science (statistician,computer scientist, industrial engineering, operations researchers, etc.) are in-demand and requirehighly skilled professionals with knowledge of data science, which has resulted in a highlycompetitive labor market. While the median annual salary for data scientists is quite high, about$122,000, according to the BLS, this reflects the higher educational, experience, and skill levelrequirements needed for such positions, as well as geographical differences related to keyemployer locations.Employers have recognized that data science professionals will be a critical resource to theiroperational excellence, as well as for the future of their innovation ecosystems. This need fordata science professionals has naturally driven an
varyconsiderably and we found no evidence of programs sharing the same assessment instruments orprotocols. A few examples are below. They describe evaluation from different viewpoints and we presentthem here to show examples of the diversity of methods employed, and some research outcomes andreflections. • One paper described the use of specific assessment methods including competency rubrics, individual development plans, and ePortfolios for evaluation (Chang, Semma, Fowler, & Arroyave, 2021). The rubrics encompassed professional and technical skills including: 1) interdisciplinary knowledge generation, 2) collaboration, 3) conflict resolution, 4) oral communication, 5) written communication, 6) self-reflection, 7
perspective, we assume the following principles: problematize status quo,look at the use of language as clues to how ways of thinking and behaviour are structured, lookfor existing mechanisms of inequality, and look for creative alternatives for a more just/equitableoutcome.First, in order to describe what mechanisms of exclusion exist and become significant in studentexperiences, we looked for student accounts of their direct experiences (e.g. of barriers to fullparticipation in engineering education). Students also reflected on their observations on thecontrast between exclusion and inclusion. This resulted in the identification of: the location ofrepresentation gap that became influential; socially-mediated mechanisms that actually lead
level contributes to this vision. Despite some gains in recent decades, women faculty inengineering are still underrepresented. Between 2006 and 2016, the proportion of women facultyin engineering grew from 16% to 23% at the assistant level, from 11.9% to 18.3% at theassociate level, and from 3.8% to 10.6% at the full professor level [2], [3]. While the proportionof women faculty at the lower ranks has increased significantly, the limited representation ofwomen at higher faculty ranks limits their potential for reaching leadership roles andcontribution with significant decision-making to influence engineering education [4]. Althoughthe presented gains are of value, and may already reflect the effect of multiple initiativesimplemented to support