This chapter, drawing on a social justice perspective, explores how to better enable epistemological access in science. Using the Specialization dimension of Legitimation Code Theory to examine the curriculum of a science foundation course, two distinct codes are recognized: a science-related knowledge code and an academic practices-related knower code. The close relationship between the two codes indicates a key aspect of learning the sciences: if students do not learn to work independently and develop deep understanding as required by the academic practices-related knower code, they seldom can develop the conceptual depth as required by the science-related knowledge code. This finding emphasizes the need to significantly rethink science higher education to take into account methods for teaching both the knowledge of science and how students can be apprenticed into becoming science learners in a higher education context.
Mathematics is a key aspect of science but how mathematics can be successfully integrated in science teaching remains a vexing issue. That students often struggle with mathematics in science lessons, even when they have little problem with those ideas in mathematics lessons, has been a long-running concern for educators. One reason this issue remains unsolved is that existing approaches cannot systematically distinguish ‘mathematics’ knowledge from ‘science’ knowledge. This chapter introduces cutting-edge tools from the Legitimation Code Theory dimension of Autonomy that enable knowledge practices to be distinguished without lapsing into either essentialist definitions that neglect how these bodies of knowledge differ between contexts or relativist claims that they are nothing but endless flux. The concepts are illustrated through detailed analyses of real-world classroom practices that show a key attribute of successful integration of mathematics in science teaching to be autonomy tours that shift between different contents and purposes in particular ways. The ideas outlined here are poised to have a major impact on both research and practice in education, far beyond science teaching.
This chapter describes a methodology for apprenticing students into the knowledge structures of mathematics, by giving them control over the elaborate procedures of maths processes. The method has been developed in partnership with primary and secondary maths teachers, as part of the Reading to Learn (R2L) action research program, informed by the models of pedagogy and language in genre-based literacy pedagogy (Rose & Martin 2012). The method has been shown internationally to achieve rapid improvements in students’ maths outcomes (Lövstedt & Rose 2015). The approach is demonstrated in training videos and resources on the website of the NSW Education Standards Authority (NESA 2018) and is a part of the R2L teacher professional learning program. The method involves close analysis by teachers of the steps they use to model maths processes, and close analysis of the spoken and mathematical modalities they use to guide their classes. This chapter presents the theoretical principles that inform the methodology, with analyzed examples from a secondary classroom.
A key aspect of science is how ideas are related to create explanations. Yet, reflecting their knowledge-blindness, dominant approaches to researching science education neglect these relations. Maton and Doran introduce the method of constellation analysis from Legitimation Code Theory as a way of revealing how ideas are brought together in different ways and the effects these differences have for teaching science. This innovative method is used to analyze explanations of the tides and seasons. In each case the logic of explanations presented in school textbooks is analyzed and compared to how the explanation is taught in a classroom. These analyses show that explanations of seemingly similar kinds of phenomena differ in terms of how ideas are related together and that the logic of these relations impacts on how they are taught in classrooms. Constellation analysis offers a new analytic method with huge potential as a practical tool for researchers, curriculum designers, educators and students.
To organize its highly technical knowledge, science consistently uses a range of different resources for making meaning, including language, mathematics and images. This chapter explores why these resources are used and what effect they have on the scientific knowledge being taught and learned. Focusing on physics, it shows that language, mathematics and image are each crucial to building its highly complex knowledge as each enables particular meanings that are indispensable to the field. Through an analysis using the Legitimation Code Theory concepts of ‘semantic gravity’ and ‘semantic density’ and the SFL model of field (presented in Chapter 5), the chapter articulates the specific affordances of each resource for building physics knowledge. In particular, the chapter shows that each resource is necessary; physics could not develop its knowledge and students cannot express this knowledge without all three. By bringing together conceptualizations of knowledge from LCT and models of meaning from SFL, the chapter also illustrates the power that interdisciplinary analysis using both approaches can bring for understanding science.
The logic of curriculum design for professional education programs is often based on a common sense view of professions as the application of disciplinary knowledge to solve ‘real world’ problems. In this chapter I trouble that model of professional reasoning. Using an expansion of semantic density to include both the complexity of disciplinary knowledge deployed (discursive relations) and the complexity of the contextual detail of the problem itself (ontic relations), I am able to show a far more dialectical relationship between knowledge and object than is suggested by ‘the application of knowledge’. The data is collected from engineering design projects located in a civil engineering curriculum. The projects are intended to mimic ‘real’ professional problems in order to prepare students for professional practice. The analysis shows how the way in which these projects are set up changes the nature of the reasoning required.
Science involves nuanced and highly technical distinctions among empirical phenomena. This chapter explores how these deep and multifaceted taxonomies are developed through language in undergraduate biology by examining both pedagogic texts and students written assessments. The chapter presents a major development in modelling ideational meanings within SFL’s discourse semantics. It first builds a system of ‘entity types’ and ‘dimensionality’ from a ‘trinocular’ perspective. It then illustrates how the framework of entities and dimensions can be applied to text analysis by revealing both the diversity and depth of taxonomies demonstrated in a student research report produced at the final year of undergraduate biology. This analysis shows the appliability of the discourse semantic method for examining scientific taxonomies and makes explicit the multitude of ways in which various field-specific resources (presented in Martin and Doran this volume) are realized in language.
The death knell for face-to-face lectures at university has been rung louder and louder in recent years, as teaching is pushed to move online. However, those advocating for online learning have rarely engaged with the functionality of lectures for building highly specialized knowledge and thus what we risk losing by abandoning them. Utilizing SFL, this chapter explores the potential of face-to-face lectures for structuring knowledge, as exemplified through a live lecture of molecular chemistry. In particular, it analyzes the co-expression of language and body language to reveal the dynamic iterations of meaning across various lecture phases. Through these phases, the various ways in which knowledge is presented and negotiated are explored to uncover how these support the apprenticeship of students into specialized knowledge in their fields. In an age of transition to online learning, this chapter presents an important and often lost argument of the significance of face-to-face lectures to knowledge-building in science education and what could be lost without them.
To understand science, students need to learn a large interconnected set of uncommon-sense meanings. But despite decades of research, the precise nature of these meanings has not been made clear. This chapter focuses on the meanings involved in scientific explanations of complex phenomena such as seasons. It synthesizes three decades of research into scientific discourse to renovate the SFL register dimension ‘field’, to better deal with the technical meanings of science. The chapter focuses on explanations of seasons as they are taught in secondary school science and illustrates the ways in which scientific meanings are construed dynamically as series of unfolding activities, and statically as relations between items. It also considers how the gradable properties of science are presented in multimodal discourse and how all of these features of scientific meaning can be reconstrued and connected together to make an integrated whole. The model clarifies the interdependent meanings at play in scientific understandings of the world and the challenges faced by teachers and students when navigating teaching/learning pathways through these networks of meaning. In doing so, it shows how the meanings made through both language and an ensemble of multimodal resources organize scientific knowledge.
How well do you know your teachers of STEM? Do you know the perceptions they have of STEM learning environments? How might these perceptions inform the school environment, and how teachers are supported to adapt effective STEM teaching practices? Finding out more about the perceptions that teachers of STEM hold about STEM learning environments can help school principals and discipline heads to understand the ways in which STEM teaching is approached in their school. To discover teachers’ perceptions, you can ask them. One way to do so is to ask them to create a drawing of a STEM learning environment. You may be surprised at the results.
Life as we know it would not be possible without plants. Plants supply food to many organisms (including people), produce oxygen, absorb carbon dioxide from the air, provide products for human use, and homes for many other living things. It is not surprising, therefore, that plant growth is a familiar topic in the primary school science curriculum. This paper describes an extension of the topic of plant growth for Year 6 students to include elements from the mathematics, in particular, statistics, and technologies curricula. In doing so, the importance of carefully collecting and analysing data to make decisions illustrates the way in which the practice of statistics supports learning outcomes in science. In the activity described here, students decide on the details for their science inquiry about plant growth to answer the question What makes plants grow best?, collect and analyse data over time using digital technology, and present a report of their findings to their class.
The need for early engagement in science, technology, engineering, and mathematics (STEM), and representation of role models in STEM careers is of national and international importance. The That's RAD! Science project aims to address these needs through a series of engaging picture books for younger children that feature women in STEM as role models. The goal of this research project was to use a survey tool to gain feedback from adults on their impression of the impacts and benefits on children of the That's RAD! Science books, as well as their perceptions of the benefits of STEM engagement and use of identifiable women in STEM as role models. Participants were sent a set of four That's RAD! Science books for children to engage with and the adult participant then completed an online survey. A total of 83 participants were recruited, with a 61% completion rate for the online survey. Of these, 98% identified as parents/carers. Survey data showed the That's RAD! Science books engaged children and the books are also beneficial to informal hands-on learning processes. In addition, survey results revealed that the books are useful in exploring STEM topics and encouraging children to think about career pathways in STEM by using identifiable female role models in STEM careers. More broadly, survey data reinforced previous findings that an understanding of STEM is important for young children; children are highly engaged by informal learning processes; and exposure to identifiable, relatable, female role models in STEM careers is perceived as being valuable to long-term engagement.
