Multiple Representations in Science Education

Gemeinschaftsprojekt mit der Leibniz Universität Hannover und der Curtin University of Technology Perth

Most actual debates about conceptions in science education describe knowledge as a personal construct of an individual. This claim refers to the epistemological position of constructivism. The insight that all meaning is a construct of the individual itself based on prior knowledge creates a start-up-problem: if our conceptions are personal constructions based on prior knowledge, how did we create the foundational knowledge acting as the basis for the development of further knowledge? This epistemological problem can be solved with the help of empirical findings emerging from the fields of linguistics (Lakoff, 1990; Lakoff & Johnson, 1980; Lakoff & Johnson, 1999), philosophy (Johnson, 1987), science education (Niebert, Riemeier & Gropengießer 2013), and neurobiology (Gallese & Lakoff, 2005; Rohrer, 2001, 2005). These findings, summarised as the theory of experientialism, show that abstract concepts—this refers to most concepts in science—are not understood directly but in terms of embodied domains of knowledge; that is, understanding is ultimately grounded in embodied experience. Based on this findings the project-team already conducted some studies analysing scientists and students understanding of science. In these studies we were able to show that for all abstract concepts in science (i.e. force concept, climate change, cell biology, chemical equilibrium etc.) both – students and scientists – are using everyday and early childhood experience to make sense of these scientific phenomena (Niebert & Gropengiesser, 2013; Niebert, Marsch, & Treagust, 2012).

Designing Representations based on experientialism

Discussing the implications of experientialism for mathematics education, Nuñéz, Edwards, and Matos (1999) noted that the insistence on the rigorous, abstract characterisation of concepts omits the reality of their grounding in experiential intuitions. As shown here, this grounding in experiential intuitions applies equally to science education: students’ and scientists’ understanding is not based on abstract concepts but on embodied conceptions. It is common knowledge in science education that learning environments based solely on abstract conceptions often are a problem for students (Niebert, Riemeier, &Gropengießer, 2013). For a scientist, abstract representations might be adequate and understandable because they refer to common scientific experience. The challenge for students is to relate these representations obtained by scientific experience to the scientific phenomena that they are meant to represent. Experientialism views understanding as being based on experience and distinguishes between direct conceptions and imaginative conceptions. This categorisation is used as a guide for developing experience-based learning environments.

The insight that embodied conceptions whether by direct experience or mapped by metaphors and analogies is an indispensible premise for understanding bears consequences for the development of learning environments. Riemeier (2005) and Niebert (2010) have shown that learning environments that reflect these assumption and present external representations whether affording experience or depicting students’ schemata are very fruitful in teaching biology. Both studies were based on teaching experiments where small groups of two or three students worked with prepared learning environments. Therefore we have evidence for how learning environments that provide or reflect experience can help students understand scientific concepts in laboratory-like situations. Besides this little is known about how those learning environments are used in real classroom settings.

Goals

This project is based on results of different studies that focus on the role of embodied understanding in science education. In these studies learning environments have been developed and evaluated in teaching experiments that can be placed in the international framework of research on multiple representations. On the basis of experientialism the author developed a theory-based coding scheme to describe, discern and choose different representations to teach science concepts. This scheme discerns different representations that a) denote conceptions, b) afford experience and c) reflect conceptions or experience.
We presented this categorisation on different conferences (NARST 2012, FDdB 2013, ESERA 2013) and in two research papers (Niebert et al. 2012, Niebert et al. 2013).
The approach of developing representations based on embodied conceptions opened up a cooperation with David Treagust at the Science and Mathematics Education Centre at Curtin University of Technology and Peter Aubusson at the Centre for Research in Learning and Change at the University of Technology Sydney. This first cooperation is intended to be intensified via a joint research project to evaluate the effects of different representations used to communicate science. It is our hypothesis that representations, which either enable experience in the target domain or reflect experience in the source domain are more effective than representations that simply denote conceptions. Furthermore we hypothesize that the conceptions that refer to or enable experience are especially effective when being connected to representations that denote conceptions. Our prior research shows the necessity to operationalize the categories of the representations and to elaborate the theory-based categories with evidence from science classrooms. Therefore the central research question of this application is

What varying characteristics do the three categories of representations show in science classrooms?

To answer this question the categories of representations described in Niebert, Riemeier & Gropengießer (2013) are used to analyse science lessons. The analyses are used to develop a system of operationalized characteristics implemented in a coding scheme as instrument for further research. In further research on these representations we want to prove the effects of the different representations on students understanding. To prepare this research based on a fund of the German Science Foundation we need a further operationalization of the categories.

Categories of Representations

The categories of three different representations for teaching science were developed and described theory guided using analyses of learning environments. For a further analysis of learning environments the categories need to be sharpened, operationalized and supplemented by anchor examples. Clear criteria are needed as well as a coding schema that helps to setup valid coding procedures including the calculation of the intercoder reliability.
The categories can be used to develop and choose learning environments to communicate a topic as well as to analyse science lessons in school. The latter is possible as the representations do not implement completely new learning environments into science education but work as a theory guided instrument to discern and describe learning materials. Therefore it is planned to develop a coding scheme based on the categorisation presented in the following table based on an analysis of 16 science lessons. These lessons will be videotaped for a further analysis. For the analysis two assistants are trained to discern the categories and use them to for an event-based coding (Seidel et al. 2003) of the lessons. A third coder will be an experienced researcher from the project team. The reliability of the coding will be proved via the intercoder-reliability in combination with a communicative validation of the codings. The preliminary categories and their descriptions are presented in the following:

• Representations that enable experience in target domain.

  Science often uses methods such as experiments in which variables are controlled or instruments such as a microscope are used to experience previously imperceptible entities. While the core of our conceptual system is grounded in everyday experience, many scientific concepts are grounded in scientific inquiry. From the perspective of experientialism this is probably the most effective teaching strategy: providing an experience and developing the scientific topic to be taught by reflecting this experience. Experiences, whether of first- or second-hand origin, prepare the basis for the development of conceptions through experiences.

  Examples:

  first hand experience: microscopy of everyday-objects and cells, preparing a histological specimen, experiments on transpiration of leafs

  second hand experience: picture of leaf in a textbook, pictures of a lynx
• Representations that reflect conceptions and experienence

  Examples and source domains to which metaphors or analogies refer need an embodied basis. Re-experiencing and reflecting on the source domains helps students to understand complex and abstract phenomena. To this end, students need to work with representations that throw light on the schema they employed in their endeavour to understand. Awareness of the schemata can be deliberately deployed to understand the scientific conception of the phenomena.
  Examples:
  “Please compare your pre-instructional conceptions with what you learned today.”; “This structure of the leaf is called palisade mesophyll. Can you imagine why?”

  Breaking a bar of chocolate to understand the principles of cell division; “You said that cells have rooms. Please compare your idea of a room with the compartments of cells.”

• Representations that denote conceptions

  Every depiction of a mental model represents a scientific conception—including the cell cycle, the citric acid cycle, the structure of a DNA molecule, the equation of photosynthesis, and the Punnett square. There are many well-known representations of this kind—such as figures, models, symbolic systems, or scientific terms that represent the scientific way of thinking. We found that learning environments based solely on this kind of representation often pose problems to students. For a scientist, these representations might be adequate and understandable, because they refer to common scientific experience. The challenge for students is to relate these representations obtained by scientific experience to the scientific phenomena that they are meant to represent.

  Examples:
  6CO2+12H2O C6H12O6 + 6H2O + 6O2, text about population cycles, table with inheritance rules, schematic figures of the structure of microorganism, diagram of cell cycle