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As high school programs are increasingly incorporating engineering content into their curricula, a question is raised as to the impacts of those programs on student attitudes towards engineering, in particular engineering design. From a collegiate perspective, there is a related question as to how first-year engineering programs at the college level should adapt to a greater percentage of incoming students with prior conceptions about engineering design and how to efficaciously uncover what those conceptions may be. Further, there is a broader question within engineering design as to how various design experiences, especially introductory experiences, may influence student attitudes towards the subject and towards engineering more broadly. Student attitudes is a broad and well-studied area and a wide array of instruments have been shown to be valid and reliable assessments of various aspects of student motivation, self-efficacy, and interests. In terms of career interests, the STEM Career Interest Survey (STEM-CIS) has been widely used in grade school settings to gauge student intentions to pursue STEM careers, with a subscale focused on engineering. In self-efficacy and motivation, the Value-Expectancy STEM Assessment Scale (VESAS) is a STEM-focused adaptation of the broader Values, Interest, and Expectations Scale (VIES), which in turns builds upon Eccles’ Value-Expectancy model of self-efficacy. When it comes to engineering design, there have been a few attempts to develop more focused instruments, such as Carberry’s Design Self-Efficacy Instrument. For the purposes of this work, evaluating novice and beginning designer attitudes about engineering design, the available instruments were not found to assess the desired attributes. Design-focused instruments such as Carberry’s were too narrowly focused on the stages of the design process, many of which required a certain a priori knowledge to effectively evaluate. Broader instruments such as the VESAS were too focused on working and studying engineering, rather than doing or identifying with engineering. A new instrument, the Engineering Design Value-Expectancy Scale (EDVES) was developed to meet this need. In its current form the EDVES includes 38 items across several subscales covering expectancy of success in, perceived value of, and identification with engineering and design. This work presents the EDVES and discusses the development process of the instrument. It presents validity evidence following the Cook validation evidence model, including scoring, generalization, and extrapolation validity evidence. This validation study was conducted using pre- and post-course deployment with 192 first-year engineering students enrolled in a foundational engineering design course. 

As institutions have struggled to chart a path forward through the current pandemic environment, a greater emphasis has been placed on online and hybrid delivery modes. In first-year programs in particular, instructors are scrambling to identify how best to deliver foundational concepts of engineering design in a remote or socially-distanced in-person environment and still retain the high-interactivity and community building aspects that have become so central to their programs. To this end, two asynchronous, interactive modules have been developed introducing the foundational design concepts of stakeholders, need statements, information gathering, and design specifications. The modules are developed in such a way that student responses to each interaction, such as identifying stakeholders or matching need statements, is captured for later analysis. The modules were deployed with first-semester engineering students enrolled in a Foundations of Design course. In this work the modules are introduced and student responses analyzed to answer the question: What are typical standards of performance on these modules for first-year engineering students? Basic descriptive statistics and trends are presented to define these standards. This includes quantitative measures, such as a how many stakeholders are identified when prompted, as well as more subjective measures, such as how well did the student identify the need in a given problem, and attitudinal measures, such as how confident they are in their answers.