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In engineering education in the United States (as elsewhere), it is widely recognized that the percentage of women and minorities who acquire engineering degrees is significantly lower than their representation in the general population. Many studies have investigated the cause of this lack of representation in engineering and other STEM (science, technology, engineering, and mathematics) degree programs. It is widely recognized that the percentage of women and minorities who acquire engineering degrees is significantly lower than their representation in the general population. Adolescents' occupational identity development depends in large part on their internalized mental models of what a given type of professional “looks like,” their subjective sense of their own capacity to be successful at certain tasks and with certain types of knowledge, and the degree to which they feel as if they belong to a community of practice. This paper considers how the concept of “hidden curriculum” can be applied to how underrepresented students experience engineering education uniquely. The concept of the “hidden curriculum” is used to describe the set of structured learning experiences or conditions that occur beyond the design intent of the learning journey established by the explicit curriculum. The hidden curriculum is typically unintentional, unplanned, and less “controllable” than the explicit curriculum. Despite the difficulty in assessing hidden learning expectations, hidden curriculum consistently places expectations on students beyond the explicit curriculum. It is critical to understand not just what variables prevent underrepresented students from persisting, but also what factors encourage their persistence, as such persistence is critical to ensuring a more diverse engineering workforce. This work focuses on how minoritized groups specifically develop professional identity through the hidden curriculum. We consider their perception of belonging in engineering, their experiences of exclusion in various forms, and the mechanisms by which exclusion transpires. By better understanding the cultural dimensions of exclusion, we hope to advance efforts toward inclusion. 

Crowdsourcing platforms are powerful tools for academic researchers. Proponents claim that crowdsourcing helps researchers quickly and affordably recruit enough human subjects with diverse backgrounds to generate significant statistical power, while critics raise concerns about unreliable data quality, labor exploitation, and unequal power dynamics between researchers and workers. We examine these concerns along three dimensions: methods, fairness, and politics. We find that researchers offer vastly different compensation rates for crowdsourced tasks, and address potential concerns about data validity by using platform-specific tools and user verification methods. Additionally, workers depend upon crowdsourcing platforms for a significant portion of their income, are motivated more by fear of losing access to work than by specific compensation rates, and are frustrated by a lack of transparency and occasional unfair treatment from job requesters. Finally, we discuss critical computing scholars’ proposals to address crowdsourcing’s problems, challenges with implementing these resolutions, and potential avenues for future research.