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Abstract BackgroundSketching exists in many disciplines and varies in how it is assessed, making it challenging to define fundamental sketching skills and the characteristics of a high‐quality sketch. For instructors to apply effective strategies for teaching and assessing engineering sketching, a clear summary of the constructs, metrics, and objectives for sketching assessment across engineering education and related disciplines is needed. PurposeThis systematic literature review explores sketching assessment definitions and approaches across engineering education research. Methodology/ApproachWe collected 671 papers from five major engineering and education databases at all skill levels for reported sketching constructs and metrics, cognition, and learning contexts. Based on the selection criteria, we eliminated all but 41 papers, on which we performed content analysis. Findings/ConclusionsEngineering, design, and art emerged as three major disciplines in the papers reviewed. We found that sketching assessment most often employs metrics on accuracy, perspective, line quality, annotations, and aesthetics. Most collected studies examined beginners in undergraduate engineering design sketching or drawing ability tests. Cognitive skills included perceiving the sketch subject, creatively sketching ideas, using metacognition to monitor the sketching process, and using sketching for communication. ImplicationsSketching assessment varies by engineering discipline and relies on many types of feedback and scoring metrics. Cognitive theory can inform instructional activities as a foundation for sketching skills. There is a need for robust evidence of high‐quality assessment practices in sketching instruction. Assessment experts can apply their knowledge toward improving sketching assessment development, implementation, and validation. 

Abstract—This WIP research paper presents validity evidence for a survey instrument designed to assess student learning in makerspaces. We report findings from expert reviews of item content and student interpretations of survey questions. The instrument was developed using a theory-driven approach to define constructs, followed by the development of questions aligned with those constructs. We solicited written feedback from 30 experts in instrument development and/or makerspaces, who rated the alignment of items with our constructs. Based on this input, we revised our items for clarity and consistency. We then conducted 25 cognitive interviews with a diverse group of students who use makerspaces, asking them to explain their understanding of each item and the reasoning behind their responses. Our recruitment ensured diversity in terms of race, gender, ethnicity, and academic background, extending beyond engineering majors. From our initial 45 items, we removed 6, modified 36, and added 1 based on expert feedback. During cognitive interviews, we began with 40 items, deleted one, and revised 23, resulting in 39 items for the pilot survey. Key findings included the value of examples in clarifying broad terms and improved student engagement with a revised rating scale—shifting from a 7-point Likert agreement scale to a self-description format encouraged fuller use of the scale. Our study contributes to the growing body of research on makerspaces by offering insights into how students describe their learning experiences and by providing initial validation evidence for a tool to assess those experiences, ultimately strengthening the credibility of the instrument.