In this paper we will present a classification of engineering projects. It is based on more than ten years of experience with bachelor end projects carried out at the Faculty of 3mE (mechanical, maritime and materials engineering) of the Delft, Technical University. Students of this faculty have to collaborate in groups of four carrying out an engineering project during the last six months of their bachelor. These projects, which originate in the research groups of the faculty, are not just applications of standard engineering procedures. They require creatively combining many topics learned during the preceding years of study. The project questions are open and their answers are unknown to the supervisors.
During the first years of this problem-based learning exercise the students were only provided with (1) hypothesis formulating and testing methodology, standard within descriptive knowledge production in the natural sciences. Soon in turned out however that many proposed projects were (2) directly design related, or (3) focusing on normative design knowledge formulation. The extensions (2) and (3) did broaden the methodological attention considerably but (1), (2) and (3) did not cover the methodological needs of all the projects. We had at least to add (4) the outlines of modeling projects, (5) the methodology in optimization operations and finally methodologies used in (6) sheer mathematical or information theory projects, which concentrated on formal proofs or algorithms. These distinctions left us through the years with a six-element classification of engineering projects, which, as we will argue, cover many and perhaps most knowledge-related engineering projects in practice. The crucial point of our classification scheme is the differences between the various goals and the way to achieve them.
In the spring of 2013 we started with a course “Research Design” at our University’s Graduate School. Our classification turned out to cover most of the diverging PhD projects presented although they were frequently combinations of some categories. These combinations can be represented suitably with radar charts the six axes of which identify the categories just described. Such charts prove to provide excellent methodological X-rays of the individual PhD-projects in engineering.
This practice-based paper, which covers more than 700 bachelor and 100 PhD projects, is part of a larger exercise in which we will study the various engineering methodologies for solving fundamental and applied problems. It serves theoretical and practical purposes. As far as we know no engineering methodology handbook exists, which gives methodological advice about the entire gamut of problems engineers encounter when carrying out their projects. The first purpose is therefore to provide such a manual for at least educational purposes; the second one is to study and describe the various ways in which engineering practices and different kinds of engineering knowledge are theoretically interrelated. We hope that the latter will also shed light upon the intricate relations between the practical and the descriptive sciences.
School science has been dominated by what seems to be an ‘essential tension’ between two competing curriculum emphases: one focusing on the products of science in the form of propositional knowledge of particular theories, laws and models, and another focusing on scientific processes that in many cases deteriorated to an emphasis on science process skills. Problems associated with the first type of emphasis is rooted in the manner in which products of science are taught in a disconnected fashion without giving learners a sense of the relations between different forms of scientific knowledge; how scientific knowledge grows; and what criteria, standards and heuristics drive growth of scientific knowledge. As Schwab (1962) pointed out decades ago, students need to understand both the substantive and syntactic structures of science. The substantive structure refers to “a body of concept-commitments about the nature of the subject matter functioning as a guide to inquiry”, while the syntactic structure refers to “the pattern of the discipline’s procedure, its method, how it goes about using its conceptions to attain its goal” (Schwab, 1962, p. 203). Communicating both structures in curriculum and instruction is a desirable goal in science curriculum and instruction. Contemporary calls for curriculum reform (e.g. the Next Generation Science Standards, NGSS Lead States, 2013) resonate with some of Schwab’s notion of structures of science, as they call for reorganizing and integrating science concepts around three dimensions: scientific and engineering practices, cross-cutting concepts and disciplinary core ideas. The question still remains as to how the growth of scientific knowledge can be coordinated in the science curriculum. The purpose of this paper is to investigate a timely topic on how scientific knowledge including its development can be captured in the science curriculum such that students acquire understanding of growth of scientific knowledge. Drawing on the rich scholarship in philosophy of science (e.g Giere, 1999; Mayr, 2004; Press, 2009) we propose a pedagogically relevant growth of knowledge framework involving theories, laws and models (TLM). The framework can serve as a metacognitive tool for designing or enacting a more coherent science curriculum. In particular, TLM provides 1) a visual tool that can have pedagogical utility, 2) supports the cognitive and epistemic goals of current science education reforms, 3) can be customized to different subject areas in science, and 4) acknowledges continuities and discontinuities in growth of scientific knowledge. Such a growth of scientific knowledge framework goes beyond the traditional ‘atomistic’ differentiations in science education between laws and theories, and focuses instead on a whole set of relationships between different forms of scientific knowledge. Such holistic consideration of theories, laws and models is more likely to assist learners in understanding growth of scientific knowledge.
Recent science curriculum reforms continue to advocate the inclusion of the nature of science in science education. For example, the Next Generation Science Standards (NGSS Lead States, 2013) call for reorganizing and integrating science concepts around three dimensions: scientific and engineering practices, cross-cutting concepts and disciplinary core ideas. While this tripartite emphasis invites reconfiguring several meta-assumptions about science into the curricular landscape, it does not offer clear pathways for so doing. Rich scholarship in philosophy of science can provide some useful insight into how characterization of the nature of science can be clarified in science education. Using Wittgenstein’s Family Resemblance Approach (FRA), Irzik and Nola (2014) proposed using broad categories that address a diverse set of features that are common to all the sciences. FRA conceptualizes science in terms of a cognitive-epistemic system and as a social-institutional system. The analytical distinctions are meant to “achieve conceptual clarity, [and] not [serve] as a categorical separation that divides one [dimension] from the other. In practice, the two constantly interact with each other in myriad ways” (Irzik & Nola, 2014, p. 1003). Science as a cognitive-epistemic system encompasses processes of inquiry, aims and values, methods and methodological rules, and scientific knowledge, while science as a social-institutional system encompasses professional activities, scientific ethos, social certification and dissemination of scientific knowledge, and social values. Our work expanded it in a way that offers a pedagogical framework for supporting the development of a more sophisticated and grounded view of the nature of science for teachers and learners.
In the paper, we review literature from philosophy of science (e.g. Giere, 1999; Mayr, 2004; Press, 2009) to illustrate the characterization of each of the FRA categories. Re-conceptualizing the nature of science for science learning and instruction is not about the replacement of some specific statements from NGSS with 11 categories. The approach we propose in applying an expanded version of the FRA is rich and nuanced and has direct implications for structuring science content for learners. The NOS content draws on overarching principles from which objectives can be developed and adapted to different settings and grade levels. These overarching principles invite teachers and learners to be active participants in seizing opportunities for understanding science in a more contextualized and relevant way.
Identifying the components of science as a cognitive-epistemic and social-institutional system is a beginning step in the design of curricula. The pedagogical strategies that accompany the realization of the FRA framework need to also be considered. There are implications for teacher education as well, in terms of familiarizing science teachers in the content of topics that they may have taught in a decontextualized fashion. There is thus the task for teacher educators in extending the framework for professional development purposes to enable teachers to incorporate FRA components in their science lessons.
Human-centered computing has expressed a sense of marginalization with respect to the scenes where software is made, and a related unease about the nature of its contributions. It has been suggested that the practice of using ethnography toward the formulation of requirements and implications for design is limiting. The presentation outlines an alternative epistemic strategy based on a distributed cognition account of the making of a software platform for teaching and learning within an open source community of institutions of higher education. The suggested strategy parallels practice-based, constructivist accounts of science in an emphasis of the mediating role of conceptual models in the scientific understanding of the world. My central claim is that insofar as knowledge about the contexts of use is taken up in the generative modelling processes of designing, the empirical strategy of human-centered computing should be derived from the understanding of the conceptual processes of design.
My analysis draws on Hutchins’ framework of distributed cognition, which views conceptual change as distributed over time, among people, and between humans and artifacts, as well as its application in Nersessian’s account of scientific research as distributed conceptual modelling. Conceptual change in the sciences has been described in terms of universal human cognitive capacities as a model-based reasoning process. According to the mental modelling hypothesis of cognition, humans create simplified structural representations of phenomena, which can be mentally manipulated for the purposes of simulating possible or future situations. Familiar representations can become generative of new models, resulting in conceptual change. Distributed cognition brings to this analysis the notion that cognition is cultural, i.e. the models used in reasoning are shared and passed on among the participants, and the material environment participates centrally in this process.
The case study describes the process of distributed conceptual modelling from which a new software platform has emerged. Central to this process was the emergence of a socially and materially distributed design space from a series of prototyping projects, which configured participants and software prototypes around the loose and open-ended agenda for building a new platform. The open-ended design space prompted a temporally extended process of sense-making with conceptual models and prototypes. Participants were formulating, sharing and discussing thought experiments for a coherent model of user experience, and visited their knowledge about the contexts of use to collectively fuel and test the model building process.
While human-centered computing thinks of its empirical contribution as preceding design both in a temporal and logical sense, the case study suggests a reverse relationship. Participants were pulling in knowledge about the educational context as needed to support their sense-making for the purpose of design, discussing experiences from personal memory and tapping into community archives of similar discussions in the past. The conceptual organization of the knowledge was also in line with the model-building efforts. This implies the viability of an empirical strategy, which embraces conceptual models and the mediation of cultural experiences, and instead of producing accurate contextual descriptions for individual design projects, seeks to make available a rich pool of cultural models for broader cultural domains of experience.