More young women will likely go into engineering if it is promoted as a profession for well-rounded people with a desire to serve society, according to a new study at the University of Waterloo.
The findings suggest that efforts to close a gender gap in the field should stress key reasons for women to pursue engineering along with the current approach of instilling confidence in their technical and academic abilities to succeed.
“The message to women isn’t just that they can do it,” said Lukasz Golab, an engineering professor who led the research. “The message is also that women should want to do it because engineering is an excellent fit for their values, priorities and approaches.”
The study used artificial intelligence (AI) software to analyze more than 30,000 applications to undergraduate engineering programs at Waterloo between 2013 and 2016.
Sophisticated text mining techniques revealed that female applicants highlighted a broad range of strengths and experiences, such as volunteer roles and artistic pursuits, while their male counterparts stressed their technical qualifications.
Young women were also much more likely than young men to mention contributing to society and making a difference in the world around them as their reasons for seeking an engineering education.
“The implications are important,” said Golab, who collaborated on the study with four non-male graduate students at Waterloo. “Everybody is talking about improving gender equity in STEM (science, technology, engineering, and technology). Now we have data-driven insight to help do that.”
According to Golab, the findings indicate that engineering needs a new image - one that highlights how engineering can help people, and that “you don’t have to be a hyper-technical person” to do well in the profession.
The study included all applications, both successful and unsuccessful, to engineering programs. Names and personal information, other than gender, were removed from the data.
The researchers used text-mining techniques to analyze the repetition of words, groups of related words, and other features to identify applicants' main motivations.
Their findings will be presented later this month at the Educational Data Mining conference in Buffalo.
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