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The notion of "resources" is often framed in an economic sense: money, time, equipment and the like. The authors reconceptualise this notion, situating resources as embedded in curricular frameworks, teacher practice and student experience. This leads them to define resources as "the potential to participate in socio-cultural action" which is illustrated in this article through a series of reflections on the part of the authors, all within the context of engineering education. First, they demonstrate that curriculum can be productively thought of as a route marker for the development of resources that students need in order to enact their role as professional engineers. Thereafter, they show that lecturers bring tacit resources of trust, care, creativity and credibility to the teaching and learning space, and that these are necessary to overcome the inertia that often resists the transformation of teaching and learning practice. Finally, they reflect on how students' prior learning experiences can be harnessed as a resource for teaching and learning. In so doing, they present resources as tied to sociocultural practices and personal and institutional histories, and encourage others to take up these ideas so as to consider how resources, viewed in the authors' sense, are valued within (engineering) education.

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.