Gendered Professional Role Confidence and Persistence of Artificial Intelligence and Machine Learning Students

Abstract

Kimberly is an enthusiastic Engineering Science student at the University of Toronto specializing in Biomedical Systems Engineering. She is a passionate proponent of women in STEM. She is an awardwinner in engineering competitions across Canada and beyond in areas including super-resolution microscopy, machine learning solutions for health care, and space missions for microbiology research.

Canada the “Interpreter”

“…Persistence refers to a commitment to remain in a profession or field. The term is often used to examine student persistence in a field of study up to [15], [16], [27]- [29] and after graduation [13], [18], [30]. Scholars have studied the persistence of underrepresented groups, such as women [13], [16], [18] or ethnic minority groups in STEM [31].…”

“…The term is often used to examine student persistence in a field of study up to [15], [16], [27]- [29] and after graduation [13], [18], [30]. Scholars have studied the persistence of underrepresented groups, such as women [13], [16], [18] or ethnic minority groups in STEM [31]. Various factors are shown to be associated with persistence, including identity [16], financial aid status [13], [28] [15], confidence [16], [18], and institutional culture [27]; a more in depth discussion of factors important to this study are included in the following sections.…”

“…Persistence is commonly measured as part of a longitudinal study where researchers follow students through their university program to track whether they graduate from the program [18], [21], or which programs they switch into [16]. This is referred to as behavioural persistence as it measures whether students actually do persist [16].…”

“…This is referred to as behavioural persistence as it measures whether students actually do persist [16]. On the other hand, intentional persistence measures whether students intend to remain in the field at some point in the future [16], [18], [30]. In this study, we measure intentional persistence as a first step in understanding persistence in this new and evolving field.…”

“…Yet we see that broad studies of persistence often show mixed results; studies are needed that narrow the scope to specific areas of study. While engineering and computer science have been the focus of studies of persistence and retention [16], [17], the persistence of students in the field of ML/AI has only recently been investigated [18]. This new survey builds on this initial work, investigating additional independent variables that may distinguish ML/AI from existing studies of persistence in STEM, such as the varying emphasis on non-technical skills [19], [20], the ambiguous ethical implications of the work [21]- [23], and the highly competitive and lucrative nature of the field [24]- [26].…”