play a key role in education, since learners are either given or expected to generate different types of external representations (e.g., text, pictures, or formulas). The use of multiple external representations, i.e., the combination of various types of representations for conveying information is called multimedia learning (mostly for text-picture combinations) or learning with multiple external representations.
Despite their high potential, the effects of such multi-representational learning opportunities are not entirely conclusive: different types of representations offer specific advantages but also place high demands on representational competencies. It is assumed that a representational dilemma arises when learners are offered representations that are helpful and conceptually necessary, while at the same time causing difficulties for the learners due to their lack of representational competencies.
Representational competencies have been found to be positively correlated with content knowledge. To explain this relationship, it is assumed firstly that representational competencies serve as a prerequisite for the acquisition of content knowledge and that, secondly, these two develop in mutual dependence. First evidence shows that there may be gender differences in the strength of this relationship, suggesting gender-specific interventions.
Despite these initial intriguing findings coming exclusively from Science, Technology, Engineering, and Math subjects, research on this topic is still in its infancy. Research from other subject groups or cross-disciplinary studies would be needed to replicate previous findings and to explore whether they generalize to other domains. Furthermore, only a few studies consider the individual facets of representational competencies, relevant learners’ prerequisites, and their effect on multi-representational learning. To enable a more refined view of the interplay between learning and representational competencies, research therefore needs to develop differentiated methods for assessing separate components of representational competencies.
More research is also needed to explore whether the choice of representation types included in performance measurements affects performance on the test depending on learners’ representational competencies. In this context, in all the vast research body on multimedia learning, there is not one example where representational competencies have been explicitly related to information processing during learning from text-picture combinations.
In particular, our objectives are:
- Discussing how understanding of MERs relates to students’ conceptual understanding and pre-conceptions.
- Discussing how effective learning with MER relates to characteristics of learners (spatial skills, metacognitive competences, etc.).
- Discussing relevant research questions in physics education research involving MERs (incl. multimedia learning environment and technology-enhanced learning).
- Identifying and discussing implications of MERs for improving learning materials and teaching processes.
- Exploring how MERs can be used to optimize assessments.
- Elaborating both quantitative and qualitative (product- and process-based) analysis methods of MERs in learning materials and problems, discussing MERs in various tasks and potentially sharing developed new analysis procedures (e.g., eye tracking).
- Discussing and highlighting theoretical frameworks and models, such as multimedia design theories, that provide guidelines to implement MERs in learning materials and assessments.
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and instruction, 16(3), 183-198.
De Jong, T., Ainsworth, S., Dobson, M., van der Hulst, A., Levonen, J., Reimann, P., … & Swaak, J. (1998). Acquiring knowledge in science and mathematics: The use of multiple representations in technology based learning environments. In Learning with multiple representations (pp. 9-40). Pergamon/Elsevier.
Disessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and instruction, 22(3), 293-331.
Horz, H., Winter, C., & Fries, S. (2009). Differential benefits of situated instructional prompts. Computers in Human Behavior, 25(4), 818-828.
Nieminen, P., Savinainen, A., & Viiri, J. (2010). Force Concept Inventory-based multiple-choice test for investigating students’ representational consistency. Physical Review Special Topics-Physics Education Research, 6(2), 020109.
Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717-761.
Treagust, D. F., Duit, R., & Fischer, H. E. (Eds.). (2017). Multiple representations in physics education (Vol. 10). Springer International Publishing.
Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (Eds.). (2013). Constructing representations to learn in science. Springer Science & Business Media.
Verschaffel, L., De Corte, E., de Jong, T., & Elen, J. (2010). Use of representations in reasoning and problem solving. London y New York: Routlege.