Abstract:Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. . IntroductionIf there is any theme that unites philosophers as regards models it is that of representation. Models are generally presumed to be representations. While scientific models as specifically designed artefacts certainly belong to a class of public objects called representations, something more is implied by the idea of representation in the context of modelling. Namely, the claim that models are representations plays out their relational nature. Models are thought of as being inherently models of something and, more often than not, this something is understood in terms of some real objects, processes, or more generally, some natural "phenomena". Thus we take it to be commonly accepted in the philosophy of science that scientific models represent some real target phenomena -or target systems.This seeming agreement disguises the fact that different philosophers understand representation in vastly different ways, yet at the bottom of this consent seems to be the belief that models give us knowledge in virtue of representing (some selected aspects) of external world sufficiently accurately (Bailer-Jones 2003; da Costa and French 2000;French and Ladyman 1999;Frigg 2002; Morrison and Morgan 1999;Suárez 1999;Giere 2004). The representational conception of knowledge as that of accurate representation which underlies this belief has long roots in Western culture, a huge topic we cannot even hope to cover in this paper. However, we argue that if we are interested in how models give us knowledge in the actual scientific practice the representational approach to models proves too minimal. Although we do not want to dispute the fact that models often are used to represent some real target systems, we claim that the representational approach to models is rather

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BackgroundSocietal challenges that call for a new type of engineer suggest the need for the implementation of interdisciplinary engineering education (IEE). The aim of IEE is to train engineering students to bring together expertise from different disciplines in a single context. This review synthesizes IEE research with a focus on characterizing vision, teaching practices, and support.PurposeWe aim to show how IEE is conceptualized, implemented, and facilitated in higher engineering education at the levels of curricula and courses. This aim leads to two research questions:What aspects of vision, teaching, and support have emerged as topics of interest in empirical studies of IEE?What points of attention regarding vision, teaching, and support can be identified in empirical studies of IEE as supporting or challenging IEE?Scope/MethodNinety‐nine studies published between 2005 and 2016 were included in a qualitative analysis across studies. The procedure included formulation of research questions, searching and screening of studies according to inclusion/exclusion criteria, description of study characteristics, appraisal, and synthesis of results.ConclusionsChallenges exist for identifying clear learning goals and assessments for interdisciplinary education in engineering (vision). Most pedagogy for interdisciplinary learning is designed to promote collaborative teamwork requiring organization and team management. Our review suggests that developing interdisciplinary skills, knowledge, and values needs sound pedagogy and teaming experiences that provide students with authentic ways of engaging in interdisciplinary practice (teaching). Furthermore, there is a limited understanding of what resources hinder the development of engineering programs designed to support interdisciplinarity (support).

Philosophy of science is booming-at least in sheer quantitative terms, such as the numbers of scholars and professional organizations associated with the field. On the surface, one might attribute these trends to the concurrent growth of science itself, along with the large amounts of funding committed to scientific research and the lasting cultural power of scientific paradigms in the late 20th and early 21st centuries. Yet, much work in the philosophy of science continues in nearly complete isolation from real scientific practice. The Society for Philosophy of Science in Practice (SPSP) grew out of a recognition of the need to promote the philosophical study of "science in practice", by which the organizers of the Society meant both scientific practice and the functioning of science in practical realms of life. Despite occasional exceptions such as some recent literature on models, experimentation, and measurement which have engaged in detailed consideration of scientific practices in pursuit of their philosophical points, concern with practice has tended to fall outside the mainstream of Anglophone analytic philosophy of science. SPSP was founded with the aim of changing this situation, through the promotion of

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research (IDR), in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science (called a physics paradigm of science ), which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm (called an engineering paradigm of science ) entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ (such as theories, models, laws, and concepts) as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines (or fields) construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives . This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced (i.e., explaining epistemological aspects of doing research). In interdisciplinary research, metacognit...