| Abstract |
Contemporary policy documents seeking reforms in science education have called for substantial changes in our thinking about instructional approaches and the design of learning environments. Built upon Giere’s views on model-based science, this paper proposes a contemporary approach to science education which we call Evidence-Explanation approach. This approach places emphasis on the epistemological conversations about data transformations in science. The process of data transformation, which we refer to as data-texts, we claim, unfolds the processes of knowledge construction and reveals the nature of scientific practice. Our claim is that a focus on the conversations surrounding the acquisition of data and the subsequent transformations of data to evidence, evidence to models, models to explanations (i.e. data-texts) can enhance the teaching and learning of and about science. |
| Summary |
Introduction Traditional approaches to the inclusion of inquiry in science classrooms have focused on carrying out investigations to test hypotheses or to clarify science concepts. Such an approach focuses on learners' attention on questions of: what we know in science. Here inquiry is typically part of a lesson with the purpose of teaching specific concepts. Such a "learning science knowledge" approach to inquiry has been called into question for some time now (Duschl, 1989; Hodson, 1989) on the grounds that it: (a) does not lead to meaningful learning, and (b) does not accurately portray the nature of scientific inquiry. The issue seems to be one of shifting the focus of science education to a "science-in-the-making" perspective and reconsidering Schwab's call for making science education an "enquiry into enquiry". In light of Schwab’s call, we claim in this paper that a missing dynamic in science education is the all-important dialectic, what we call conversations about science, between observation and explanation, which focuses on the processes of data transformations. Our claim is that a focus on the conversations surrounding the acquisition of data and the subsequent transformations of data to evidence, evidence to models, models to explanations, a collective we will refer to as data-texts, can enhance the teaching and learning of and about science. Data-Texts We ground data-texts on Giere’s (1987) view of models as representation of the world and theories as families of models. According to Giere (1987), ‘understanding science is primarily a matter of understanding the cognitive processes of scientists involved in doing science’ (p. 322). Data-texts are an element of scientific practice, which refer to the process of data transformation in science from observations to explanations - a journey of both comprehension and articulation, which moves from the whats to the whys and hows of a phenomenon. In specific, data-texts are a collective system of complex transformation processes through which raw data are obtained and then reviewed and selected to become evidence. In turn, the evidence is examined to generate scientific explanations. Our intent is to suggest the linkage between data and explanations is evidence, which results in an account of the processes of data transformations and uses. We describe this process of data transformations as being collective because it does not happen in isolation; instead it is a process of social consolidation, situated within sociocultural contexts and specific learning environments. We describe data texts as complex because it is not a straightforward, clear-cut process; instead it entails errors, missing, conflicting and anomalous data. Data-texts involve the following processes: a) examining observations to become data; b) interpreting data to become evidence; c) using evidence to develop patterns and models; d) employing the patterns and models to propose explanations. We maintain that data-texts resemble a sort of circular motion because there is no end to possible stems of transformation, as explanations undergo change in light of new evidence and/or alternative interpretations of existing evidence. Data-texts then provide explanations, yet they remain dependent to the origin of the phenomenon and are interconnected to existing observations and experiences as they revive upon new observations and interpretations. We argue that data become explanations through a series of epistemic and social conversations that involve interpretations of evidence, argumentation and negotiation of understandings. In the next section we discuss the foundational frameworks upon which these ideas are drawn and which led us to propose a new approach to science education based on the evidence-explanation continuum. The Evidence-Explanation Continuum The evidence-explanation continuum has its roots in perspectives from history and philosophy of science and connects to cognitive and psychological views of learning. Adopting an image of science education which is guided by the development, evaluation and deployment of data texts is grounded in the idea that scientific inquiry and scientific reasoning are both fundamentally decision making activities mediated by epistemological, cultural and technological factors. The appeal to decision making firmly roots our view to the cognitive sciences and human decision making and to philosophies of science that seek to explain science employing the cognitive sciences. A leading voice is that of Ronald Giere (1986; 1988) and our account below draws heavily on his points-of-view about the cognitive model of science. The adoption of cognitive models and decision making within human judgement, or more precisely cognitive structures and cognitive processes, as a mechanism to explain science shifts, Giere argue, the philosophical goal from the justification of scientific knowledge, processes and methods to an understanding of scientific knowledge, processes and methods. The appeal to adopting the EE continuum as a framework for guiding the design science education curriculum, instruction and assessment models is that it seeks to work out the details of the process. The EE continuum recognizes how cognitive structures and processes guide judgements about data texts. It does so by formatting into the instructional sequence select junctures of reasoning, what we call, data texts transformations. The critical transformations or judgements in the EE continuum include: a) Selecting data to become evidence b) Using evidence patterns of evidence and models c) Employing the models and patterns to propose explanations How raw data are selected and analyzed to be evidence, how evidence is selected and analyzed to generate scientific explanations are important ‘transitional’ steps in doing science. Each transition involves data texts and making judgements about ‘what counts’. Conclusion In this paper we argued on the significance of the epistemological conversations about data transformations in science. We referred to these transformations as data-texts and we built upon this concept to introduce a contemporary approach to science education, where the continuum of evidence-explanation is central. We hope that this paper will provide the basis for thinking about the epistemological issues of the construction of scientific knowledge and for fostering discussions about the epistemological conversations in science. A shift of emphasis on the epistemics of science is important for two main reasons: it has implications for the design of learning environments and it has much to contribute in our understanding of the processes of scientific practice. |
